From 5939cb8146d605c5d2043fa1f2cfc37c2ea00926 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:11:43 +0200 Subject: [PATCH 01/32] Fix adjusted MSE for the extra flexible model --- PM1/r-ols-and-lasso-for-wage-prediction.irnb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/PM1/r-ols-and-lasso-for-wage-prediction.irnb b/PM1/r-ols-and-lasso-for-wage-prediction.irnb index 3f45551c..b02ac5ac 100644 --- a/PM1/r-ols-and-lasso-for-wage-prediction.irnb +++ b/PM1/r-ols-and-lasso-for-wage-prediction.irnb @@ -734,11 +734,12 @@ "r2_adjextra <- sumextra$adj.r.squared\n", "cat(\"adjusted R-squared for the extra flexible model (in-sample): \", r2_adjextra, \"\\n\")\n", "\n", + "p_use <- sumextra$df[1]\n", "# MSE and adjusted MSE\n", "mse_extra <- mean(sumextra$res^2)\n", "cat(\"MSE for the extra flexible model (in-sample): \", mse_extra, \"\\n\")\n", - "mse_adjextra <- (ntrain / (ntrain - p)) * mse_extra\n", - "cat(\"adjusted MSE for the basic model (in-sample): \", mse_adj1, \"\\n\")" + "mse_adjextra <- (ntrain / (ntrain - p_use)) * mse_extra\n", + "cat(\"adjusted MSE for the extra flexible model (in-sample): \", mse_adjextra, \"\\n\")" ] }, { From f4b25e069f451ee04efde62e1822d4fc2792a25a Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:12:09 +0200 Subject: [PATCH 02/32] Fix no-selection check in double lasso simulation --- PM2/python_orthogonal_orig.ipynb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/PM2/python_orthogonal_orig.ipynb b/PM2/python_orthogonal_orig.ipynb index 91215793..f8c44056 100644 --- a/PM2/python_orthogonal_orig.ipynb +++ b/PM2/python_orthogonal_orig.ipynb @@ -45,6 +45,8 @@ }, "outputs": [], "source": [ + "import sys\n", + "sys.path.insert(0, '/content/hdmpy')\n", "import hdmpy\n", "import seaborn as sns\n", "import numpy as np\n", @@ -88,11 +90,12 @@ "\n", " # Single selection method using rlasso\n", " r_lasso_estimation = hdmpy.rlasso(np.concatenate((D, X), axis=1), Y, post=True)\n", + "\n", " coef_array = r_lasso_estimation.est['coefficients'].iloc[2:, :].to_numpy()\n", " SX_IDs = np.where(coef_array != 0)[0]\n", "\n", " # Check if any X coefficients are selected\n", - " if sum(SX_IDs) == 0:\n", + " if SX_IDs.size == 0:\n", " # If no X coefficients are selected, regress Y on D only\n", " Naive = sm.OLS(Y, sm.add_constant(D)).fit().params[1]\n", " else:\n", From 6ea1a3b1aa834a2c2fd2f6c1fb1e2aa5c1890d8c Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:15:11 +0200 Subject: [PATCH 03/32] Use HC1 robust SEs and fix interaction term selection --- CM1/python-rct-penn-precision-adj.ipynb | 69 ++++++++++++++++--------- 1 file changed, 44 insertions(+), 25 deletions(-) diff --git a/CM1/python-rct-penn-precision-adj.ipynb b/CM1/python-rct-penn-precision-adj.ipynb index b90bc08c..3104e353 100644 --- a/CM1/python-rct-penn-precision-adj.ipynb +++ b/CM1/python-rct-penn-precision-adj.ipynb @@ -376,9 +376,9 @@ " data)\n", "# Omitted dummies: q1, nondurable, muld\n", "\n", - "cl_results = cl.fit(cov_type=\"HC0\")\n", + "cl_results = cl.fit(cov_type=\"HC1\")\n", "cl_est = cl_results.params['T4']\n", - "cl_se = cl_results.HC0_se['T4']\n", + "cl_se = cl_results.HC1_se['T4']\n", "print(f\"ATE: {cl_est:.4f}, (std={cl_se:.4f})\")" ] }, @@ -390,9 +390,9 @@ }, "outputs": [], "source": [ - "cra_results = cra.fit(cov_type=\"HC0\")\n", + "cra_results = cra.fit(cov_type=\"HC1\")\n", "cra_est = cra_results.params['T4']\n", - "cra_se = cra_results.HC0_se['T4']\n", + "cra_se = cra_results.HC1_se['T4']\n", "print(f\"ATE: {cra_est:.4f}, (std={cra_se:.4f})\")" ] }, @@ -441,17 +441,30 @@ "X['T4'] = data['T4']\n", "X = patsy.dmatrix(ira_formula, X, return_type='dataframe')\n", "ira = sm.OLS(np.log(data[[\"inuidur1\"]]), X)\n", - "ira_results = ira.fit(cov_type=\"HC0\")\n", + "ira_results = ira.fit(cov_type=\"HC1\")\n", "ira_est = ira_results.params['T4']\n", "\n", "# because we de-meaned the covariates and interacted them with the treamtent\n", "# we need to account for the error in the means of X, which has a first-order\n", "# effect on the ATE estimate\n", - "interaction_cols = list(map(lambda x: x.startswith('T4:'), list(X.columns)))\n", - "cate = X.iloc[:, interaction_cols] @ ira_results.params[interaction_cols]\n", - "ira_se = np.sqrt(ira_results.HC0_se['T4']**2 + np.var(cate) / X.shape[0])\n", + "interaction_cols = [\n", + " col for col in X.columns if col.startswith(\"T4:\")\n", + "]\n", + "covariate_cols = [\n", + " col[len(\"T4:\"):] for col in interaction_cols\n", + "]\n", "\n", - "print(f\"ATE: {ira_est:.4f}, (std={ira_se:.4f})\")" + "cate = (\n", + " X.loc[:, covariate_cols].to_numpy()\n", + " @ ira_results.params.loc[interaction_cols].to_numpy()\n", + ")\n", + "\n", + "ira_se = np.sqrt(\n", + " ira_results.bse[\"T4\"]**2\n", + " + np.var(cate, ddof=1) / len(X)\n", + ")\n", + "\n", + "print(f\"ATE: {ira_est:.4f}, (std={ira_se:.4f})\")\n" ] }, { @@ -543,7 +556,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "0YGFcOC91xZM" + }, "outputs": [], "source": [ "y = np.log(data[\"inuidur1\"])\n", @@ -555,9 +570,9 @@ "Dres = D - np.mean(D)\n", "yres = y - lasso_model().fit(X, y).predict(X)\n", "\n", - "cra_lasso_results = sm.OLS(yres, Dres).fit(cov_type='HC0')\n", + "cra_lasso_results = sm.OLS(yres, Dres).fit(cov_type='HC1')\n", "cra_lasso_est = cra_lasso_results.params['T4']\n", - "cra_lasso_se = cra_lasso_results.HC0_se['T4']\n", + "cra_lasso_se = cra_lasso_results.HC1_se['T4']\n", "print(f\"ATE: {cra_lasso_est:.4f}, (std={cra_lasso_se:.4f})\")" ] }, @@ -600,15 +615,28 @@ "yres = y - modely.predict(X)\n", "\n", "\n", - "ira_lasso_results = sm.OLS(yres, Dres).fit(cov_type='HC0')\n", + "ira_lasso_results = sm.OLS(yres, Dres).fit(cov_type='HC1')\n", "ira_lasso_est = ira_lasso_results.params['T4']\n", "\n", "# because we de-meaned the covariates and interacted them with the treamtent\n", "# we need to account for the error in the means of X, which has a first-order\n", "# effect on the ATE estimate\n", - "interaction_cols = list(map(lambda x: x.startswith('T4:'), list(X.columns)))\n", - "cate = X.iloc[:, interaction_cols] @ modely.coef_[interaction_cols]\n", - "ira_lasso_se = np.sqrt(ira_lasso_results.HC0_se['T4']**2 + np.var(cate) / X.shape[0])" + "interaction_mask = np.array([\n", + " col.startswith(\"T4:\") for col in X.columns\n", + "])\n", + "covariate_cols = [\n", + " col[len(\"T4:\"):] for col in X.columns[interaction_mask]\n", + "]\n", + "\n", + "cate = (\n", + " X.loc[:, covariate_cols].to_numpy()\n", + " @ np.asarray(modely.coef_)[interaction_mask]\n", + ")\n", + "\n", + "ira_lasso_se = np.sqrt(\n", + " ira_lasso_results.bse[\"T4\"]**2\n", + " + np.var(cate, ddof=1) / len(X)\n", + ")" ] }, { @@ -678,15 +706,6 @@ "\n", "\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "odb5eio_AUfI" - }, - "outputs": [], - "source": [] } ], "metadata": { From 05c2bc3fd79de756bfc19898df89349ec1592ad2 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:15:42 +0200 Subject: [PATCH 04/32] Update to current pgmpy API --- CM2/python-colliderbias-hollywood.ipynb | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/CM2/python-colliderbias-hollywood.ipynb b/CM2/python-colliderbias-hollywood.ipynb index 28d23240..0daa3703 100644 --- a/CM2/python-colliderbias-hollywood.ipynb +++ b/CM2/python-colliderbias-hollywood.ipynb @@ -50,9 +50,9 @@ "import numpy as np\n", "import statsmodels.formula.api as smf\n", "import networkx as nx\n", - "from pgmpy.base.DAG import DAG\n", - "from pgmpy.models.BayesianModel import BayesianNetwork\n", - "from pgmpy.inference.CausalInference import CausalInference\n", + "from pgmpy.base import DAG\n", + "from pgmpy.models import DiscreteBayesianNetwork as BayesianNetwork\n", + "from pgmpy.inference import CausalInference\n", "import pylab as plt" ] }, @@ -131,9 +131,14 @@ }, "outputs": [], "source": [ - "inference = CausalInference(BayesianNetwork(g))\n", - "inference.get_all_backdoor_adjustment_sets('T', 'B')\n", - "# empty set -- we should not condition on the additional variable C." + "from pgmpy.base import DAG\n", + "from pgmpy.identification import Adjustment\n", + "\n", + "model = DAG(g, roles={\"exposures\": \"T\", \"outcomes\": \"B\"})\n", + "adjusted_graphs, success = Adjustment(variant=\"all\").identify(model)\n", + "\n", + "[graph.get_role(\"adjustment\") for graph in adjusted_graphs]\n", + "# Should return [[]] -- the empty adjustment set; no conditioning is needed." ] } ], From 1ef2857773456572579f6a87e4e76a5d8b557527 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:16:01 +0200 Subject: [PATCH 05/32] Update to current pgmpy API --- CM3/python-pgmpy.ipynb | 15 +++------------ 1 file changed, 3 insertions(+), 12 deletions(-) diff --git a/CM3/python-pgmpy.ipynb b/CM3/python-pgmpy.ipynb index 834f730a..045643a7 100644 --- a/CM3/python-pgmpy.ipynb +++ b/CM3/python-pgmpy.ipynb @@ -183,10 +183,10 @@ }, "outputs": [], "source": [ - "from pgmpy.models.BayesianModel import BayesianNetwork\n", - "from pgmpy.inference.CausalInference import CausalInference\n", + "from pgmpy.models import DiscreteBayesianNetwork\n", + "from pgmpy.inference import CausalInference\n", "\n", - "inference = CausalInference(BayesianNetwork(G))" + "inference = CausalInference(DiscreteBayesianNetwork(G))" ] }, { @@ -500,15 +500,6 @@ "source": [ "print(estimate)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NgGRQ5cas94t" - }, - "outputs": [], - "source": [] } ], "metadata": { From 84c15a51ee61155329423e9cff96ef0ce361c144 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:16:42 +0200 Subject: [PATCH 06/32] Correct CI method descriptions, add Cornfield exact interval --- CM1/python-rct-vaccines.ipynb | 114 +++++++++++++++++++++++----------- 1 file changed, 79 insertions(+), 35 deletions(-) diff --git a/CM1/python-rct-vaccines.ipynb b/CM1/python-rct-vaccines.ipynb index 9edce34c..edd4db2a 100644 --- a/CM1/python-rct-vaccines.ipynb +++ b/CM1/python-rct-vaccines.ipynb @@ -14,7 +14,9 @@ "tags": [] }, "source": [ - "# Vaccine RCT Examples\n" + "# Vaccine RCT Examples\n", + "\n", + "We use vaccine trial counts to illustrate estimation of risk differences and vaccine efficacy, then compare several confidence interval methods. Reported risks and efficacy estimates are proportions unless explicitly expressed as percentages." ] }, { @@ -45,29 +47,19 @@ "tags": [] }, "source": [ - " \n", "# Polio RCT\n", "\n", - "One of the earliest randomized experiments were the Polio vaccination trials conducted by the Public Health Service in 1954. The question was whether Salk vaccine prevented polio. Children in the study were randomly assigned either a treatment (polio vaccine shot) or a placebo (saline solution shot), without knowing which one they received. The doctors in the study, making the diagnosis, did not know whether a child received a vaccine or not. In other words, the trial was a double-blind, randomized control trial. The trial had to be large, because the rate at which Polio occured in the population was 50 per 100,000. The treatment group saw 33 polio cases per 200,745; the control group saw 115 cases per 201,229. The estimated average treatment effect is about\n", - "$$\n", - "-40\n", - "$$\n", - "with 95% confidence band (based on approximate normality of the two sample means and their differences)\n", - "$$[-52, -28].$$\n", - "The confidence suggests that the Polio vaccine **caused** the reduction in the risk of polio.\n", + "In the randomized portion of the 1954 Salk polio vaccine trial, children received either the vaccine or a placebo. Participants and the doctors assessing outcomes did not know the assignments. The vaccine group had 33 polio cases among 200,745 children; the placebo group had 115 among 201,229.\n", "\n", - "The interesting thing here is that we don't need the underlying individual data to evaluate the effectivess of the vaccine. This is because the outcomes are Bernoulli random variabales, and we have enough information to compute the estimate of ATE as well as the confidence intervals.\n", + "We estimate the average treatment effect as the difference in case proportions. The estimate is about $-40.7$ cases per 100,000 children, with an approximate 95% confidence interval of $[-52.6,-28.9]$. Under the RCT assumptions, the difference estimates the causal effect of vaccination on the risk of polio.\n", "\n", - "\n", - "We also compute Vaccine Efficacy metric, which (I googled [CDC](https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section6.html)) refers to the following measure:\n", + "Because the outcomes are Bernoulli, the group totals and case counts suffice to calculate the difference in means and its estimated standard error. We also estimate vaccine efficacy,\n", "$$\n", - "\\operatorname{VE} = \\frac{\\text{Risk for Unvaccianted - Risk for Vaccinated}}{\\text{Risk for Unvaccianted}}.\n", + "\\mathsf{VE}=1-\\frac{\\text{Risk for Vaccinated}}{\\text{Risk for Unvaccinated}},\n", "$$\n", - "It describes the relative reduction in risk caused by vaccination.\n", - "\n", + "which measures the relative reduction in risk. The point estimate is approximately 71.2%.\n", "\n", - "It is staighborward to get VE estimate by just plugging-in the numbers, but how do we get the approximate variance estimate. I am too lazy to do calculations for the delta methods, so I will just use a simulation (a form of approximate bootstrap) to obtain the confidence intervals.\n", - "\n" + "For the risk difference, we use a normal confidence interval. For vaccine efficacy, we simulate independent Gaussian draws for the two estimated risks using their estimated variances, calculate efficacy for each draw, and take the 2.5th and 97.5th percentiles. This is an approximate simulation-based interval, not an exact interval." ] }, { @@ -170,19 +162,15 @@ "tags": [] }, "source": [ - "# Pfizer/BNTX Covid-19 RCT\n", + "# Pfizer/BioNTech COVID-19 RCT\n", "\n", - "Here is a link to the FDA [briefing](https://www.fda.gov/media/144245/download) and an interesting [discussion](\n", - "https://garycornell.com/2020/12/09/statistics-in-the-pfizer-data-how-good-is-the-vaccine/?fbclid=IwAR282lS0Vl3tWmicQDDhIJAQCMO8NIsCXyWbUWwTtPuKcnuJ2v0VWXRDQac), as well as data.\n", + "The [FDA briefing](https://www.fda.gov/media/144245/download) reports results from a randomized trial comparing a two-dose vaccine regimen with placebo. Participants and the observers assessing outcomes were blinded to assignment. The table below reports vaccine efficacy against symptomatic, confirmed COVID-19, overall and by age group.\n", "\n", - "Pfizer/BNTX is the first vaccine approved for emergency use to reduce the risk of Covid-19. Volunteers were randomly assigned to receive either a treatment (2-dose vaccination) or a placebo, without knowing which they received. The doctors making the diagnoses did not know now whether a given volunteer received a vaccination or not. The results of the study are given in the following table:\n", + "![Pfizer/BioNTech trial efficacy results](https://lh6.googleusercontent.com/oiO6gYom1UZyrOhgpFx2iq8ike979u3805JHiVygP-Efh1Yaz2ttyPcgWKlT1AqHDM4v46th3EPIkOvRLyXA0fNUloPL-mL9eOFmSAzfbNOHyCZSQ0DyzMhcFUtQuZ520R5Qd2lj)\n", "\n", - "![pfizer/biontech result table](https://lh6.googleusercontent.com/oiO6gYom1UZyrOhgpFx2iq8ike979u3805JHiVygP-Efh1Yaz2ttyPcgWKlT1AqHDM4v46th3EPIkOvRLyXA0fNUloPL-mL9eOFmSAzfbNOHyCZSQ0DyzMhcFUtQuZ520R5Qd2lj)\n", + "For a simple illustration of the two-means approach, we treat the reported case counts and group totals as binomial data, abstracting from differences in follow-up. The calculations illustrate inference methods rather than reproduce the trial analysis.\n", "\n", - "Here we see both the overall effects and the effects by age group. The confidence intervals for the overall ATE are tight and suggest high effectivness of the vaccine. The confidence intervals for the age groups 65-74 and 75+ are much wider. We could group the 65-74 and >75 groups to evaluate the effectiveness of the vaccine and also narrow down the width of the confidence band.\n", - "\n", - "In this case, the reported results are for vaccine effectiveness. We use the same approach as above.\n", - "\n" + "We first apply the normal and Gaussian simulation approaches used in the polio example. We then pool the 65–74 and 75-and-older groups to illustrate how interval estimates differ when there are few observed cases." ] }, { @@ -199,7 +187,7 @@ "tags": [] }, "source": [ - "In the code cell below we calculate the overall effectiveness of the vaccine and construct confidence intervals for it." + "The following cells estimate the overall risk difference and vaccine efficacy, using a normal interval for the risk difference and Gaussian simulation for efficacy." ] }, { @@ -288,7 +276,9 @@ "tags": [] }, "source": [ - "In the code cell below, we calculate the effectiveness of the vaccine for the two groups that are 65 or older" + "## Participants Aged 65 and Older\n", + "\n", + "Pooling the two older age groups gives 1 case among 4,044 vaccinated participants and 19 among 4,067 placebo participants. Estimated efficacy is approximately 94.7%. We repeat the Gaussian simulation to construct two-sided intervals and one-sided lower confidence bounds. With so few cases, the approximation can produce negative simulated risks and efficacy values above 100%." ] }, { @@ -344,7 +334,9 @@ "id": "UQw0zHWGegrK" }, "source": [ - "Let's try the Binomial parametric bootstrap using the fact that outcomes are Bernoulli" + "## Binomial Parametric Bootstrap\n", + "\n", + "We next simulate case counts from binomial distributions with the observed group sizes and estimated risks. The cell uses the ratio of simulated counts as an approximation to the risk ratio because the group sizes are nearly equal. The resulting percentile intervals remain approximate; simulating binomial counts does not make a bootstrap interval exact." ] }, { @@ -400,11 +392,9 @@ "id": "jPZWoSzsegrM" }, "source": [ - "# More Accurate Confidence Intervals\n", + "# Log-Risk-Ratio Confidence Intervals\n", "\n", - "The table from the FDA uses exact inference by inverting tests based on the exact binomial nature of the outcome variable. This method is known as the Cornfield Procedure to find the exact confidence interval on the estimate of vaccine efficacy.\n", - "\n", - "Another typical more accurate approximation is to approximate the log of the risks by a normal distribution, construct confidence intervals for the quantity $\\log(RV) - \\log(RU)$ and then invert. This is for instance method C in [this paper](https://www.jstor.org/stable/pdf/2530610.pdf) and is implemented in the statsmodels package (and also in the `scipy.stats.contengency_table` package)." + "The next cells use `statsmodels.stats.Table2x2.riskratio_confint` to construct a normal interval for the log risk ratio and exponentiate the endpoints. Transforming the risk-ratio interval $[L,U]$ to $[1-U,1-L]$ gives an interval for vaccine efficacy. This is an approximate method, distinct from the exact conditional odds-ratio interval introduced below." ] }, { @@ -438,7 +428,7 @@ "id": "d9rPuODEegrP" }, "source": [ - "We see that this matches much more accurately the table from the FDA." + "The overall log-risk-ratio calculation gives an efficacy interval of approximately **[89.5%, 97.3%]**. The result is close to the interval in the FDA table, but uses a different method." ] }, { @@ -447,7 +437,7 @@ "id": "8ntswBDwrfTt" }, "source": [ - "Next we repeat this procedure to find a more accurate confidence interval on vaccine effectiveness for the two groups that are 65 or older. Here we see a big discrepancy between various asymptotic approaches and the exact finite-sample inference. This occurs because the binomial counts are too low for central limit theorems to work successfully." + "We repeat the log-risk-ratio calculation for participants aged 65 and older. The interval is approximately **[60.5%, 99.3%]**. With only one vaccinated case and 19 placebo cases, different interval methods can give substantially different lower endpoints." ] }, { @@ -472,6 +462,60 @@ "lb, ub = tb.riskratio_confint(alpha=.05)\n", "print(f\"Approximate 95% confidence interval of VE: [{1 - ub:.4f}, {1 - lb:.4f}]\")" ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cornfield-method-description" + }, + "source": [ + "# Cornfield Exact Conditional Interval\n", + "\n", + "We now compute the Cornfield exact conditional confidence interval for the odds ratio; see [Cornfield (1956), *A Statistical Problem Arising from Retrospective Studies*](https://digicoll.lib.berkeley.edu/record/112867).\n", + "\n", + "For these rare outcomes, the odds ratio approximates the risk ratio. If $[L,U]$ is the odds-ratio interval, we therefore use $[1-U,1-L]$ as an approximate interval for vaccine efficacy. The conditional interval is exact for the odds ratio; its interpretation as an efficacy interval uses the rare-outcome approximation.\n", + "\n", + "The following cell calculates intervals for the full sample and for participants aged 65 and older." + ] + }, + { + "cell_type": "code", + "source": [ + "from scipy.stats.contingency import odds_ratio\n", + "\n", + "def cornfield_ve_ci(cases_v, n_v, cases_u, n_u):\n", + " table = [\n", + " [cases_v, n_v - cases_v],\n", + " [cases_u, n_u - cases_u],\n", + " ]\n", + "\n", + " ci_or = odds_ratio(\n", + " table, kind=\"conditional\"\n", + " ).confidence_interval(confidence_level=0.95)\n", + "\n", + " # Approximate VE interval: odds ratio approximates risk ratio\n", + " return 1 - ci_or.high, 1 - ci_or.low\n", + "\n", + "# Overall\n", + "print(cornfield_ve_ci(9, 19965, 169, 20172))\n", + "\n", + "# Ages 65 and older\n", + "print(cornfield_ve_ci(1, 3239 + 805, 19, 3255 + 812))" + ], + "metadata": { + "id": "zPeHWmFHLuGW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cornfield-results-description" + }, + "source": [ + "The Cornfield calculations give approximate efficacy intervals of **[89.6%, 97.6%] overall** and **[66.8%, 99.9%] for participants aged 65 and older**. The wider interval for older participants reflects the limited information in the small case counts. Differences across interval methods are particularly noticeable in that group." + ] } ], "metadata": { From 37761cc7c881d2a53b27d6f70eb8263ddebf7be8 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:17:05 +0200 Subject: [PATCH 07/32] Correct CI method descriptions, add Cornfield exact interval --- CM1/r-rct-vaccines.irnb | 108 ++++++++++++++++++++++++++++------------ 1 file changed, 77 insertions(+), 31 deletions(-) diff --git a/CM1/r-rct-vaccines.irnb b/CM1/r-rct-vaccines.irnb index 720c2c58..e35ccec0 100644 --- a/CM1/r-rct-vaccines.irnb +++ b/CM1/r-rct-vaccines.irnb @@ -6,7 +6,9 @@ "id": "Di8kfLCYerNJ" }, "source": [ - "This notebook contains some RCT examples for teaching." + "# Vaccine RCT Examples\n", + "\n", + "We use vaccine trial counts to illustrate estimation of risk differences and vaccine efficacy, then compare several confidence interval methods. Reported risks and efficacy estimates are proportions unless explicitly expressed as percentages." ] }, { @@ -20,7 +22,7 @@ }, "outputs": [], "source": [ - "install.packages(\"PropCIs\") # Exact CI exploiting Bernoulli outcome using the Cornfield Procedure" + "install.packages(\"PropCIs\")" ] }, { @@ -42,28 +44,19 @@ "id": "Id8Df7PUerNX" }, "source": [ - " \n", "# Polio RCT\n", "\n", - "One of the earliest randomized experiments were the Polio vaccination trials conducted by the Public Health Service in 1954. The question was whether Salk vaccine prevented polio. Children in the study were randomly assigned either a treatment (polio vaccine shot) or a placebo (saline solution shot), without knowing which one they received. The doctors in the study, making the diagnosis, did not know whether a child received a vaccine or not. In other words, the trial was a double-blind, randomized controlled trial. The trial had to be large, because the rate at which Polio occured in the population was 50 per 100,000. The treatment group saw 33 polio cases per 200,745; the control group saw 115 cases per 201,229. The estimated average treatment effect is about\n", - "$$\n", - "-40\n", - "$$\n", - "with the 95% confidence band (based on approximate normality of the two sample means and their differences):\n", - "$$[-52, -28].$$\n", - "As this is an RCT, the confidence band suggests that the Polio vaccine **caused** the reduction in the risk of polio.\n", + "In the randomized portion of the 1954 Salk polio vaccine trial, children received either the vaccine or a placebo. Participants and the doctors assessing outcomes did not know the assignments. The vaccine group had 33 polio cases among 200,745 children; the placebo group had 115 among 201,229.\n", "\n", - "The interesting thing here is that we don't need the underlying individual data to evaluate the effectivess of the vaccine. This is because the outcomes are Bernoulli random variables, and we have enough information to compute the estimate of ATE as well as the confidence intervals from the group case counts.\n", + "We estimate the average treatment effect as the difference in case proportions. The estimate is about $-40.7$ cases per 100,000 children, with an approximate 95% confidence interval of $[-52.6,-28.9]$. Under the RCT assumptions, the difference estimates the causal effect of vaccination on the risk of polio.\n", "\n", - "We also compute the Vaccine Efficacy metric, which refers to the following measure according to the [CDC](https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section6.html):\n", + "Because the outcomes are Bernoulli, the group totals and case counts suffice to calculate the difference in means and its estimated standard error. We also estimate vaccine efficacy,\n", "$$\n", - "VE = \\frac{\\text{Risk for Unvaccinated - Risk for Vaccinated}}{\\text{Risk for Unvaccinated}}.\n", + "\\mathsf{VE}=1-\\frac{\\text{Risk for Vaccinated}}{\\text{Risk for Unvaccinated}},\n", "$$\n", - "It describes the relative reduction in risk caused by vaccination.\n", + "which measures the relative reduction in risk. The point estimate is approximately 71.2%.\n", "\n", - "\n", - "It is staightforward to get the VE estimate by just plugging-in the numbers, but how do we get the approximate variance estimate? I am too lazy to do calculations for the delta method, so I will just use a simulation (a form of approximate bootstrap) to obtain the confidence intervals.\n", - "\n" + "For the risk difference, we use a normal confidence interval. For vaccine efficacy, we simulate independent Gaussian draws for the two estimated risks using their estimated variances, calculate efficacy for each draw, and take the 2.5th and 97.5th percentiles. This is an approximate simulation-based interval, not an exact interval." ] }, { @@ -143,18 +136,15 @@ "id": "ahcRWgyEerNk" }, "source": [ - "# Pfizer/BNTX Covid-19 RCT\n", - "\n", - "Here is a link to the FDA [briefing](https://www.fda.gov/media/144245/download) and an interesting [discussion](\n", - "https://garycornell.com/2020/12/09/statistics-in-the-pfizer-data-how-good-is-the-vaccine/?fbclid=IwAR282lS0Vl3tWmicQDDhIJAQCMO8NIsCXyWbUWwTtPuKcnuJ2v0VWXRDQac), as well as data.\n", + "# Pfizer/BioNTech COVID-19 RCT\n", "\n", - "Pfizer/BNTX was the first vaccine approved for emergency use to reduce the risk of Covid-19 decease. In studies to assess vaccine efficacy, volunteers were randomly assigned to receive either a treatment (2-dose vaccination) or a placebo, without knowing which they received. The doctors making the diagnoses did not know now whether a given volunteer received a vaccination or not. The results of the study are given in the following table.\n", + "The [FDA briefing](https://www.fda.gov/media/144245/download) reports results from a randomized trial comparing a two-dose vaccine regimen with placebo. Participants and the observers assessing outcomes were blinded to assignment. The table below reports vaccine efficacy against symptomatic, confirmed COVID-19, overall and by age group.\n", "\n", - "![](https://lh6.googleusercontent.com/oiO6gYom1UZyrOhgpFx2iq8ike979u3805JHiVygP-Efh1Yaz2ttyPcgWKlT1AqHDM4v46th3EPIkOvRLyXA0fNUloPL-mL9eOFmSAzfbNOHyCZSQ0DyzMhcFUtQuZ520R5Qd2lj):\n", + "![Pfizer/BioNTech trial efficacy results](https://lh6.googleusercontent.com/oiO6gYom1UZyrOhgpFx2iq8ike979u3805JHiVygP-Efh1Yaz2ttyPcgWKlT1AqHDM4v46th3EPIkOvRLyXA0fNUloPL-mL9eOFmSAzfbNOHyCZSQ0DyzMhcFUtQuZ520R5Qd2lj)\n", "\n", - "Here we see both the overall effects and the effects by age group. The confidence intervals for the overall ATE are tight and suggest high effectiveness of the vaccine. The confidence intervals for the age groups 65-74 and 75+ are much wider due to the relatively small number of people in these groups. We could group 65-74 and >75 groups to evaluate the effectiveness of the vaccine for this broader age group and narrow the width of the confidence band.\n", + "For a simple illustration of the two-means approach, we treat the reported case counts and group totals as binomial data, abstracting from differences in follow-up. The calculations illustrate inference methods rather than reproduce the trial analysis.\n", "\n", - "We use the same approach as that for the Polio example. This gives slightly different results than the FDA result, because the FDA used inversion of exact binomial tests to construct confidence intervals. We use asymptotic approches based on approximate normality, which is more crude, but delivers a rather similar result.\n" + "We first apply the normal and Gaussian simulation approaches used in the polio example. We then pool the 65–74 and 75-and-older groups to illustrate how interval estimates differ when there are few observed cases." ] }, { @@ -238,7 +228,9 @@ "id": "kqupG5u7erNn" }, "source": [ - "In the code cell below we calculate the effectiveness of the vaccine for the two groups that are 65 or older" + "## Participants Aged 65 and Older\n", + "\n", + "Pooling the two older age groups gives 1 case among 4,044 vaccinated participants and 19 among 4,067 placebo participants. Estimated efficacy is approximately 94.7%. We repeat the Gaussian simulation to construct two-sided intervals and one-sided lower confidence bounds. With so few cases, the approximation can produce negative simulated risks and efficacy values above 100%." ] }, { @@ -295,7 +287,9 @@ "id": "A8wEW0sQerNq" }, "source": [ - "Let's try the parametric boostrap next, using the fact that the outcome is Bernouli." + "## Binomial Parametric Bootstrap\n", + "\n", + "We next simulate case counts from binomial distributions with the observed group sizes and estimated risks. The cell uses the ratio of simulated counts as an approximation to the risk ratio because the group sizes are nearly equal. The resulting percentile intervals remain approximate; simulating binomial counts does not make a bootstrap interval exact." ] }, { @@ -342,9 +336,9 @@ "id": "MjenLiQAerNw" }, "source": [ - "# Exact Binomial Test Inversion\n", + "# Score-Based Confidence Intervals for the Risk Ratio\n", "\n", - "It is possible to use exact inference by inverting tests based on the exact binomial nature of the outcome variable. Here, we perform the Cornfield Procedure to find the exact confidence interval on the estimate of vaccine efficacy." + "The next cells use `PropCIs::riskscoreci` to construct an approximate score confidence interval for the risk ratio. Transforming its endpoints from $[L,U]$ to $[1-U,1-L]$ gives an interval for vaccine efficacy. This is a score-based approximation, not the exact Cornfield procedure introduced below." ] }, { @@ -375,7 +369,7 @@ "id": "jQ64-QaxerN0" }, "source": [ - "Note that this exactly recovers the result in the FDA table (first row)." + "The overall score-based efficacy interval is approximately **[89.6%, 97.2%]**. It is close to, but does not exactly reproduce, the interval in the FDA table." ] }, { @@ -384,7 +378,7 @@ "id": "iQ_lO8userN2" }, "source": [ - "Next we repeat the cornfield procedure to find the exact confidence interval on vaccine effectiveness for the two groups that are 65 or older. Here we see a big discrepancy between various asymptotic approaches and the exact finite-sample inference. This occurs because the binomial counts are too low for central limit theorems to work successfully." + "We repeat the score-based calculation for participants aged 65 and older. The interval is approximately **[68.9%, 99.1%]**. With only one vaccinated case and 19 placebo cases, the choice of interval method has a substantial effect on the lower endpoint." ] }, { @@ -408,6 +402,58 @@ "1 - riskscoreci(1, NV, 19, NU, 0.95)$conf.int[2]\n", "1 - riskscoreci(1, NV, 19, NU, 0.95)$conf.int[1]" ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cornfield-method-description" + }, + "source": [ + "# Cornfield Exact Conditional Interval\n", + "\n", + "We now compute the Cornfield exact conditional confidence interval for the odds ratio; see [Cornfield (1956), *A Statistical Problem Arising from Retrospective Studies*](https://digicoll.lib.berkeley.edu/record/112867).\n", + "\n", + "For these rare outcomes, the odds ratio approximates the risk ratio. If $[L,U]$ is the odds-ratio interval, we therefore use $[1-U,1-L]$ as an approximate interval for vaccine efficacy. The conditional interval is exact for the odds ratio; its interpretation as an efficacy interval uses the rare-outcome approximation.\n", + "\n", + "The following cell calculates intervals for the full sample and for participants aged 65 and older." + ] + }, + { + "cell_type": "code", + "source": [ + "cornfield_ve_ci <- function(cases_v, n_v, cases_u, n_u) {\n", + " tab <- matrix(\n", + " c(cases_v, n_v - cases_v,\n", + " cases_u, n_u - cases_u),\n", + " nrow = 2, byrow = TRUE\n", + " )\n", + "\n", + " ci_or <- fisher.test(tab, conf.level = 0.95)$conf.int\n", + "\n", + " # Approximate VE interval: odds ratio approximates risk ratio\n", + " c(lower = 1 - ci_or[2], upper = 1 - ci_or[1])\n", + "}\n", + "\n", + "# Overall\n", + "cornfield_ve_ci(9, 19965, 169, 20172)\n", + "\n", + "# Ages 65 and older\n", + "cornfield_ve_ci(1, 3239 + 805, 19, 3255 + 812)" + ], + "metadata": { + "id": "pvs4wwrbLpdF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cornfield-results-description" + }, + "source": [ + "The Cornfield calculations give approximate efficacy intervals of **[89.6%, 97.6%] overall** and **[66.8%, 99.9%] for participants aged 65 and older**. The wider interval for older participants reflects the limited information in the small case counts. Differences across interval methods are particularly noticeable in that group." + ] } ], "metadata": { From ee448bff8c48dda95c1c834b7d351207007b2427 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:17:37 +0200 Subject: [PATCH 08/32] Use heteroskedasticity-robust SEs to match Python notebook --- PM2/r_heterogenous_wage_effects.irnb | 35 +++++++++++++++++++--------- 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/PM2/r_heterogenous_wage_effects.irnb b/PM2/r_heterogenous_wage_effects.irnb index f869b712..9f3370c3 100644 --- a/PM2/r_heterogenous_wage_effects.irnb +++ b/PM2/r_heterogenous_wage_effects.irnb @@ -117,11 +117,33 @@ "source": [ "# this cell takes 30 minutes to run\n", "index_sex <- grep(\"sex\", colnames(X))\n", - "effects_female <- hdm::rlassoEffects(x = X, y = y, index = index_sex, post = FALSE)\n", + "effects_female <- hdm::rlassoEffects(x = X, y = y, index = index_sex, post = FALSE)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Heteroskedasticity-robust covariance, matching the Python notebook\n", + "e <- effects_female$residuals$e\n", + "v <- effects_female$residuals$v\n", + "n <- nrow(v)\n", + "\n", + "scores <- sweep(e * v, 2, colMeans(v^2), FUN = \"/\")\n", + "V <- crossprod(scores) / n\n", + "\n", + "effects_female$se <- sqrt(diag(V) / n)\n", + "effects_female$t <- effects_female$coefficients / effects_female$se\n", + "effects_female$pval <- 2 * pnorm(-abs(effects_female$t))\n", + "\n", "result <- summary(effects_female)\n", "result$coef\n", "print(xtable(result$coef[, c(1, 2, 4)], type = \"latex\"), digits = 3)" - ] + ], + "metadata": { + "id": "FQp-qd1qNeWX" + }, + "execution_count": null, + "outputs": [] }, { "cell_type": "markdown", @@ -181,15 +203,6 @@ "joint_ci\n", "print(xtable(joint_ci), type = \"latex\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "pAPpZBivh15T" - }, - "outputs": [], - "source": [] } ], "metadata": { From 7097b192bab2996a112230157deb50b988c671da Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:17:50 +0200 Subject: [PATCH 09/32] Add restyled histogram of estimation error --- PM2/r_orthogonal_orig.irnb | 51 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/PM2/r_orthogonal_orig.irnb b/PM2/r_orthogonal_orig.irnb index 29ad277d..838a47d8 100644 --- a/PM2/r_orthogonal_orig.irnb +++ b/PM2/r_orthogonal_orig.irnb @@ -181,6 +181,57 @@ "print(hist_plot)\n" ] }, + { + "cell_type": "code", + "source": [ + "df <- data.frame(\n", + " Method = factor(\n", + " rep(c(\"Single selection\", \"Double Lasso\"),\n", + " times = c(length(Naive), length(Orthogonal))),\n", + " levels = c(\"Single selection\", \"Double Lasso\")\n", + " ),\n", + " Value = c(as.numeric(Naive) - 1, as.numeric(Orthogonal) - 1)\n", + ")\n", + "\n", + "hist_plot <- ggplot(df, aes(x = Value, fill = Method)) +\n", + " geom_histogram(\n", + " aes(y = after_stat(density)),\n", + " binwidth = 0.1, boundary = 0,\n", + " position = \"identity\",\n", + " alpha = 0.45,\n", + " colour = \"white\", linewidth = 0.2\n", + " ) +\n", + " geom_vline(\n", + " xintercept = 0, colour = \"grey30\",\n", + " linetype = \"dashed\", linewidth = 0.5\n", + " ) +\n", + " scale_fill_manual(values = c(\n", + " \"Single selection\" = \"#D55E00\",\n", + " \"Double Lasso\" = \"#0072B2\"\n", + " )) +\n", + " scale_x_continuous(breaks = seq(-2, 2, 0.5)) +\n", + " scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +\n", + " labs(\n", + " x = expression(\"Estimation error, \" * hat(alpha) - alpha),\n", + " y = \"Density\",\n", + " fill = NULL\n", + " ) +\n", + " theme_classic(base_size = 11) +\n", + " theme(legend.position = \"top\")\n", + "\n", + "options(repr.plot.width = 5.5, repr.plot.height = 3.5)\n", + "print(hist_plot)\n", + "\n", + "# Optional export\n", + "ggsave(\"orthogonal_vs_naive.pdf\", hist_plot,\n", + " width = 5.5, height = 3.5, units = \"in\")" + ], + "metadata": { + "id": "XA3v_xLtnHr7" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "markdown", "metadata": { From 88bdea8a2417681575e0278949a71b90a4e4b5bd Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:18:04 +0200 Subject: [PATCH 10/32] Remove stale Colab badge pointing at feature branch --- CM1/r-rct-penn-precision-adj.irnb | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/CM1/r-rct-penn-precision-adj.irnb b/CM1/r-rct-penn-precision-adj.irnb index 8673799f..bc39f7e8 100644 --- a/CM1/r-rct-penn-precision-adj.irnb +++ b/CM1/r-rct-penn-precision-adj.irnb @@ -1,15 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, { "cell_type": "markdown", "metadata": { From 91d309f9f6ebca646a2c0c1c4fd4aacb75aca7c3 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 11:18:12 +0200 Subject: [PATCH 11/32] Remove trailing empty cell --- PM2/python_heterogeneous_wage_effects.ipynb | 7 ------- 1 file changed, 7 deletions(-) diff --git a/PM2/python_heterogeneous_wage_effects.ipynb b/PM2/python_heterogeneous_wage_effects.ipynb index edc2592b..4abb2421 100644 --- a/PM2/python_heterogeneous_wage_effects.ipynb +++ b/PM2/python_heterogeneous_wage_effects.ipynb @@ -318,13 +318,6 @@ "summary['CI upper'] = df['point'] + c * df['stderr']\n", "summary" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From ebd5fdf0a6dfbb671b233e40e09a574e492f4509 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Wed, 23 Sep 2026 12:14:26 +0200 Subject: [PATCH 12/32] Update .gitignore --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index fa8cd793..83e4b313 100644 --- a/.gitignore +++ b/.gitignore @@ -132,3 +132,7 @@ dmypy.json /CM1/hdmpy /AC1/hdmpy /PM2/hdmpy + +# Notebook updates +/TEMP_updated_notebooks +/tools From bc4399ce916d937753311a0674ae217f8c01bcfe Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Fri, 25 Sep 2026 16:19:31 +0200 Subject: [PATCH 13/32] Add long story short examples --- T-ADML/gasoline_example/.DS_Store | Bin 0 -> 8196 bytes .../BHP2017/data_BHP2_with_region.csv | 3641 +++++++++++++++++ T-ADML/gasoline_example/ablation.py | 204 + T-ADML/gasoline_example/automl.py | 211 + T-ADML/gasoline_example/debiased.py | 59 + T-ADML/gasoline_example/pyproject.toml | 27 + T-ADML/gasoline_example/python_ADML_gas.ipynb | 735 ++++ T-ADML/gasoline_example/rfriesz.py | 701 ++++ T-ADML/gasoline_example/sensitivity.py | 185 + T-ADML/gasoline_example/sievereisz.py | 124 + T-ADML/gasoline_example/utilities.py | 222 + T-ADML/gasoline_example/uv.lock | 3230 +++++++++++++++ T-ADML/python_ADML_401k.ipynb | 1152 ++++++ 13 files changed, 10491 insertions(+) create mode 100644 T-ADML/gasoline_example/.DS_Store create mode 100644 T-ADML/gasoline_example/BHP2017/data_BHP2_with_region.csv create mode 100644 T-ADML/gasoline_example/ablation.py create mode 100644 T-ADML/gasoline_example/automl.py create mode 100644 T-ADML/gasoline_example/debiased.py create mode 100644 T-ADML/gasoline_example/pyproject.toml create mode 100644 T-ADML/gasoline_example/python_ADML_gas.ipynb create mode 100644 T-ADML/gasoline_example/rfriesz.py create mode 100644 T-ADML/gasoline_example/sensitivity.py create mode 100644 T-ADML/gasoline_example/sievereisz.py create mode 100644 T-ADML/gasoline_example/utilities.py create mode 100644 T-ADML/gasoline_example/uv.lock create mode 100644 T-ADML/python_ADML_401k.ipynb diff --git a/T-ADML/gasoline_example/.DS_Store b/T-ADML/gasoline_example/.DS_Store new file mode 100644 index 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+++ b/T-ADML/gasoline_example/ablation.py @@ -0,0 +1,204 @@ +import numpy as np +from sklearn.model_selection import cross_val_predict +from sklearn.base import clone +from sklearn.linear_model import LinearRegression + +def cross_val_reisz_and_reg(model_reisz, model_reg, X, y, splits, moment_fn): + reisz_preds = np.zeros(X.shape[0]) + moment_preds = np.zeros(X.shape[0]) + reg_preds = np.zeros(X.shape[0]) + moment_reg_preds = np.zeros(X.shape[0]) + for train, test in splits: + est = clone(model_reisz) + est.fit(X[train]) + reisz_preds[test] = est.predict(X[test]) + moment_preds[test] = moment_fn(X[test], est.predict) + + est = clone(model_reg) + est.fit(X[train], y[train]) + reg_preds[test] = est.predict(X[test]) + moment_reg_preds[test] = moment_fn(X[test], est.predict) + return reisz_preds, moment_preds, reg_preds, moment_reg_preds + +def plr_cross_val_reisz_and_reg(model_y, model_t, X, y, splits, moment_fn): + res_preds_t = np.zeros(X.shape[0]) + moment_preds = np.zeros(X.shape[0]) + moment_reg_preds = np.zeros(X.shape[0]) + reg_preds_y = np.zeros(X.shape[0]) + reg_preds = np.zeros(X.shape[0]) + for train, test in splits: + model_t_ = clone(model_t) + model_t_.fit(X[train, 1:], X[train, 0]) + res_preds_t[test] = X[test, 0] - model_t_.predict(X[test, 1:]) + reisz_fn = lambda x: (x[:, 0] - model_t_.predict(x[:, 1:])) + moment_preds[test] = moment_fn(X[test], reisz_fn) + + model_y_ = clone(model_y) + model_y_.fit(X[train, 1:], y[train]) + reg_preds_y[test] = model_y_.predict(X[test, 1:]) + partial_reg_fn = lambda x: model_y_.predict(x[:, 1:]) + moment_reg_preds[test] = moment_fn(X[test], partial_reg_fn) + + theta = LinearRegression(fit_intercept=False).fit(res_preds_t.reshape(-1, 1), y - reg_preds_y).coef_[0] + reg_preds = theta * res_preds_t + reg_preds_y + moment_reg_preds = theta * moment_preds + moment_reg_preds + + reisz_preds = res_preds_t / np.mean(res_preds_t**2) + moment_preds = moment_preds / np.mean(res_preds_t**2) + + return reisz_preds, moment_preds, reg_preds, moment_reg_preds + + +def ablation_inference(y, moment, reisz, reg, moment_reg, ablation_groups, verbose): + phi_a = 2 * moment[0] - reisz[0]**2 # orthogonal moment for E[a(X)^2] + ma_sq = np.mean(phi_a) # estimate of E[a(X)^2] + phi_g = 2*y*reg[0] - reg[0]**2 # orthogonal moment for E[g(X)^2] + mg_sq = np.mean(phi_g) # estimate of E[g(X)^2] + all_etasq = [] + all_Cy = [] + all_dtheta = [] + all_rho = [] + for it, (name, group) in enumerate(ablation_groups): + i = it + 1 + + # Estimating eta^2 = (E[a(X)^2] - E[a_s(X)^2]) / E[a(X)^2] + phi_as = 2 * moment[i] - reisz[i]**2 # orthogonal moment for E[a_s(X)^2] + mas_sq = np.mean(phi_as) # estimate of E[a_s(X)^2] + etasq = (ma_sq - mas_sq) / ma_sq # estimate of eta^2 + inf = (phi_as - mas_sq)/ma_sq - mas_sq * (phi_a - ma_sq) / (ma_sq**2) # influence function for eta^2 via delta method + stderr = np.sqrt(np.mean(inf**2) / inf.shape[0]) # standard error for eta^2 + Cd = etasq / (1 - etasq) # estimate of Cd^2 = eta^2 / (1 - eta^2) + + # Estimating Cy=E[g(X)^2 - g_s(X)^2] / E[(Y - g_s(X))^2] + phi_gs = 2*y*reg[i] - reg[i]**2 # orthogonal moment for E[g_s(X)^2] + mgs_sq = np.mean(phi_gs) # estimate of E[g_s(X)^2] + phi_ss = (y - reg[i])**2 # orthogonal moment for E[(Y - g_s(X))^2] + sigma_sq = np.mean(phi_ss) # estimate of sigma^2 = E[(Y - g_s(X))^2] + Cy = (mg_sq - mgs_sq)/ sigma_sq # estimate of Cy^2 + # influence function for Cy^2 via delta method + inf_y = (phi_g - mg_sq)/sigma_sq + inf_y += - (phi_gs - mgs_sq)/sigma_sq + inf_y += - (mg_sq - mgs_sq) * (phi_ss - sigma_sq) / (sigma_sq**2) + # standard error for Cy^2 + stderr_y = np.sqrt(np.mean(inf_y**2) / inf_y.shape[0]) + + # Estimating dtheta + phi_dtheta = (moment_reg[i] + (y - reg[i]) * reisz[i]) + phi_dtheta -= (moment_reg[0] + (y - reg[0]) * reisz[0]) + dtheta = np.mean(phi_dtheta) + stderr_dtheta = np.sqrt(np.mean((phi_dtheta - dtheta)**2) / phi_dtheta.shape[0]) + + # Estimating rho + denom_g = np.sqrt(np.clip(mg_sq - mgs_sq, 1e-6, np.inf)) + denom_a = np.sqrt(np.clip(ma_sq - mas_sq, 1e-6, np.inf)) + rho = dtheta / (denom_g * denom_a) + phi_rho = (phi_dtheta - dtheta) / (denom_g * denom_a) + phi_rho += - .5 * (phi_g - mg_sq - phi_gs + mgs_sq) * dtheta / ((denom_g**(3)) * denom_a) + phi_rho += - .5 * (phi_a - ma_sq - phi_as + mas_sq) * dtheta / (denom_g * (denom_a**(3))) + stderr_rho = np.sqrt(np.mean(phi_rho**2) / phi_rho.shape[0]) + + if verbose: + print(f'{name}, etasq={etasq:.4f} ({stderr:.4f}), Cd={Cd:.4f}, ' + f'Cy={Cy:.4f} ({stderr_y:.4f}), RV={min(etasq, Cy):.4f}', + f'rho={rho:.4f} ({stderr_rho:.4f})', + f'dtheta={dtheta:.4f} ({stderr_dtheta:.4f})') + all_etasq += [(etasq, stderr)] + all_Cy += [(Cy, stderr_y)] + all_dtheta += [(dtheta, stderr_dtheta)] + all_rho += [(rho, stderr_rho)] + return all_etasq, all_Cy, all_dtheta, all_rho + +def plr_ablation(seed, ablation_groups, moment_fn, get_data_fn, model_y_gen, model_t_gen, verbose): + X, y, num_cols, splits, column_names = get_data_fn(seed) + num_cols_t = [t - 1 for t in num_cols[1:]] + model_t = model_t_gen(num_cols_t, cv=splits, verbose=verbose)(X[:, 1:], X[:, 0]) + model_y = model_y_gen(num_cols_t, cv=splits, verbose=verbose)(X[:, 1:], y) + if verbose: + print('model_t', model_t()) + print('model_y', model_y()) + + rp, mp, regp, mregp = plr_cross_val_reisz_and_reg(model_y(), model_t(), X, y, splits, moment_fn) + reisz, moment, reg, moment_reg = [rp], [mp], [regp], [mregp] + + for name, group in ablation_groups: + if verbose: + print(name) + Xmi = X[:, np.setdiff1d(np.arange(len(column_names)), group)] + rp, mp, regp, mregp = plr_cross_val_reisz_and_reg(model_y(), model_t(), Xmi, y, splits, moment_fn) + reisz += [rp] + moment += [mp] + reg += [regp] + moment_reg += [mregp] + + return ablation_inference(y, moment, reisz, reg, moment_reg, ablation_groups, verbose) + +def ablation(seed, ablation_groups, moment_fn, get_data_fn, reg_fn_gen, rr_fn_gen, verbose): + X, y, num_cols, splits, column_names = get_data_fn(seed) + reisz_fn = rr_fn_gen(moment_fn, num_cols, cv=splits, verbose=verbose)(X) + reg_fn = reg_fn_gen(num_cols, cv=splits, verbose=verbose)(X, y) + if verbose: + print('reisz_model', reisz_fn()) + print('reg_model', reg_fn()) + + rp, mp, regp, mregp = cross_val_reisz_and_reg(reisz_fn(), reg_fn(), X, y, splits, moment_fn) + reisz, moment, reg, moment_reg = [rp], [mp], [regp], [mregp] + + for name, group in ablation_groups: + if verbose: + print(name) + Xmi = X[:, np.setdiff1d(np.arange(len(column_names)), group)] + rp, mp, regp, mregp = cross_val_reisz_and_reg(reisz_fn(), reg_fn(), Xmi, y, splits, moment_fn) + reisz += [rp] + moment += [mp] + reg += [regp] + moment_reg += [mregp] + + return ablation_inference(y, moment, reisz, reg, moment_reg, ablation_groups, verbose) + + +def aggregate(array, median=True): + if median: + agg_point = np.median([r[0] for r in array]) + agg_stderr = np.sqrt(np.median([r[1]**2 + (r[0] - agg_point)**2 for r in array])) + else: + agg_point = np.mean([r[0] for r in array]) + agg_stderr = np.sqrt(np.mean([r[1]**2 for r in array]) + np.var([r[0] for r in array])) + return (agg_point, agg_stderr) + +def process_multiseed(ablation_groups, all_etasq_list, all_Cy_list, all_dtheta_list, all_rho_list, median=True): + all_etasq = list(zip(*all_etasq_list)) + all_Cy = list(zip(*all_Cy_list)) + all_dtheta = list(zip(*all_dtheta_list)) + all_rho = list(zip(*all_rho_list)) + multiseed_etasq = {} + multiseed_Cy = {} + multiseed_dtheta = {} + multiseed_rho = {} + for it, (name, _) in enumerate(ablation_groups): + multiseed_etasq[name] = aggregate(all_etasq[it], median=median) + multiseed_Cy[name] = aggregate(all_Cy[it], median=median) + multiseed_dtheta[name] = aggregate(all_dtheta[it], median=median) + multiseed_rho[name] = aggregate(all_rho[it], median=median) + return multiseed_etasq, multiseed_Cy, multiseed_dtheta, multiseed_rho + + +def plr_ablation_multiseed(ablation_groups, moment_fn, get_data_fn, model_y_gen, model_t_gen, verbose, median=True): + all_etasq_list, all_Cy_list, all_dtheta_list, all_rho_list = [], [], [], [] + for seed in np.arange(123, 128): + all_etasq, all_Cy, all_dtheta, all_rho = plr_ablation(seed, ablation_groups, moment_fn, get_data_fn, model_y_gen, model_t_gen, verbose) + all_etasq_list.append(all_etasq) + all_Cy_list.append(all_Cy) + all_dtheta_list.append(all_dtheta) + all_rho_list.append(all_rho) + return process_multiseed(ablation_groups, all_etasq_list, all_Cy_list, all_dtheta_list, all_rho_list, median=median) + + +def ablation_multiseed(ablation_groups, moment_fn, get_data_fn, reg_fn_gen, rr_fn_gen, verbose, median=True): + all_etasq_list, all_Cy_list, all_dtheta_list, all_rho_list = [], [], [], [] + for seed in np.arange(123, 128): + all_etasq, all_Cy, all_dtheta, all_rho = ablation(seed, ablation_groups, moment_fn, get_data_fn, reg_fn_gen, rr_fn_gen, verbose) + all_etasq_list.append(all_etasq) + all_Cy_list.append(all_Cy) + all_dtheta_list.append(all_dtheta) + all_rho_list.append(all_rho) + return process_multiseed(ablation_groups, all_etasq_list, all_Cy_list, all_dtheta_list, all_rho_list, median=median) \ No newline at end of file diff --git a/T-ADML/gasoline_example/automl.py b/T-ADML/gasoline_example/automl.py new file mode 100644 index 00000000..7535c0a2 --- /dev/null +++ b/T-ADML/gasoline_example/automl.py @@ -0,0 +1,211 @@ +import numpy as np +from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline +from econml.sklearn_extensions.model_selection import GridSearchCVList +from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor +from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier +from sklearn.linear_model import LassoCV, LogisticRegressionCV, LinearRegression, Lasso, LogisticRegression +from rfriesz import RFreg, RFrr, poly_feature_fns +from sievereisz import SieveRR, SparseSieveRR, Featurizer +from sklearn.base import clone, BaseEstimator + + +# Generic machine learning regressor with cross validation +def first_stage_reg(numeric_columns=[], cv=None): + return GridSearchCVList([ + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('lasso', Lasso())]), + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('feat', Featurizer(degree=1, normalize=False)), + ('lasso', Lasso())]), + RandomForestRegressor(n_estimators=100, random_state=123), + RFreg(reg_feature_fns=poly_feature_fns(10), + min_var_leaf_on_val=True, + min_impurity_decrease=1e-4, max_samples=.85, inference=False, subforest_size=1, + random_state=123) + ], + param_grid_list=[ + {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]}, + {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]}, + {'max_depth': [3, 5], + 'min_samples_leaf': [10, 50]}, + {'reg_feature_fns': [poly_feature_fns(2)], + 'min_samples_leaf': [10], + 'min_var_fraction_leaf': [0.01, 0.1], + 'l2': [1e-3, 1e-2]} + ], + cv=cv, + scoring='neg_mean_squared_error', + n_jobs=-1) + +# Returns a function that when called with data (X, y), returns a generator that when called +# returns an un-fitted regression estimator; e.g. the one with the best hyperparameters. +def reg_fn_gen(numeric_columns, cv=None, verbose=0): + def get_reg_fn(X, y): + est = first_stage_reg(numeric_columns, cv=cv).fit(X, y) + if verbose > 0: + print('Chose: ', est.best_score_, est.best_estimator_) + print('Scores: ', [(l.best_score_, l.best_estimator_) for l in est._gcv_list]) + return lambda: clone(est.best_estimator_) + return get_reg_fn + + +# The negative of the reisz score: L(g) := E[2 * m(X;g) - g(X)^2] +def neg_reisz_score_gen(moment_fn): + def neg_reisz_score(model, X): + return np.mean(2 * moment_fn(X, model.predict) - model.predict(X)**2) + return neg_reisz_score + +# A generic machine learning riesz representer estimator with cross-validation +def first_stage_rr(moment_fn, numeric_columns, cv=None): + return GridSearchCVList([ + SieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn), + SieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn), + SparseSieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn, tol=1e-4), + SparseSieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn, tol=1e-4), + RFrr(riesz_feature_fns=poly_feature_fns(10), + moment_fn=moment_fn, + min_var_leaf_on_val=True, + min_impurity_decrease=0.001, + max_samples=.65, + inference=False, + subforest_size=1, + random_state=123) + ], + param_grid_list=[ + {'alpha': np.geomspace(1e-6, 1e3, 20)}, + {'alpha': np.geomspace(1e-6, 1e3, 20)}, + {'alpha': np.geomspace(1e-6, 1e3, 20), + 'l1_ratio': [.5, 1.0]}, + {'alpha': np.geomspace(1e-6, 1e3, 20), + 'l1_ratio': [.5, 1.0]}, + {'riesz_feature_fns': [poly_feature_fns(2), poly_feature_fns(3)], + 'min_samples_leaf': [10, 50], + 'min_var_fraction_leaf': [0.01, 0.1], + 'l2': [1e-5, 1e-3], + 'max_depth': [5, None]} + ], + scoring=neg_reisz_score_gen(moment_fn), + cv=cv, verbose=0, n_jobs=-1) + + +# Returns a function that when called with data X, returns a generator that when called +# returns an un-fitted riesz estimator; e.g. the one with the best hyperparameters. +def rr_fn_gen(moment_fn, numeric_columns, cv=None, verbose=0): + def get_rr_fn(X): + est = first_stage_rr(moment_fn, numeric_columns, cv=cv).fit(X) + if verbose > 0: + print('Chose: ', est.best_score_, est.best_estimator_) + print('Scores: ', [(l.best_score_, l.best_estimator_) for l in est._gcv_list]) + return lambda: clone(est.best_estimator_) + return get_rr_fn + + +# Generic machine learning regressor with cross validation +def first_stage_model_y(numeric_columns=[], cv=None): + return GridSearchCVList([ + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('lasso', Lasso())]), + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('feat', Featurizer(degree=1, normalize=False)), + ('lasso', Lasso())]), + RandomForestRegressor(n_estimators=100, random_state=123) + ], + param_grid_list=[ + {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]}, + {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]}, + {'max_depth': [3, 5], + 'min_samples_leaf': [10, 50]}, + ], + cv=cv, + scoring='neg_mean_squared_error', + n_jobs=-1) + +# Returns a function that when called with data (X, y), returns a generator that when called +# returns an un-fitted regression estimator; e.g. the one with the best hyperparameters. +def model_y_fn_gen(numeric_columns, cv=None, verbose=0): + def get_reg_fn(X, y): + est = first_stage_model_y(numeric_columns, cv=cv).fit(X, y) + if verbose > 0: + print('Chose: ', est.best_score_, est.best_estimator_) + print('Scores: ', [(l.best_score_, l.best_estimator_) for l in est._gcv_list]) + return lambda: clone(est.best_estimator_) + return get_reg_fn + + +class RegRandomForestClassifier(BaseEstimator): + def __init__(self, *, max_depth=None, min_samples_leaf=1): + self.max_depth = max_depth + self.min_samples_leaf = min_samples_leaf + + def fit(self, X, y): + self.model_ = RandomForestClassifier(n_estimators=100, random_state=123, + max_depth=self.max_depth, + min_samples_leaf=self.min_samples_leaf) + self.model_.fit(X, y) + return self + + def predict(self, X): + return self.model_.predict_proba(X)[:, 1] + +class RegLogisticRegression(BaseEstimator): + def __init__(self, *, C=1): + self.C = C + + def fit(self, X, y): + self.model_ = LogisticRegression(C=self.C, max_iter=1000, random_state=123) + self.model_.fit(X, y) + return self + + def predict(self, X): + return self.model_.predict_proba(X)[:, 1] + +# Generic machine learning classifier with cross validation +def first_stage_model_t(numeric_columns=[], cv=None): + return GridSearchCVList([ + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('lasso', Lasso())]), + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('feat', Featurizer(degree=1, normalize=False)), + ('lasso', Lasso())]), + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('lg', RegLogisticRegression())]), + Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)], + remainder='passthrough')), + ('feat', Featurizer(degree=1, normalize=False)), + ('lg', RegLogisticRegression())]), + RandomForestRegressor(n_estimators=100, random_state=123), + RegRandomForestClassifier(), + ], + param_grid_list=[ + {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]}, + {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]}, + {'lg__C': [1e-3, 1e-2, 1e-1, 1, 10, 100]}, + {'lg__C': [1e-3, 1e-2, 1e-1, 1, 10, 100]}, + {'max_depth': [3, 5], + 'min_samples_leaf': [10, 50]}, + {'max_depth': [3, 5], + 'min_samples_leaf': [10, 50]}, + ], + cv=cv, + scoring='neg_mean_squared_error', + n_jobs=-1) + +# Returns a function that when called with data (X, y), returns a generator that when called +# returns an un-fitted regression estimator; e.g. the one with the best hyperparameters. +def model_t_fn_gen(numeric_columns, cv=None, verbose=0): + def get_reg_fn(X, y): + est = first_stage_model_t(numeric_columns, cv=cv).fit(X, y) + if verbose > 0: + print('Chose: ', est.best_score_, est.best_estimator_) + print('Scores: ', [(l.best_score_, l.best_estimator_) for l in est._gcv_list]) + return lambda: clone(est.best_estimator_) + return get_reg_fn diff --git a/T-ADML/gasoline_example/debiased.py b/T-ADML/gasoline_example/debiased.py new file mode 100644 index 00000000..f09ad209 --- /dev/null +++ b/T-ADML/gasoline_example/debiased.py @@ -0,0 +1,59 @@ +import numpy as np +from sklearn.base import clone +from sklearn.linear_model import LinearRegression +from sklearn.model_selection import KFold +from utilities import mean_ci +import scipy + +class DebiasedMoment: + + def __init__(self, *, moment_fn, get_reisz_fn, get_reg_fn, n_splits=5): + self.moment_fn = moment_fn + self.get_reisz_fn = get_reisz_fn + self.get_reg_fn = get_reg_fn + self.n_splits = n_splits + + def fit(self, X, y): + y = y.flatten() + if self.n_splits == 1: + splits = [(np.arange(X.shape[0]), np.arange(X.shape[0]))] + elif isinstance(self.n_splits, int): + splits = list(KFold(n_splits=self.n_splits).split(X)) + else: + splits = self.n_splits + + reisz_pred = np.zeros(X.shape[0]) + reg_pred = np.zeros(X.shape[0]) + moment_pred = np.zeros(X.shape[0]) + moment_reisz = np.zeros(X.shape[0]) + self.reisz_models_ = [] + self.reg_models_ = [] + reisz_fn = self.get_reisz_fn(X) + reg_fn = self.get_reg_fn(X, y) + for train, test in splits: + reisz = reisz_fn().fit(X[train]) + reisz_pred[test] = reisz.predict(X[test]) + reg = reg_fn().fit(X[train], y[train]) + reg_pred[test] = reg.predict(X[test]) + moment_pred[test] = self.moment_fn(X[test], reg.predict) + moment_reisz[test] = self.moment_fn(X[test], reisz.predict) + self.reisz_models_.append(reisz) + self.reg_models_.append(reg) + + self.reg_pred_ = reg_pred + self.reisz_pred_ = reisz_pred + self.moment_ = moment_pred + reisz_pred * (y - reg_pred) + self.moment_reisz_ = moment_reisz + self.moment_pred_ = moment_pred + self.y_ = y.copy() + + epsilon = LinearRegression(fit_intercept=False).fit(reisz_pred.reshape(-1, 1), y - reg_pred).coef_[0] + moment_pred = moment_pred + moment_reisz * epsilon + reg_pred = reg_pred + epsilon * reisz_pred + self.tmle_moment_ = moment_pred + reisz_pred * (y - reg_pred) + return self + + def avg_moment(self, alpha=0.05, tmle=False): + if tmle: + return mean_ci(self.tmle_moment_, confidence=1-alpha) + return mean_ci(self.moment_, confidence=1-alpha) diff --git a/T-ADML/gasoline_example/pyproject.toml b/T-ADML/gasoline_example/pyproject.toml new file mode 100644 index 00000000..43949a69 --- /dev/null +++ b/T-ADML/gasoline_example/pyproject.toml @@ -0,0 +1,27 @@ +[project] +name = "adml-gasoline" +version = "0.1.0" +requires-python = ">=3.11" + +dependencies = [ + "numpy>=2.0", + "scipy>=1.14", + "pandas>=2.2", + "matplotlib>=3.9", + "statsmodels>=0.14", + "joblib>=1.4", + "scikit-learn>=1.5", + "econml>=0.15", + # Needed to run the notebook itself + "ipykernel>=6.29", +] + +[dependency-groups] +# Optional: `uv sync --group notebook` if you want the full Jupyter UI +# rather than just a kernel for VS Code / an existing Jupyter install. +notebook = [ + "jupyterlab>=4.2", +] + +[tool.uv] +package = false diff --git a/T-ADML/gasoline_example/python_ADML_gas.ipynb b/T-ADML/gasoline_example/python_ADML_gas.ipynb new file mode 100644 index 00000000..9163e4d0 --- /dev/null +++ b/T-ADML/gasoline_example/python_ADML_gas.ipynb @@ -0,0 +1,735 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Empirical Example for Sensitivity Analysis: Gasoline Demand\n", + "\n", + "This is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n", + "*Long Story Short: Omitted Variable Bias in Causal Machine Learning* \n", + "\n", + "The original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "import scipy.special\n", + "import joblib\n", + "from statsmodels.api import OLS\n", + "from sklearn.model_selection import StratifiedGroupKFold\n", + "import pandas as pd\n", + "import math\n", + "from sklearn.base import clone\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LassoCV, LogisticRegressionCV, LinearRegression, Lasso, LogisticRegression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "from sklearn.exceptions import ConvergenceWarning\n", + "\n", + "warnings.filterwarnings('ignore', category=ConvergenceWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sensitivity import dml_sensitivity_bounds, dml_tvalue\n", + "from sensitivity import sensitivity_bounds, tvalue, sensitivity_contours\n", + "from debiased import DebiasedMoment\n", + "from econml.dml import LinearDML" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from automl import reg_fn_gen, rr_fn_gen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting\n", + "\n", + "The data come from the 2001 National Household Travel Survey and contain household gasoline consumption, gasoline prices, income brackets, and 24 observed controls describing population density, urbanization, demographics, and Census regions. After the sample restrictions used in the earlier demand studies, the analysis contains 3,466 households.\n", + "\n", + "The outcome $Y$ is log gasoline consumption, the treatment $D$ is log gasoline price, and $X^o$ collects the observed controls. Under conditional ignorability, the short regression is\n", + "\n", + "$$g_s(d, x^o) = \\mathrm{E}[Y \\mid D = d, X^o = x^o],$$\n", + "\n", + "and the target is the average causal derivative\n", + "\n", + "$$\\theta_s = \\mathrm{E}\\big\\{\\partial_d\\, g_s(D, X^o)\\big\\}.$$\n", + "\n", + "Because both variables are in logarithms, $\\theta_s$ is the average price elasticity of gasoline demand. Its short Riesz representer is the negative conditional density score,\n", + "\n", + "$$\\alpha_s(D, X^o) = -\\partial_d \\log f_{D \\mid X^o}(D \\mid X^o).$$\n", + "\n", + "The moment function below encodes $m(W_s, g) = \\partial_d\\, g(D, X^o)$. The derivative with respect to log price is approximated by a central finite difference, $\\{g(D + \\epsilon, X^o) - g(D - \\epsilon, X^o)\\}/(2\\epsilon)$ with $\\epsilon = 0.01$." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# The moment function, whose average we want to estimate: E[m(X; g)]\n", + "def moment_fn(x, test_fn):\n", + " epsilon = 0.01\n", + " t1 = np.hstack([x[:, [0]] + epsilon, x[:, 1:]])\n", + " t0 = np.hstack([x[:, [0]] - epsilon, x[:, 1:]])\n", + " return (test_fn(t1) - test_fn(t0)) / (2*epsilon)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "Following the earlier demand studies, households with very low prices or incomes are dropped. Besides the full sample (`q=5`), the analysis uses four income groups. The first three (`q=1,2,3`) are overlapping groups centered near annual incomes of \\$42,500, \\$57,500 and \\$72,500. The fourth (`q=4`) contains high-income households with income above roughly \\$110,000 ($\\log y \\geq 11.6$).\n", + "\n", + "Cross-validation and cross-fitting are grouped by state, so households from the same state remain in the same fold. The folds are stratified by Census region, and region dummies are included as controls." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Data loading and manipulation\n", + "\n", + "# for semi-synthetic data generation\n", + "def true_f(X):\n", + " y = (-.7 - .4 * scipy.special.expit(3 * (1 - 2*(X[:, 1] - 10.5)))) * X[:, 0]\n", + " y += scipy.special.expit(3 * (1 - 2 * (X[:, 1] > 11.5)))\n", + " return y\n", + "\n", + "# def true_f(X):\n", + "# y = -.7 * X[:, 0]\n", + "# y += scipy.special.expit(3 * (1 - 2 * (X[:, 1] > 11.5)))\n", + "# return y\n", + "\n", + "def get_data(q=None, synthetic=False, region=True, state=False, region_ids=None, random_seed=123):\n", + " df = pd.read_csv('./BHP2017/data_BHP2_with_region.csv')\n", + " # Throw away low prices and low income due to sample quality as in Chetverikov\n", + " df = df[df[\"log_p\"] > math.log(1.2)]\n", + " df = df[df[\"log_y\"] > math.log(15000)]\n", + " if not ((q==4) | (q==5)): # all data in first three brackets\n", + " df = df[df[\"log_y\"] >= math.log(42500) - .5]\n", + " df = df[df[\"log_y\"] <= math.log(72500) + .5]\n", + " if q == 1:\n", + " df = df[df[\"log_y\"] >= math.log(42500) - .5]\n", + " df = df[df[\"log_y\"] <= math.log(42500) + .5]\n", + " if q == 2:\n", + " df = df[df[\"log_y\"] >= math.log(57500) - .5]\n", + " df = df[df[\"log_y\"] <= math.log(57500) + .5]\n", + " if q == 3:\n", + " df = df[df[\"log_y\"] >= math.log(72500) - .5]\n", + " df = df[df[\"log_y\"] <= math.log(72500) + .5]\n", + " if q == 4:\n", + " df = df[df[\"log_y\"] >= 11.6]\n", + " if region_ids is not None: # Sub-select particular regions\n", + " df = df[np.any([df['region'] == r for r in region_ids], axis=0)]\n", + " Xdf = df.iloc[:, 1:] # Covariates\n", + " X_nostatedum = Xdf.drop([\"distance_oil1000\", \"share\"], axis=1).values \n", + " if state: # whether to control for state id; almost no price variation left\n", + " state_dum = pd.get_dummies(Xdf['state_fips'], prefix=\"state\", drop_first=True)\n", + " Xdf = pd.concat([Xdf, state_dum], axis = 1)\n", + " if region: # whether to control for regrion id\n", + " region_dum = pd.get_dummies(Xdf['region'], prefix=\"region\", drop_first=True)\n", + " Xdf = pd.concat([Xdf, region_dum], axis = 1)\n", + " # Drop instrument and things we've one-hot encoded and weird share variable\n", + " Xdf = Xdf.drop([\"distance_oil1000\", \"state_fips\", \"share\", 'region'], axis=1)\n", + " column_names = Xdf.columns\n", + " X = Xdf.values # to numpy array\n", + " y = df['log_q'].values # outcome to numpy array\n", + " if synthetic: # impute synthetic outcome based on known relationship\n", + " y = true_f(X) + np.random.normal(0, .2, size=X[:, 0].shape)\n", + " print(np.mean(moment_fn(X, true_f)))\n", + " \n", + " # Create state-block-wise and stratified by region folds for cross validation and cross fitting\n", + " cv = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=random_seed)\n", + " splits = list(cv.split(df, y=df['region'], groups=df['state_fips']))\n", + " return X, y, [0, 1, 6, 7, 8], splits, column_names" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# global result dictionaries and global parameters\n", + "ests = {}\n", + "res = {}\n", + "res_d = {}\n", + "q_grid = np.arange(1, 6)\n", + "synthetic = False # use synthetic outcome\n", + "verbose = 0\n", + "region = True # control for region" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Short Estimates Under Conditional Ignorability" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# General pipeline for all types of estimators to generate the estimation result we want\n", + "def generic_pipeline(estimator, q_grid, name, n_seeds=5):\n", + " ests = {}\n", + " res = {}\n", + " for q in q_grid:\n", + " print(f'q={q}')\n", + " ests[f'q={q}'] = []\n", + " estimate_list = []\n", + " for random_seed in np.arange(123, 123 + n_seeds):\n", + " X, y, num_cols, splits, column_names = get_data(q, region=region, synthetic=synthetic, random_seed=random_seed)\n", + " estimates, model = estimator(X, y, num_cols, splits)\n", + " print(estimates)\n", + " estimate_list.append(estimates)\n", + " ests[f'q={q}'].append(model)\n", + " estimate_list = np.array(estimate_list)\n", + " res[f'q={q}'] = {}\n", + " res[f'q={q}'][f'{name}'] = {}\n", + " res[f'q={q}'][f'{name}']['point'] = np.mean(estimate_list[:, 0])\n", + " if estimate_list.shape[1] > 2:\n", + " res[f'q={q}'][f'{name}']['stderr'] = np.sqrt(np.mean(estimate_list[:, 1]**2) \n", + " + np.var(estimate_list[:, 0]))\n", + " res[f'q={q}'] = pd.DataFrame(res[f'q={q}']).transpose()\n", + " return pd.concat(res), ests" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Transforming results to a nicely printable dataframe where each cell\n", + "# contains point (stderr) [ci_lower, ci_upper]\n", + "def transform_res_to_df(res, q_grid, alpha=None):\n", + " res_f = res.reset_index()\n", + " res_d = {}\n", + " for q in q_grid:\n", + " key = 'all <= 11.6' if q==0 else ('all' if q==5 else f'q={q}')\n", + " res_d[key] = {}\n", + " for method in np.unique(res_f['level_1'].values):\n", + " p = res.loc[(f'q={q}', method), 'point']\n", + " if alpha is not None:\n", + " s = res.loc[(f'q={q}', method), 'stderr']\n", + " error = scipy.stats.norm.ppf(1 - alpha) * s\n", + " l, u = p - error, p + error\n", + " res_d[key][method] = f\"{p:.2f} ({s:.2f}) [{l:.2f}, {u:.2f}]\"\n", + " else:\n", + " res_d[key][method] = f\"{p:.2f}\"\n", + " \n", + " return pd.DataFrame(res_d)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "if synthetic:\n", + " def oracle_estimator(X, y, num_cols, splits):\n", + " return (np.mean(moment_fn(X, true_f)),), None\n", + " res['true'], _ = generic_pipeline(oracle_estimator, q_grid, 'true', n_seeds=1)\n", + " res_d['true'] = transform_res_to_df(res['true'], q_grid)\n", + " display(res_d['true'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Nonparametric Model\n", + "\n", + "The first specification leaves the short regression $g_s(D, X^o)$ unrestricted and targets $\\theta_s = \\mathrm{E}\\{\\partial_d g_s(D, X^o)\\}$ directly. Rather than estimating the conditional density derivative in $\\alpha_s$, which can be unstable, we learn the Riesz representer from the Riesz loss. Model selection is used for both the regression and the representer. Candidate procedures include expanded-feature lasso, forests and local polynomial forests for the regression, and penalized linear Riesz regression or ForestRiesz for the representer. The target and score stay fixed while the approximation class changes." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# fully non-parametric average derivative estimation\n", + "def nonlinrr(X, y, num_cols, splits):\n", + " get_reisz_fn = rr_fn_gen(moment_fn, num_cols, cv=splits, verbose=verbose)\n", + " get_reg_fn = reg_fn_gen(num_cols, cv=splits, verbose=verbose)\n", + " est = DebiasedMoment(moment_fn=moment_fn,\n", + " get_reisz_fn=get_reisz_fn,\n", + " get_reg_fn=get_reg_fn,\n", + " n_splits=splits)\n", + " est.fit(X, y)\n", + " return est.avg_moment(alpha=.1), est" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res['rr'], ests['rr'] = generic_pipeline(nonlinrr, q_grid, 'dr')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " q=1 q=2 \\\n", + "dml -0.45 (0.37) [-1.06, 0.17] -0.85 (0.31) [-1.36, -0.33] \n", + "\n", + " q=3 q=4 \\\n", + "dml -0.78 (0.34) [-1.34, -0.22] -1.94 (0.54) [-2.83, -1.06] \n", + "\n", + " all \n", + "dml -0.84 (0.25) [-1.26, -0.43] " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_d['dml'] = transform_res_to_df(res['dml'], q_grid, alpha=.05)\n", + "res_d['dml']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensitivity Results for the Full Sample\n", + "\n", + "For the full sample we report the short elasticity, the sensitivity bounds and their confidence bounds under the scenario $C_Y^2 = 0.03$, $C_D^2 \\approx 0.03$ and $|\\rho| = 1$, together with robustness values." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "table = {}\n", + "table['point'] = '{:.3f} ({:.3f})'.format(res['rr'].loc[(f'q={5}', 'dr'), 'point'],\n", + " res['rr'].loc[(f'q={5}', 'dr'), 'stderr'])\n", + "\n", + "est_list = ests['rr'][f'q={5}']\n", + "table['Bounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=None))\n", + "table['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=.05))\n", + "table['RV(v=-1.5)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=None))\n", + "table['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=.05))\n", + "table['RV(v=0)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=None))\n", + "table['RV(v=0,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=.05))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "table2 = {}\n", + "table2['point'] = '{:.3f} ({:.3f})'.format(res['dml'].loc[(f'q={5}', 'dml'), 'point'],\n", + " res['dml'].loc[(f'q={5}', 'dml'), 'stderr'])\n", + "\n", + "est_list = ests['dml'][f'q={5}']\n", + "table2['Bounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=None))\n", + "table2['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=.05))\n", + "table2['RV(v=-1.5)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=None))\n", + "table2['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=.05))\n", + "table2['RV(v=0)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=None))\n", + "table2['RV(v=0,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=.05))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Non-parametric-0.573 (0.240)[-1.104, -0.043][-1.500, 0.350]0.0520.0300.0330.011
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" + ], + "text/plain": [ + " point Bounds CIBounds \\\n", + "Non-parametric -0.573 (0.240) [-1.104, -0.043] [-1.500, 0.350] \n", + "Partially linear -0.844 (0.251) [-1.299, -0.390] [-1.712, 0.022] \n", + "\n", + " RV(v=-1.5) RV(v=-1.5,a=.05) RV(v=0) RV(v=0,a=.05) \n", + "Non-parametric 0.052 0.030 0.033 0.011 \n", + "Partially linear 0.043 0.017 0.055 0.029 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tabledf = pd.DataFrame({'Non-parametric': table, 'Partially linear': table2}).T[['point', 'Bounds', 'CIBounds',\n", + " 'RV(v=-1.5)', 'RV(v=-1.5,a=.05)',\n", + " 'RV(v=0)', 'RV(v=0,a=.05)']]\n", + "tabledf" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "qgr = np.arange(1, 5)\n", + "\n", + "point = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] for q in qgr]\n", + "lower = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] - 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", + " for q in qgr]\n", + "upper = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] + 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", + " for q in qgr]\n", + "sens_band = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\n", + "sens_band_unc = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n", + "\n", + "if synthetic:\n", + " true_moment = [res['true'].loc[(f'q={q}', 'true'), 'point'] for q in qgr]\n", + "\n", + "plt.figure(figsize=(5, 5))\n", + "plt.plot(qgr, point)\n", + "plt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\n", + "plt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\n", + "plt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\n", + "if synthetic:\n", + " plt.plot(qgr, true_moment, label='true')\n", + "plt.xticks(qgr)\n", + "plt.title('Non-parametric')\n", + "plt.legend()\n", + "plt.grid(color = \"0.85\", linewidth = 0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "qgr = np.arange(1, 5)\n", + "\n", + "point = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] for q in qgr]\n", + "lower = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] - 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", + " for q in qgr]\n", + "upper = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] + 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", + " for q in qgr]\n", + "sens_band = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\n", + "sens_band_unc = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n", + "\n", + "plt.figure(figsize=(5, 5))\n", + "plt.plot(qgr, point)\n", + "plt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\n", + "plt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\n", + "plt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\n", + "if synthetic:\n", + " plt.plot(qgr, true_moment, label='true')\n", + "plt.xticks(qgr)\n", + "plt.title('Partially linear')\n", + "plt.legend()\n", + "plt.grid(color = \"0.85\", linewidth = 0.6)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "het (3.13.5.final.0)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/T-ADML/gasoline_example/rfriesz.py b/T-ADML/gasoline_example/rfriesz.py new file mode 100644 index 00000000..20eda14f --- /dev/null +++ b/T-ADML/gasoline_example/rfriesz.py @@ -0,0 +1,701 @@ +from econml.grf._base_grf import BaseGRF +from econml.utilities import cross_product +import numpy as np +import scipy.stats +import statsmodels.api as sm + +def mean_ci(data, confidence=0.95): + a = 1.0 * np.array(data) + n = len(a) + m, se = np.mean(a), scipy.stats.sem(a) + h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1) + return m, m-h, m+h + + +def poly_feature_fns(degree): + def poly(d, sign=1.0): + return lambda x: sign * x[:, [0]]**d + return [poly(t) for t in np.arange(0, degree + 1)] + + +class RFrr(BaseGRF): + """ + A forest that estimates a riesz representer of a linear functional, + using the linear moment equation: + + .. code-block:: + + E[ phi(T,X) phi(T,X)' alpha(x) - m(Z; phi) | X=x] = 0 + + Equivalently it can be viewed as minimizing the loss function: + + .. code-block:: + + E[ (phi(T,X)' alpha(x))^2 - 2 * | X=x] + + + Parameters + ---------- + riesz_feature_fns : list of lambda + A list of functions that each take as input the concatenation of [T;X] and return + either a scalar, representating the value of a feature phi(T,X), or a tuple + of a scalar, where the first entry is the feature and the second the gradient of the + feature with respect to its first argument. This will be used to approximate + the riesz representer function via a local sieve. + + moment_fn : lambda x, fn -> float + A moment function m([T;X], g) that takes as input the concatenation of [T;X] and a + function and return a linear moment. This is the moment whose average value we want + to estimate + + l2 : float, optional (default=0.01) + An l2 penalty added to the covariance matrix J(node)=E[phi(T,X) phi(T,X)' | X in node], + to be used for calculating the local parameter. Equivalent to using a modified jabocian of: + J(node) + lambda * I + + n_estimators : int, default=100 + Number of trees + + criterion : {``"mse"``, ``"het"``}, default="mse" + The function to measure the quality of a split. Supported criteria + are "mse" for the mean squared error in a linear moment estimation tree and "het" for + heterogeneity score. + + - The "mse" criterion finds splits that minimize the score: + + .. code-block:: + + sum_{child} E[ (phi(T,X)' alpha(x))^2 - 2 * | X=child] weight(child) + + sum_{child} E[ ( - Y)^2 | X=child] weight(child) + + Internally, this criterion is approximated by computationally simpler variants for + computational purposes. In particular, it is replaced by:: + + sum_{child} weight(child) * rho1(child).T @ E[phi(T,X) phi(T,X)' | X in child] @ rho1(child) + + sum_{child} weight(child) * rho2(child).T @ E[phi(T,X) phi(T,X)' | X in child] @ rho2(child) + + where: + + .. code-block:: + + rho1(child) := E[phi(T,X) phi(T,X)' | X in parent]^{-1} + * E[m(Z; phi) - phi(T,X) phi(T,X)'alpha(parent) | X in child] + rho2(child) := E[phi(T,X) phi(T,X)' | X in parent]^{-1} + * E[(Y - `min_var_fraction_leaf` * E[T[i]^2] + + When ``T`` is the residual treatment (i.e. centered), this translates to a requirement that + + .. code-block:: + + for all i in {1, ..., T.shape[1]}: + Var(T[i] | X in leaf) > `min_var_fraction_leaf` * Var(T[i]) + + - If ``criterion='mse'``, because the criterion stores more information about the leaf for + every candidate split, then this constraint imposes further constraints on the pairwise correlations + of different coordinates of each treatment, i.e.: + + .. code-block:: + + for all i neq j: + sqrt(Var(T[i]|X in leaf) * Var(T[j]|X in leaf) * (1 - rho(T[i], T[j]| in leaf)^2)) + > `min_var_fraction_leaf` sqrt(Var(T[i]) * Var(T[j]) * (1 - rho(T[i], T[j])^2)) + + where rho(X, Y) is the Pearson correlation coefficient of two random variables X, Y. Thus this + constraint also enforces that no two pairs of treatments be very co-linear within a leaf. This + extra constraint primarily has bite in the case of more than two input treatments and also avoids + leafs where the parameter estimate has large variance due to local co-linearities of the treatments. + + min_var_leaf_on_val : bool, default=False + Whether the `min_var_fraction_leaf` constraint should also be enforced to hold on the validation set of the + honest split too. If `min_var_leaf=None` then this flag does nothing. Setting this to True should + be done with caution, as this partially violates the honesty structure, since the treatment variable + of the validation set is used to inform the split structure of the tree. However, this is a benign + dependence as it only uses local correlation structure of the treatment T to decide whether + a split is feasible. + + max_features : int, float or {"auto", "sqrt", "log2"}, default=None + The number of features to consider when looking for the best split: + + - If int, then consider `max_features` features at each split. + - If float, then `max_features` is a fraction and + `int(max_features * n_features)` features are considered at each + split. + - If "auto", then `max_features=n_features`. + - If "sqrt", then `max_features=sqrt(n_features)`. + - If "log2", then `max_features=log2(n_features)`. + - If None, then `max_features=n_features`. + + Note: the search for a split does not stop until at least one + valid partition of the node samples is found, even if it requires to + effectively inspect more than ``max_features`` features. + + min_impurity_decrease : float, default=0.0 + A node will be split if this split induces a decrease of the impurity + greater than or equal to this value. + The weighted impurity decrease equation is the following:: + + N_t / N * (impurity - N_t_R / N_t * right_impurity + - N_t_L / N_t * left_impurity) + + where ``N`` is the total number of samples, ``N_t`` is the number of + samples at the current node, ``N_t_L`` is the number of samples in the + left child, and ``N_t_R`` is the number of samples in the right child. + ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, + if ``sample_weight`` is passed. + + max_samples : int or float in (0, 1], default=.45, + The number of samples to use for each subsample that is used to train each tree: + + - If int, then train each tree on `max_samples` samples, sampled without replacement from all the samples + - If float, then train each tree on ceil(`max_samples` * `n_samples`), sampled without replacement + from all the samples. + + If ``inference=True``, then `max_samples` must either be an integer smaller than `n_samples//2` or a float + less than or equal to .5. + + min_balancedness_tol: float in [0, .5], default=.45 + How imbalanced a split we can tolerate. This enforces that each split leaves at least + (.5 - min_balancedness_tol) fraction of samples on each side of the split; or fraction + of the total weight of samples, when sample_weight is not None. Default value, ensures + that at least 5% of the parent node weight falls in each side of the split. Set it to 0.0 for no + balancedness and to .5 for perfectly balanced splits. For the formal inference theory + to be valid, this has to be any positive constant bounded away from zero. + + honest : bool, default=True + Whether each tree should be trained in an honest manner, i.e. the training set is split into two equal + sized subsets, the train and the val set. All samples in train are used to create the split structure + and all samples in val are used to calculate the value of each node in the tree. + + inference : bool, default=True + Whether inference (i.e. confidence interval construction and uncertainty quantification of the estimates) + should be enabled. If `inference=True`, then the estimator uses a bootstrap-of-little-bags approach + to calculate the covariance of the parameter vector, with am objective Bayesian debiasing correction + to ensure that variance quantities are positive. + + subforest_size : int, default=4, + The number of trees in each sub-forest that is used in the bootstrap-of-little-bags calculation. + The parameter `n_estimators` must be divisible by `subforest_size`. Should typically be a small constant. + + n_jobs : int or None, default=-1 + The number of parallel jobs to be used for parallelism; follows joblib semantics. + ``n_jobs=-1`` means all available cpu cores. ``n_jobs=None`` means no parallelism. + + random_state : int, RandomState instance or None, default=None + Controls the randomness of the estimator. The features are always + randomly permuted at each split. When ``max_features < n_features``, the algorithm will + select ``max_features`` at random at each split before finding the best + split among them. But the best found split may vary across different + runs, even if ``max_features=n_features``. That is the case, if the + improvement of the criterion is identical for several splits and one + split has to be selected at random. To obtain a deterministic behaviour + during fitting, ``random_state`` has to be fixed to an integer. + + verbose : int, default=0 + Controls the verbosity when fitting and predicting. + + warm_start : bool, default=``False`` + When set to ``True``, reuse the solution of the previous call to fit + and add more estimators to the ensemble, otherwise, just fit a whole + new forest. If ``True``, then `oob_predict` method for out-of-bag predictions is not available. + + Attributes + ---------- + feature_importances_ : ndarray of shape (n_features,) + The feature importances based on the amount of parameter heterogeneity they create. + The higher, the more important the feature. + The importance of a feature is computed as the (normalized) total heterogeneity that the feature + creates. Each split that the feature was chosen adds:: + + parent_weight * (left_weight * right_weight) + * mean((value_left[k] - value_right[k])**2) / parent_weight**2 + + to the importance of the feature. Each such quantity is also weighted by the depth of the split. + By default splits below ``max_depth=4`` are not used in this calculation and also each split + at depth `depth`, is re-weighted by ``1 / (1 + `depth`)**2.0``. See the method ``feature_importances`` + for a method that allows one to change these defaults. + + estimators_ : list of objects of type :class:`~econml.grf.GRFTree` + The fitted trees. + """ + + def __init__(self, *, + riesz_feature_fns, + moment_fn, + l2=0.01, + n_estimators=100, + criterion="mse", + max_depth=None, + min_samples_split=10, + min_samples_leaf=5, + min_weight_fraction_leaf=0., + min_var_fraction_leaf=None, + min_var_leaf_on_val=False, + max_features="auto", + min_impurity_decrease=0., + max_samples=.45, + min_balancedness_tol=.45, + honest=True, + inference=True, + fit_intercept=True, + subforest_size=4, + n_jobs=-1, + random_state=None, + verbose=0, + warm_start=False): + self.riesz_feature_fns = riesz_feature_fns + self.moment_fn = moment_fn + self.l2 = l2 + super().__init__(n_estimators=n_estimators, + criterion=criterion, + max_depth=max_depth, + min_samples_split=min_samples_split, + min_samples_leaf=min_samples_leaf, + min_weight_fraction_leaf=min_weight_fraction_leaf, + min_var_fraction_leaf=min_var_fraction_leaf, + min_var_leaf_on_val=min_var_leaf_on_val, + max_features=max_features, + min_impurity_decrease=min_impurity_decrease, + max_samples=max_samples, + min_balancedness_tol=min_balancedness_tol, + honest=honest, + inference=inference, + fit_intercept=False, + subforest_size=subforest_size, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start) + + def _get_alpha_and_pointJ(self, X, T, y): + n_riesz_feats = len(self.riesz_feature_fns) + TX = np.hstack([T, X]) + riesz_feats = np.hstack([feat_fn(TX) + for feat_fn in self.riesz_feature_fns]) + mfeats = np.hstack([self.moment_fn(TX, feat_fn) + for feat_fn in self.riesz_feature_fns]) + alpha = np.zeros((X.shape[0], n_riesz_feats)) + alpha[:, :n_riesz_feats] = mfeats + riesz_cov_matrix = cross_product(riesz_feats, riesz_feats).reshape( + (X.shape[0], n_riesz_feats, n_riesz_feats)) + penalty = self.l2 * np.eye(n_riesz_feats) + penalty[0, 0] = 0 + pointJ = riesz_cov_matrix + penalty + return alpha, pointJ.reshape((X.shape[0], -1)) + + def _get_n_outputs_decomposition(self, X, T, y): + n_relevant_outputs = len(self.riesz_feature_fns) + n_outputs = n_relevant_outputs + return n_outputs, n_relevant_outputs + + def _translate(self, point, TX): + riesz_feats = np.hstack([feat_fn(TX) + for feat_fn in self.riesz_feature_fns]) + n_riesz_feats = riesz_feats.shape[1] + riesz = np.sum(point[:, :n_riesz_feats] * riesz_feats, axis=1) + return riesz + + def fit(self, X): + return super().fit(X[:, 1:], X[:, [0]], X[:, [0]]) + + def predict(self, X_test): + point = super().predict(X_test[:, 1:]) + return self._translate(point, X_test) + + + +class RFreg(BaseGRF): + """ + A forest that estimates a regression function with pre-specified features, + using the linear moment equations: + + .. code-block:: + + E[ ( - Y) phi(T, X) | X=x] = 0 + + Equivalently it can be viewed as minimizing jointly the loss functions: + + .. code-block:: + + E[ ( - Y)^2 | X=x] + + + Parameters + ---------- + reg_feature_fns : list of lambda + A list of functions that each take as input the concatenation of [T;X] and return + either a scalar, representating the value of a feature phi(T,X), or a tuple + of a scalar, where the first entry is the feature and the second the gradient of the + feature with respect to its first argument. This will be used to approximate + the regression function via a local sieve. + + l2 : float, optional (default=0.01) + An l2 penalty added to the covariance matrix J(node)=E[phi(T,X) phi(T,X)' | X in node], + to be used for calculating the local parameter. Equivalent to using a modified jabocian of: + J(node) + lambda * I + + n_estimators : int, default=100 + Number of trees + + criterion : {``"mse"``, ``"het"``}, default="mse" + The function to measure the quality of a split. Supported criteria + are "mse" for the mean squared error in a linear moment estimation tree and "het" for + heterogeneity score. + + - The "mse" criterion finds splits that minimize the score: + + .. code-block:: + + sum_{child} E[ (phi(T,X)' alpha(x))^2 - 2 * | X=child] weight(child) + + sum_{child} E[ ( - Y)^2 | X=child] weight(child) + + Internally, this criterion is approximated by computationally simpler variants for + computational purposes. In particular, it is replaced by:: + + sum_{child} weight(child) * rho1(child).T @ E[phi(T,X) phi(T,X)' | X in child] @ rho1(child) + + sum_{child} weight(child) * rho2(child).T @ E[phi(T,X) phi(T,X)' | X in child] @ rho2(child) + + where: + + .. code-block:: + + rho1(child) := E[phi(T,X) phi(T,X)' | X in parent]^{-1} + * E[m(Z; phi) - phi(T,X) phi(T,X)'alpha(parent) | X in child] + rho2(child) := E[phi(T,X) phi(T,X)' | X in parent]^{-1} + * E[(Y - `min_var_fraction_leaf` * E[T[i]^2] + + When ``T`` is the residual treatment (i.e. centered), this translates to a requirement that + + .. code-block:: + + for all i in {1, ..., T.shape[1]}: + Var(T[i] | X in leaf) > `min_var_fraction_leaf` * Var(T[i]) + + - If ``criterion='mse'``, because the criterion stores more information about the leaf for + every candidate split, then this constraint imposes further constraints on the pairwise correlations + of different coordinates of each treatment, i.e.: + + .. code-block:: + + for all i neq j: + sqrt(Var(T[i]|X in leaf) * Var(T[j]|X in leaf) * (1 - rho(T[i], T[j]| in leaf)^2)) + > `min_var_fraction_leaf` sqrt(Var(T[i]) * Var(T[j]) * (1 - rho(T[i], T[j])^2)) + + where rho(X, Y) is the Pearson correlation coefficient of two random variables X, Y. Thus this + constraint also enforces that no two pairs of treatments be very co-linear within a leaf. This + extra constraint primarily has bite in the case of more than two input treatments and also avoids + leafs where the parameter estimate has large variance due to local co-linearities of the treatments. + + min_var_leaf_on_val : bool, default=False + Whether the `min_var_fraction_leaf` constraint should also be enforced to hold on the validation set of the + honest split too. If `min_var_leaf=None` then this flag does nothing. Setting this to True should + be done with caution, as this partially violates the honesty structure, since the treatment variable + of the validation set is used to inform the split structure of the tree. However, this is a benign + dependence as it only uses local correlation structure of the treatment T to decide whether + a split is feasible. + + max_features : int, float or {"auto", "sqrt", "log2"}, default=None + The number of features to consider when looking for the best split: + + - If int, then consider `max_features` features at each split. + - If float, then `max_features` is a fraction and + `int(max_features * n_features)` features are considered at each + split. + - If "auto", then `max_features=n_features`. + - If "sqrt", then `max_features=sqrt(n_features)`. + - If "log2", then `max_features=log2(n_features)`. + - If None, then `max_features=n_features`. + + Note: the search for a split does not stop until at least one + valid partition of the node samples is found, even if it requires to + effectively inspect more than ``max_features`` features. + + min_impurity_decrease : float, default=0.0 + A node will be split if this split induces a decrease of the impurity + greater than or equal to this value. + The weighted impurity decrease equation is the following:: + + N_t / N * (impurity - N_t_R / N_t * right_impurity + - N_t_L / N_t * left_impurity) + + where ``N`` is the total number of samples, ``N_t`` is the number of + samples at the current node, ``N_t_L`` is the number of samples in the + left child, and ``N_t_R`` is the number of samples in the right child. + ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, + if ``sample_weight`` is passed. + + max_samples : int or float in (0, 1], default=.45, + The number of samples to use for each subsample that is used to train each tree: + + - If int, then train each tree on `max_samples` samples, sampled without replacement from all the samples + - If float, then train each tree on ceil(`max_samples` * `n_samples`), sampled without replacement + from all the samples. + + If ``inference=True``, then `max_samples` must either be an integer smaller than `n_samples//2` or a float + less than or equal to .5. + + min_balancedness_tol: float in [0, .5], default=.45 + How imbalanced a split we can tolerate. This enforces that each split leaves at least + (.5 - min_balancedness_tol) fraction of samples on each side of the split; or fraction + of the total weight of samples, when sample_weight is not None. Default value, ensures + that at least 5% of the parent node weight falls in each side of the split. Set it to 0.0 for no + balancedness and to .5 for perfectly balanced splits. For the formal inference theory + to be valid, this has to be any positive constant bounded away from zero. + + honest : bool, default=True + Whether each tree should be trained in an honest manner, i.e. the training set is split into two equal + sized subsets, the train and the val set. All samples in train are used to create the split structure + and all samples in val are used to calculate the value of each node in the tree. + + inference : bool, default=True + Whether inference (i.e. confidence interval construction and uncertainty quantification of the estimates) + should be enabled. If `inference=True`, then the estimator uses a bootstrap-of-little-bags approach + to calculate the covariance of the parameter vector, with am objective Bayesian debiasing correction + to ensure that variance quantities are positive. + + subforest_size : int, default=4, + The number of trees in each sub-forest that is used in the bootstrap-of-little-bags calculation. + The parameter `n_estimators` must be divisible by `subforest_size`. Should typically be a small constant. + + n_jobs : int or None, default=-1 + The number of parallel jobs to be used for parallelism; follows joblib semantics. + ``n_jobs=-1`` means all available cpu cores. ``n_jobs=None`` means no parallelism. + + random_state : int, RandomState instance or None, default=None + Controls the randomness of the estimator. The features are always + randomly permuted at each split. When ``max_features < n_features``, the algorithm will + select ``max_features`` at random at each split before finding the best + split among them. But the best found split may vary across different + runs, even if ``max_features=n_features``. That is the case, if the + improvement of the criterion is identical for several splits and one + split has to be selected at random. To obtain a deterministic behaviour + during fitting, ``random_state`` has to be fixed to an integer. + + verbose : int, default=0 + Controls the verbosity when fitting and predicting. + + warm_start : bool, default=``False`` + When set to ``True``, reuse the solution of the previous call to fit + and add more estimators to the ensemble, otherwise, just fit a whole + new forest. If ``True``, then `oob_predict` method for out-of-bag predictions is not available. + + Attributes + ---------- + feature_importances_ : ndarray of shape (n_features,) + The feature importances based on the amount of parameter heterogeneity they create. + The higher, the more important the feature. + The importance of a feature is computed as the (normalized) total heterogeneity that the feature + creates. Each split that the feature was chosen adds:: + + parent_weight * (left_weight * right_weight) + * mean((value_left[k] - value_right[k])**2) / parent_weight**2 + + to the importance of the feature. Each such quantity is also weighted by the depth of the split. + By default splits below ``max_depth=4`` are not used in this calculation and also each split + at depth `depth`, is re-weighted by ``1 / (1 + `depth`)**2.0``. See the method ``feature_importances`` + for a method that allows one to change these defaults. + + estimators_ : list of objects of type :class:`~econml.grf.GRFTree` + The fitted trees. + """ + + def __init__(self, *, + reg_feature_fns, + l2=0.01, + n_estimators=100, + criterion="mse", + max_depth=None, + min_samples_split=10, + min_samples_leaf=5, + min_weight_fraction_leaf=0., + min_var_fraction_leaf=None, + min_var_leaf_on_val=False, + max_features="auto", + min_impurity_decrease=0., + max_samples=.45, + min_balancedness_tol=.45, + honest=True, + inference=True, + fit_intercept=True, + subforest_size=4, + n_jobs=-1, + random_state=None, + verbose=0, + warm_start=False): + self.reg_feature_fns = reg_feature_fns + self.l2 = l2 + super().__init__(n_estimators=n_estimators, + criterion=criterion, + max_depth=max_depth, + min_samples_split=min_samples_split, + min_samples_leaf=min_samples_leaf, + min_weight_fraction_leaf=min_weight_fraction_leaf, + min_var_fraction_leaf=min_var_fraction_leaf, + min_var_leaf_on_val=min_var_leaf_on_val, + max_features=max_features, + min_impurity_decrease=min_impurity_decrease, + max_samples=max_samples, + min_balancedness_tol=min_balancedness_tol, + honest=honest, + inference=inference, + fit_intercept=False, + subforest_size=subforest_size, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start) + + def fit(self, X, y): + return super().fit(X[:, 1:], X[:, [0]], y) + + def _get_alpha_and_pointJ(self, X, T, y): + n_reg_feats = len(self.reg_feature_fns) + n_feats = n_reg_feats + TX = np.hstack([T, X]) + reg_feats = np.hstack([feat_fn(TX) + for feat_fn in self.reg_feature_fns]) + alpha = y.reshape(-1, 1) * reg_feats + reg_cov_matrix = cross_product(reg_feats, reg_feats).reshape( + (X.shape[0], n_reg_feats, n_reg_feats)) + penalty = self.l2 * np.eye(n_reg_feats) + penalty[0, 0] = 0 + pointJ = reg_cov_matrix + penalty + return alpha, pointJ.reshape((X.shape[0], -1)) + + def _get_n_outputs_decomposition(self, X, T, y): + n_relevant_outputs = len(self.reg_feature_fns) + n_outputs = n_relevant_outputs + return n_outputs, n_relevant_outputs + + def _translate(self, point, TX): + reg_feats = np.hstack([feat_fn(TX) + for feat_fn in self.reg_feature_fns]) + reg = np.sum(point * reg_feats, axis=1) + return reg + + def predict(self, X_test): + point = super().predict(X_test[:, 1:]) + return self._translate(point, X_test) diff --git a/T-ADML/gasoline_example/sensitivity.py b/T-ADML/gasoline_example/sensitivity.py new file mode 100644 index 00000000..90e2eda2 --- /dev/null +++ b/T-ADML/gasoline_example/sensitivity.py @@ -0,0 +1,185 @@ +import numpy as np +import scipy + +def moment_estimate(est, inds=None): + if inds is None: + inds = np.arange(est.moment_reisz_.shape[0]) + theta = np.mean(est.moment_[inds]) + psi_theta = est.moment_[inds] - theta + return theta, np.sqrt(np.mean(psi_theta**2) / psi_theta.shape[0]) + +def sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None): + ''' Sensitivity bound based on a single trained doubly robust + average moment model and for some sensitivity parameters `eta_ysq` + and `eta_asq`. If `alpha` is float, incorporates sampling uncertainty + at the `alpha` level. If `inds` is specified, then only samples in `inds` + are used for the estimation and reporting. Useful for sub-group analysis. + ''' + if inds is None: + inds = np.arange(est.moment_reisz_.shape[0]) + nusq = np.mean(2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2) + sigmasq = np.mean((est.y_[inds] - est.reg_pred_[inds])**2) + S = np.sqrt(sigmasq * nusq) + + Casq = eta_asq / (1 - eta_asq) + Cgsq = eta_ysq + theta = np.mean(est.moment_[inds]) + error = S * np.sqrt(Casq * Cgsq) + + if alpha is None: + return theta - error, theta + error + + psi_theta = est.moment_[inds] - theta + psi_sigmasq = (est.y_[inds] - est.reg_pred_[inds])**2 - sigmasq + psi_nusq = 2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2 - nusq + + phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq) + stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0]) + + phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq) + stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0]) + return theta - error, stderr_minus, theta + error, stderr_plus + +def sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None): + ''' Sensitivity bound based on a many trained doubly robust + average moment models and for some sensitivity parameters `eta_ysq` + and `eta_asq`. If `alpha` is float, incorporates sampling uncertainty + at the `alpha` level. If `inds` is specified, then only samples in `inds` + are used for the estimation and reporting. Useful for sub-group analysis. + ''' + if alpha is None: + lower, upper = zip(*[sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds) for est in est_list]) + return np.mean(lower), np.mean(upper) + else: + lower, std_lower, upper, std_upper = zip(*[sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds) + for est in est_list]) + std_lower = np.array(std_lower) + std_upper = np.array(std_upper) + lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower)) + upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper)) + return lower, upper + +def tvalue(est_list, value=0, leq=None, alpha=None, inds=None): + ''' Robustness value based on a many trained doubly robust + average moment models and for some target `value`. If `alpha` is + float, incorporates sampling uncertainty at the `alpha` level. + If `leq=True`, then checks that the bounds interval overlaps with the + `[-infty, value]` interval, and if `leq=False`, checks if it overlaps + with the `[value, infty]` interval. Otherwise, if `leq=None` it checks + if the bounds interval contains `value`. If `inds` is specified, then + only samples in `inds` are used for the estimation and reporting. + Useful for sub-group analysis. + ''' + for t in np.linspace(0, 1, 1000): + l, u = sensitivity_bounds(est_list, t, t, alpha=alpha, inds=inds) + if (leq is None) & (value >= l) & (value <= u): + break + if (leq == True) & (value >= l): + break + if (leq == False) & (value <= u): + break + return t + +def sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None): + ''' Sensitivity bounds contour plots based on a many trained doubly robust + average moment models. If `alpha` is float, incorporates sampling uncertainty + at the `alpha` level. Sensitivity parameter `eta_ysq` ranges in `[0, y_upper]` + and parameter `eta_asq` ranges in `[0, a_upper]`. + ''' + xlist = np.linspace(0, a_upper, 100) + ylist = np.linspace(0, y_upper, 100) + X, Y = np.meshgrid(xlist, ylist) + Zlower = np.zeros(X.shape) + Zupper = np.zeros(X.shape) + for itx in np.arange(X.shape[1]): + for ity in np.arange(X.shape[0]): + l, u = sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds) + Zlower[ity, itx] = l + Zupper[ity, itx] = u + + return X, Y, Zlower, Zupper + +def dml_sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None): + ''' Specialized for linear DML. Sensitivity bounds contour plots based on a many trained doubly robust + average moment models. If `alpha` is float, incorporates sampling uncertainty + at the `alpha` level. Sensitivity parameter `eta_ysq` ranges in `[0, y_upper]` + and parameter `eta_asq` ranges in `[0, a_upper]`. + ''' + xlist = np.linspace(0, a_upper, 100) + ylist = np.linspace(0, y_upper, 100) + X, Y = np.meshgrid(xlist, ylist) + Zlower = np.zeros(X.shape) + Zupper = np.zeros(X.shape) + for itx in np.arange(X.shape[1]): + for ity in np.arange(X.shape[0]): + l, u = dml_sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds) + Zlower[ity, itx] = l + Zupper[ity, itx] = u + + return X, Y, Zlower, Zupper + +def dml_estimate(est, inds=None): + if inds is None: + inds = np.arange(est.residuals_[0].shape[0]) + yres, Tres, _, _ = est.residuals_ + yres, Tres = yres[inds], Tres[inds] + nusq = np.mean(Tres ** 2) + theta = np.mean(yres * Tres) / nusq + psi_theta = (yres - Tres * theta) * Tres / nusq + return theta, np.sqrt(np.mean(psi_theta**2)/psi_theta.shape[0]) + +def dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None): + ''' Sensitivity analysis, specialized for the partially linear DML moment + E[(yres - theta * Tres) * Tres]. `est` is a `LinearDML` estimator fitted + with `cache_values=True` so that residuals are being stored after fitting. + ''' + if inds is None: + inds = np.arange(est.residuals_[0].shape[0]) + yres, Tres, _, _ = est.residuals_ + yres, Tres = yres[inds], Tres[inds] + nusq = np.mean(Tres ** 2) + theta = np.mean(yres * Tres) / nusq + sigmasq = np.mean((yres - Tres * theta)**2) + S = np.sqrt(sigmasq / nusq) + Casq = eta_asq / (1 - eta_asq) + Cgsq = eta_ysq + error = S * np.sqrt(Casq * Cgsq) + + if alpha is None: + return theta - error, theta + error + + psi_theta = (yres - Tres * theta) * Tres / nusq + psi_sigmasq = (yres - Tres * theta)**2 - sigmasq + psi_nusq = Tres**2 - nusq + + phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (-(sigmasq/(nusq**2)) * psi_nusq + (1/nusq) * psi_sigmasq) + stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0]) + + phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (-(sigmasq/(nusq**2)) * psi_nusq + (1/nusq) * psi_sigmasq) + stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0]) + return theta - error, stderr_minus, theta + error, stderr_plus + +def dml_sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None): + if alpha is None: + lower, upper = zip(*[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds) + for est in est_list]) + return np.mean(lower), np.mean(upper) + else: + lower, std_lower, upper, std_upper = zip(*[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds) + for est in est_list]) + std_lower = np.array(std_lower) + std_upper = np.array(std_upper) + lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower)) + upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper)) + return lower, upper + +def dml_tvalue(est, value=0, leq=None, alpha=None, inds=None): + for t in np.linspace(0, 1, 1000): + l, u = dml_sensitivity_bounds(est, t, t, alpha=alpha, inds=inds) + if (leq is None) & (value >= l) & (value <= u): + break + if (leq == True) & (value >= l): + break + if (leq == False) & (value <= u): + break + return t diff --git a/T-ADML/gasoline_example/sievereisz.py b/T-ADML/gasoline_example/sievereisz.py new file mode 100644 index 00000000..1b706565 --- /dev/null +++ b/T-ADML/gasoline_example/sievereisz.py @@ -0,0 +1,124 @@ +from sklearn.base import BaseEstimator +import numpy as np +from sklearn.base import TransformerMixin, BaseEstimator, clone +from econml.utilities import cross_product +from sklearn.preprocessing import PolynomialFeatures, StandardScaler +import scipy +from scipy.optimize import fmin_l_bfgs_b + +class Featurizer(TransformerMixin, BaseEstimator): + ''' Creates features of the form X[:, 1:] * poly(X[:, 0]) + i.e. some polynomial of the treatment, multiplied by some other feature. + ''' + + def __init__(self, *, degree=1, normalize=True): + self.degree = degree + self.normalize = normalize + + def _transform(self, X): + Tfeats = PolynomialFeatures(degree=self.degree, include_bias=True).fit_transform(X[:, [0]]) + feats = cross_product(Tfeats, X[:, 1:]) + return feats + + def fit(self, X, y=None): + if self.normalize: + self.scaler_ = StandardScaler().fit(self._transform(X)) + return self + + def transform(self, X): + if self.normalize: + return self.scaler_.transform(self._transform(X)) + return self._transform(X) + +class SieveRR(BaseEstimator): + ''' Linear sieve representation learning with some feature map and + l2 penalty. + ''' + + def __init__(self, *, featurizer, moment_fn, alpha=0): + self.alpha = alpha + self.featurizer = featurizer + self.moment_fn = moment_fn + + def fit(self, X): + self.featurizer_ = clone(self.featurizer).fit(X) + n_samples = X.shape[0] + Xfeats = self.featurizer_.transform(X) + Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats]) + n_feats = Xfeats.shape[1] + Mfeats = np.zeros((n_samples, n_feats)) + for t in np.arange(n_feats): + if t == 0: + feat_fn = lambda x: np.ones(x.shape[0]) + else: + feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1] + Mfeats[:, t] = self.moment_fn(X, feat_fn) + Sigma = Xfeats.T @ Xfeats / n_samples + reg = self.alpha * np.eye(n_feats) + reg[0, 0] = 0 + Sigma += reg + params = np.linalg.pinv(Sigma) @ np.mean(Mfeats, axis=0) + self.coef_ = params[1:] + self.intercept_ = params[0] + + return self + + def predict(self, X): + return self.featurizer_.transform(X) @ self.coef_ + self.intercept_ + +class SparseSieveRR(BaseEstimator): + ''' Linear sieve representation learning with some feature map and + combination of l1 and l2 penalty. + ''' + + def __init__(self, *, featurizer, moment_fn, alpha=0.0, l1_ratio=1.0, tol=1e-4): + self.featurizer = featurizer + self.moment_fn = moment_fn + self.alpha = alpha + self.l1_ratio = l1_ratio + self.tol = tol + + def fit(self, X): + self.featurizer_ = clone(self.featurizer).fit(X) + n_samples = X.shape[0] + Xfeats = self.featurizer_.transform(X) + Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats]) + n_feats = Xfeats.shape[1] + Mfeats = np.zeros((n_samples, n_feats)) + for t in np.arange(n_feats): + if t == 0: + feat_fn = lambda x: np.ones(x.shape[0]) + else: + feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1] + Mfeats[:, t] = self.moment_fn(X, feat_fn) + Sigma = Xfeats.T @ Xfeats / n_samples + mfeats = np.mean(Mfeats, axis=0) + + alpha_l1 = self.alpha * self.l1_ratio + alpha_l2 = self.alpha * (1 - self.l1_ratio) + def loss_and_jac(extended_coef): + coef = extended_coef[:n_feats] - extended_coef[n_feats:] + loss = - 2 * mfeats @ coef + coef.T @ Sigma @ coef + loss += alpha_l1 * np.sum(extended_coef[1:n_feats]) + loss += alpha_l1 * np.sum(extended_coef[n_feats + 1:]) + loss += 0.5 * alpha_l2 * np.sum(extended_coef[1:n_feats]**2) + loss += 0.5 * alpha_l2 * np.sum(extended_coef[n_feats + 1:]**2) + grad = 2 * (Sigma @ coef - mfeats) + jac = np.concatenate([grad, -grad]) + jac[1:n_feats] += alpha_l1 + alpha_l2 * extended_coef[1:n_feats] + jac[n_feats + 1:] += alpha_l1 + alpha_l2 * extended_coef[n_feats + 1:] + return loss, jac + + w, _, _ = fmin_l_bfgs_b(loss_and_jac, np.zeros(2*n_feats), + bounds=[(0, None)] * 2 * n_feats, + pgtol=self.tol) + + params = w[:n_feats] - w[n_feats:] + self.coef_ = params[1:] + self.intercept_ = params[0] + + + return self + + def predict(self, X): + return self.featurizer_.transform(X) @ self.coef_ + self.intercept_ \ No newline at end of file diff --git a/T-ADML/gasoline_example/utilities.py b/T-ADML/gasoline_example/utilities.py new file mode 100644 index 00000000..988b303c --- /dev/null +++ b/T-ADML/gasoline_example/utilities.py @@ -0,0 +1,222 @@ +import numpy as np +from sklearn.base import BaseEstimator, clone + +import warnings +from functools import partial + +import numpy as np +import scipy +import scipy.sparse as sp +from scipy.linalg import svd +try: + from scipy.fft import fft, ifft +except ImportError: # scipy < 1.4 + from scipy.fftpack import fft, ifft + +from sklearn.base import BaseEstimator +from sklearn.base import TransformerMixin +from sklearn.utils import check_random_state, as_float_array +from sklearn.utils.extmath import safe_sparse_dot +from sklearn.utils.validation import check_is_fitted +from sklearn.metrics.pairwise import pairwise_kernels, KERNEL_PARAMS, PAIRWISE_KERNEL_FUNCTIONS, _parallel_pairwise, _pairwise_callable +from sklearn.metrics.pairwise import rbf_kernel, cosine_similarity, pairwise_kernels + + +def rmse(x, y): + return np.sqrt(np.mean((x - y)**2)) + +def mean_ci(data, confidence=0.95): + a = 1.0 * np.array(data).flatten() + n = len(a) + m, se = np.mean(a), scipy.stats.sem(a) + h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1) + return m, se, m-h, m+h + + +def binary_kernel(X, Y=None): + return 1.0 * (X[:, [0]] == X[:, [0]].T) if Y is None else 1.0 * (X[:, [0]] == Y[:, [0]].T) + + +def binary_or_rbf_kernel(X, Y=None, *, gamma): + return binary_kernel(X, Y=Y) if len(np.unique(X)) == 2 else rbf_kernel(X, Y=Y, gamma=gamma) + + +def prod_kernel(X, Y=None, *, gamma): + + if hasattr(gamma, '__len__'): + X = X.copy() + X[:, 1:] = X[:, 1:] * np.sqrt(gamma).reshape(1, -1) + if Y is not None: + Y = Y.copy() + Y[:, 1:] = Y[:, 1:] * np.sqrt(gamma).reshape(1, -1) + gamma = 1 + + if Y is None: + # return np.product([binary_or_rbf_kernel(X[:, [t]], gamma=gamma) for t in np.arange(1, X.shape[1])], axis=0) * binary_kernel(X, Y) + return rbf_kernel(X[:, 1:], gamma=gamma) * binary_kernel(X, Y) + else: + # return np.product([binary_or_rbf_kernel(X[:, [t]], Y=Y[:, [t]], gamma=gamma) for t in np.arange(1, X.shape[1])], axis=0) * binary_kernel(X, Y) + return rbf_kernel(X[:, 1:], Y=Y[:, 1:], gamma=gamma) * binary_kernel(X, Y) + + +class AutoKernel: + + def __init__(self, *, type='var'): + self.type = type + + def fit(self, X): + if self.type == 'var': + self.gamma_ = 1/ ((X.shape[1] - 1) * np.var(X[:, 1:], axis=0)) + if self.type == 'median': + self.gamma_ = np.array([1 / ((X.shape[1] - 1) * np.median(np.abs(X[:, [i]] - X[:, [i]].T))**2) + for i in np.arange(1, X.shape[1])]) + self.kernel_ = lambda X, Y=None: prod_kernel(X, Y=Y, gamma=self.gamma_) + return self + +class FitParamsWrapper: + + def __init__(self, model, **fit_params): + self.model = model + self.fit_params = fit_params + + def fit(self, X): + return self.model.fit(X, **self.fit_params) + + def predict(self, X): + return self.model.predict(X) + +class PluginRR(BaseEstimator): + + def __init__(self, *, model_t, min_propensity=0): + self.model_t = clone(model_t, safe=False) + self.min_propensity = min_propensity + + def fit(self, X): + self.model_t_ = clone(self.model_t, safe=False).fit(X[:, 1:], X[:, 0]) + return self + + def predict(self, X): + propensity = np.clip(self.model_t_.predict_proba(X[:, 1:]), self.min_propensity, 1 - self.min_propensity) + return X[:, 0] / propensity[:, 1] - (1 - X[:, 0]) / propensity[:, 0] + + + +def pairwise_kernels(X, Y=None, metric="linear", *, filter_params=False, + n_jobs=None, **kwds): + if metric in PAIRWISE_KERNEL_FUNCTIONS: + if filter_params: + kwds = {k: kwds[k] for k in kwds + if k in KERNEL_PARAMS[metric]} + func = PAIRWISE_KERNEL_FUNCTIONS[metric] + elif callable(metric): + func = metric + else: + raise ValueError("Unknown kernel %r" % metric) + + return _parallel_pairwise(X, Y, func, n_jobs, **kwds) + +class Nystroem(TransformerMixin, BaseEstimator): + + def __init__(self, kernel="rbf", *, gamma=None, coef0=None, degree=None, + kernel_params=None, n_components=100, random_state=None, + n_jobs=None): + + self.kernel = kernel + self.gamma = gamma + self.coef0 = coef0 + self.degree = degree + self.kernel_params = kernel_params + self.n_components = n_components + self.random_state = random_state + self.n_jobs = n_jobs + + def fit(self, X, y=None): + """Fit estimator to data. + Samples a subset of training points, computes kernel + on these and computes normalization matrix. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + """ + X = self._validate_data(X, accept_sparse='csr') + rnd = check_random_state(self.random_state) + n_samples = X.shape[0] + + # get basis vectors + if self.n_components > n_samples: + # XXX should we just bail? + n_components = n_samples + warnings.warn("n_components > n_samples. This is not possible.\n" + "n_components was set to n_samples, which results" + " in inefficient evaluation of the full kernel.") + + else: + n_components = self.n_components + n_components = min(n_samples, n_components) + inds = rnd.permutation(n_samples) + basis_inds = inds[:n_components] + basis = X[basis_inds] + + basis_kernel = pairwise_kernels(basis, metric=self.kernel, + filter_params=True, + n_jobs=self.n_jobs, + **self._get_kernel_params()) + # sqrt of kernel matrix on basis vectors + U, S, V = svd(basis_kernel) + S = np.maximum(S, 1e-12) + self.normalization_ = np.dot(U / np.sqrt(S), V) + self.components_ = basis + self.component_indices_ = inds + return self + + def transform(self, X): + """Apply feature map to X. + Computes an approximate feature map using the kernel + between some training points and X. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to transform. + Returns + ------- + X_transformed : ndarray of shape (n_samples, n_components) + Transformed data. + """ + check_is_fitted(self) + X = self._validate_data(X, accept_sparse='csr', reset=False) + + kernel_params = self._get_kernel_params() + embedded = pairwise_kernels(X, self.components_, + metric=self.kernel, + filter_params=True, + n_jobs=self.n_jobs, + **kernel_params) + return np.dot(embedded, self.normalization_.T) + + def _get_kernel_params(self): + params = self.kernel_params + if params is None: + params = {} + if not callable(self.kernel) and self.kernel != 'precomputed': + for param in (KERNEL_PARAMS[self.kernel]): + if getattr(self, param) is not None: + params[param] = getattr(self, param) + else: + if (self.gamma is not None or + self.coef0 is not None or + self.degree is not None): + raise ValueError("Don't pass gamma, coef0 or degree to " + "Nystroem if using a callable " + "or precomputed kernel") + + return params + + def _more_tags(self): + return { + '_xfail_checks': { + 'check_transformer_preserve_dtypes': + 'dtypes are preserved but not at a close enough precision', + }, + 'preserves_dtype': [np.float64, np.float32] + } \ No newline at end of file diff --git a/T-ADML/gasoline_example/uv.lock b/T-ADML/gasoline_example/uv.lock new file mode 100644 index 00000000..da4a8015 --- /dev/null +++ b/T-ADML/gasoline_example/uv.lock @@ -0,0 +1,3230 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", + "(python_full_version >= '3.14' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version 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--- /dev/null +++ b/T-ADML/python_ADML_401k.ipynb @@ -0,0 +1,1152 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ec70e3f1", + "metadata": {}, + "source": [ + "# Empirical Example for Sensitivity Analysis: 401(k) Eligibility and Financial Wealth\n", + "\n", + "This is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n", + "*Long Story Short: Omitted Variable Bias in Causal Machine Learning* \n", + "\n", + "The original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "170f2037", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:10.878737Z", + "iopub.status.busy": "2026-09-11T14:54:10.877672Z", + "iopub.status.idle": "2026-09-11T14:54:11.736717Z", + "shell.execute_reply": "2026-09-11T14:54:11.736203Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Install packages if needed\n", + "repos <- c(CRAN = \"https://cloud.r-project.org\")\n", + "\n", + "if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\", repos = repos)\n", + "if (!requireNamespace(\"sensemakr\", quietly = TRUE)) install.packages(\"sensemakr\", repos = repos)\n", + "if (!requireNamespace(\"ggplot2\", quietly = TRUE)) install.packages(\"ggplot2\", repos = repos)\n", + "if (!requireNamespace(\"ranger\", quietly = TRUE)) install.packages(\"ranger\", repos = repos)\n", + "if (!requireNamespace(\"dml.sensemakr\", quietly = TRUE)) {\n", + " remotes::install_github(\"carloscinelli/dml.sensemakr\", upgrade = \"never\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "267a6538", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:11.745657Z", + "iopub.status.busy": "2026-09-11T14:54:11.738176Z", + "iopub.status.idle": "2026-09-11T14:54:11.759105Z", + "shell.execute_reply": "2026-09-11T14:54:11.758692Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dml.sensemakr 0.2.2 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sensemakr 0.1.6 \n" + ] + } + ], + "source": [ + "suppressMessages({\n", + " library(sensemakr) # attached first: we only need add_bound_to_contour()\n", + " library(dml.sensemakr) # attached last: keeps its methods for class \"dml\" unmasked\n", + " library(ggplot2)\n", + "})\n", + "\n", + "options(scipen = 99) \n", + "\n", + "# figure sizing inside the notebook\n", + "options(repr.plot.width = 7, repr.plot.height = 6, repr.plot.res = 150)\n", + "\n", + "dir.create(\"figures\", showWarnings = FALSE)\n", + "\n", + "cat(\"dml.sensemakr\", as.character(packageVersion(\"dml.sensemakr\")), \"\\n\")\n", + "cat(\"sensemakr \", as.character(packageVersion(\"sensemakr\")), \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "a7079423", + "metadata": {}, + "source": [ + "## Setting\n", + "\n", + "The outcome $Y$ is net financial assets and the treatment $D$ is eligibility to enroll in a 401(k) plan. The observed covariates $X^o$ include income, age, education, family size, marital status, two-earner status, IRA participation, and home ownership. The identifying argument used in earlier work is that, after conditioning on these worker characteristics, eligibility is approximately as good as randomly assigned.\n", + "\n", + "However, the threat is that unobserved firm characteristics $A$ may affect both eligibility and financial assets, for example through employer matching policies. Conditional ignorability then holds given the full $X = (X^o, A)$, while the data identify only functions of the short regressor vector $W_s = (D, X^o)$. What we can estimate is the short parameter $\\theta_s$. The task is to bound its\n", + "distance from the causal target $\\theta$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "641048b9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:11.760854Z", + "iopub.status.busy": "2026-09-11T14:54:11.760463Z", + "iopub.status.idle": "2026-09-11T14:54:11.771931Z", + "shell.execute_reply": "2026-09-11T14:54:11.771465Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "households: 9915 covariates: 8 eligible: 37.1% \n" + ] + } + ], + "source": [ + "data(\"pension\")\n", + "\n", + "y <- pension$net_tfa # Y: net total financial assets\n", + "d <- pension$e401 # D: 401(k) eligibility\n", + "\n", + "# X^o: the observed worker characteristics\n", + "x <- model.matrix(~ -1 + age + inc + educ + fsize + marr + twoearn + pira + hown,\n", + " data = pension)\n", + "\n", + "cat(\"households:\", nrow(x), \" covariates:\", ncol(x),\n", + " \" eligible:\", sprintf(\"%.1f%%\", 100 * mean(d)), \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "5be9f408", + "metadata": {}, + "source": [ + "## Short Estimates Under Conditional Ignorability\n", + "\n", + "### Partially Linear Model\n", + "\n", + "The first specification is the partially linear model,\n", + "\n", + "$$Y = \\theta_s D + f_s(X^o) + \\varepsilon, \\qquad \\mathrm{E}[\\varepsilon \\mid D, X^o] = 0,$$\n", + "\n", + "with the nuisance functions modeled by random forests, cross-fitted over five folds and repeated five times." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f4dad1cf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:11.773658Z", + "iopub.status.busy": "2026-09-11T14:54:11.773277Z", + "iopub.status.idle": "2026-09-11T14:54:38.547333Z", + "shell.execute_reply": "2026-09-11T14:54:38.546902Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Partially Linear \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 26.663%), treatment (ranger, R2 = 11.588%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 9038 1326 6.815 0.00000000000944 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set.seed(1)\n", + "dml.401k.plm <- dml(y, d, x, model = \"plm\", cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", + "\n", + "summary(dml.401k.plm)" + ] + }, + { + "cell_type": "markdown", + "id": "994cf0bc", + "metadata": {}, + "source": [ + "### Nonparametric Model, with Income Groups\n", + "\n", + "The second specification drops the additive structure. The short regression\n", + "$g_s(W_s) = \\mathrm{E}[Y \\mid D, X^o]$ is left unrestricted and the target is the average contrast,\n", + "\n", + "$$\\theta_s = \\mathrm{E}\\big\\{g_s(1, X^o) - g_s(0, X^o)\\big\\},$$\n", + "\n", + "estimated with the same nuisance learners. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c886bdf1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:38.550249Z", + "iopub.status.busy": "2026-09-11T14:54:38.549906Z", + "iopub.status.idle": "2026-09-11T14:54:38.554742Z", + "shell.execute_reply": "2026-09-11T14:54:38.554353Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# income quartiles\n", + "g1 <- cut(x[, \"inc\"],\n", + " quantile(x[, \"inc\"], c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE),\n", + " labels = c(\"q1\", \"q2\", \"q3\", \"q4\"), include.lowest = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e281ccde", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:38.556283Z", + "iopub.status.busy": "2026-09-11T14:54:38.555936Z", + "iopub.status.idle": "2026-09-11T14:55:06.503086Z", + "shell.execute_reply": "2026-09-11T14:55:06.502651Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Nonparametric \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 25.821%), treatment (ranger, R2 = 11.499%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 8252 1187 6.95 0.00000000000365 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Group Average Treatment Effect: \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "g.ate.q1 4560.8 806.8 5.653 0.0000000158 ***\n", + "g.ate.q2 2801.4 1310.0 2.139 0.032474 * \n", + "g.ate.q3 6806.1 1835.0 3.709 0.000208 ***\n", + "g.ate.q4 18597.4 4071.7 4.567 0.0000049363 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set.seed(1)\n", + "dml.401k.npm <- dml(y, d, x, groups = g1, model = \"npm\",\n", + " cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", + "\n", + "summary(dml.401k.npm)" + ] + }, + { + "cell_type": "markdown", + "id": "ef3e8146", + "metadata": {}, + "source": [ + "## Group Effects by Income Quartile\n", + "\n", + "Plotted against income quartile, with sampling confidence bounds under conditional ignorability,\n", + "the pattern in the summary above is easier to read.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5c41f860", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:06.505709Z", + "iopub.status.busy": "2026-09-11T14:55:06.505362Z", + "iopub.status.idle": "2026-09-11T14:55:06.510354Z", + "shell.execute_reply": "2026-09-11T14:55:06.509927Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "group.names <- setdiff(names(coef(dml.401k.npm)), \"ate\")\n", + "\n", + "df <- data.frame(groups = 1:4, estimate = coef(dml.401k.npm)[group.names])\n", + "cis <- setNames(as.data.frame(confint(dml.401k.npm)[group.names, ]), c(\"lwr.ci\", \"upr.ci\"))\n", + "df <- cbind(df, cis)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "522bd397", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:06.511809Z", + "iopub.status.busy": "2026-09-11T14:55:06.511512Z", + "iopub.status.idle": "2026-09-11T14:55:06.689836Z", + "shell.execute_reply": "2026-09-11T14:55:06.689403Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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5UurSRaqokHbeWTr77AQ0mibmRYBAQ14YyQQBBBBAAAEEEEAAAQQQiJfAqFHSv/4lNW7sPzLRpEm82kdrCidAoKFwtuSMAAIIIIAAAggggAACCERS4JNP/reEZd++/oiGSDaESpdEgEBDSdgpFAEEEEAAAQQQQAABBBAIp4CtLmGrTCxf7s/J0Lt3OOtJrcIrQKAhvH1DzRBAAAEEEEAAAQQQQACBogtMnCi9/LJf7Pjx/moTRa8EBUZagEBDpLuPyiOAAAIIIIAAAggggAAC+RP45htpxAg/v549pb33zl/e5JQcAQINyelrWooAAggggAACCCCAAAIIZBWw4MKiRdKGG0oDBmRNykEEahQg0FAjDQcQQAABBBBAAAEEEEAAgeQI3HKL9PjjfnvHjpVatkxO22lpfgUINOTXk9wQQAABBBBAAAEEEEAAgcgJzJ4tDR7sV/u006RDDolcE6hwiAQINISoM6gKAggggAACCCCAAAIIIFAKgT59pDlzpHXWkS6+uBQ1oMw4CRBoiFNv0hYEEEAAAQQQQAABBBBAIEeBe+6R7r3XP+nyy6W1184xA5IjUE2AQEM1EN4igAACCCCAAAIIIIAAAkkRmD9f6t/fb23HjpL9x4ZAQwUINDRUkPMRQAABBBBAAAEEEEAAgYgKnHee9P33UuvW0vDhEW0E1Q6dAIGG0HUJFUIAAQQQQAABBBBAAAEECi/w3HPS1Kl+OaNHSx06FL5MSkiGAIGGZPQzrUQAAQQQQAABBBBAAAEE0gJLl0rduvlv999fOumk9CFeINBgAQINDSYkAwQQQAABBBBAAAEEEEAgWgIXXih9+aXUvLk0fny06k5twy9AoCH8fUQNEUAAAQQQQAABBBBAAIG8Cfzzn9KNN/rZDR0qbbZZ3rImIwQ8AQINXAgIIIAAAggggAACCCCAQEIEVqyQunSRUinpF7+QzjwzIQ2nmUUVINBQVG4KQwABBBBAAAEEEEAAAQRKJ2ArS7z/vtSkiTRxotSIO8LSdUaMS+ayinHn0jQEEEAAAQQQQAABBBBAIBD44ANpwgT/nc3RsP32wRF+IpBfAQIN+fUkNwQQQAABBBBAAAEEEEAgdAIVFVLXrlJ5ubTNNlLPnqGrIhWKkQCBhhh1Jk1BAAEEEEAAAQQQQAABBDIJjB0rvfGGVFbmPzLRrFmmVOxDID8CBBry40guCCCAAAIIIIAAAggggEAoBf79b2nUKL9qvXpJu+8eympSqRgJEGiIUWfSFAQQQAABBBBAAAEEEECgukD37tKSJdImm0j9+lU/ynsE8i9AoCH/puSIAAIIIIAAAggggAACCIRC4IYbpGee8asybpzUokUoqkUlYi5AoCHmHUzzEEAAAQQQQAABBBBAIJkCM2dKQ4b4bT/zTOmgg5LpQKuLL0CgofjmlIgAAggggAACCCCAAAIIFFygd29p3jxp/fWlQYMKXhwFIJAWINCQpuAFAggggAACCCCAAAIIIBAPgalTpQce8NsyZozUtm082kUroiFAoCEa/UQtEUAAAQQQQAABBBBAAIE6CcydK114oZ/0xBOlI4+s02kkQiBvAgQa8kZJRggggAACCCCAAAIIIIBA6QXOPVf64Qd/FMOwYaWvDzVIngCBhuT1OS1GAAEEEEAAAQQQQACBmAo8+aR0zz1+4664QlpvvZg2lGaFWoBAQ6i7h8ohgAACCCCAAAIIIIAAAnUTWLxY6tHDT3vwwdJxx9XtPFIhkG8BAg35FiU/BBBAAAEEEEAAAQQQQKAEAv36SV9/LbVoIY0bV4IKUCQC/xUg0MClgAACCCCAAAIIIIAAAghEXODVV6Vbb/UbMWKEtPHGEW8Q1Y+0AIGGSHcflUcAAQQQQAABBBBAAIGkCyxfLnXtKqVS0p57Sp06JV2E9pdagEBDqXuA8hFAAAEEEEAAAQQQQACBBggMHSp9/LHUrJk0YYJUVtaAzDgVgTwIEGjIAyJZIIAAAggggAACCCCAAAKlEHjnHenqq/2SL7pI2nrrUtSCMhGoKkCgoaoH7xBAAAEEEEAAAQQQQACBSAisWuU/MmE/d9hB6t49EtWmkgkQiFSgYcWKFfruu++0dOnSWrvmxx9/VEVFRa3pli1bpvnz5xc9Xa0FkgABBBBAAAEEEEAAAQQQyCIwZoz05ptSI3dXN3Gi1KRJlsQcQqCIApG4FGfOnKlJkybp5ZdfdhOcuBlO3Lbjjjuqf//+2m677dJcFliYMmWKnnnmGX3zzTduWZcWOuyww9TPrfPSpNqn7qOPPtLUqVP1xhtvaLmbPWWzzTbToEGDtMsuu6Tzsxf5Tlclc94ggAACCCCAAAIIIIAAAvUQ+OwzyQINtp1/vrTrrv5r/kUgDAKhH9FgIw4GDhyot956SyeddJL+9Kc/qUuXLvrhhx/Ut29f72cAef/993uBBgsu3HTTTTr99NP1yCOPaPTo0UES7+eSJUt06aWX6vvvv/eOjR8/3gtKWEDiM/vE/nfLd7ogX34igAACCCCAAAIIIIAAAvUVsO9eu3WT3K2SNt9c6tOnvjlxHgKFEQh9oOH555/X119/rVNPPVUXXHCBfvnLX+rss892UbvztWjRIj344IOejAUeJk+erGOOOUZnnXWWdtppJy/Q0LlzZz399NP69ttv04K33HKLZs+erSuvvFL77LOP99/VbgaVMjc96913312wdOmMeYEAAggggAACCCCAAAII1FPAffeqF1/0T3bfmap583pmxGkIFEgg9IEGa/cee+yhww8/vArBvvvu6723ORtse+WVV7y5G373u99574N/jjjiCO/lY489FuzyAg8WsGjfvn16X6tWrXTggQfqueeec5FBFxp0mwUo8pkuXRgvEEAAAQQQQAABBBBAAIF6CNj3p8OG+Sd27Srtt189MuEUBAosEPpAgwUKrrnmGm2wwQZVKF566SXv/eY2VshtwSMPW2yxhfc++GeTTTZR48aN9fnnn3u7bJLIefPmqXo6O2j7bMJJG0GR73RBffiJAAIIIIAAAggggAACCNRX4JxzpAUL5O6P5B4xr28unIdAYQUiMRlkdQIbcWCPONhkj0cffbR32FaOaOSmW23dunX15GrTpk16ZYlghYm2bdtmTGc7LY0FJ2zLVzovs2r/2KMbNtlk5S0od+7cuemJLysfD8vrlStXelUpLy/Xzz//HJZqUY8ICNikrcHnNQLVpYohEFhla3a5zX7y+yYEHRKhKtj/q+wxS5tziQ2BuggEK5bZ5OP8vqmLGGkCAft9Y19Y2iTzhdz+8pc19OijrbwihgxZpJUrV8jdNrBFUCBY5MCqPmfOHO8x/rA2w1Z9DP4eq2sdIxdosA/v0KFDvREKF198cfrxB9u/5pprZuygli2sgPKSAABAAElEQVRbym6IbQs+/PaoRPXN0tlmafOdrnpZ9t6CDK+//nqVQ82aNfPeWx2COldJELI39gGJQj1Dxpb46gSBqsRDAJCTAL9vcuIi8X8F+H3DpVBfAf6+qa9css/L9WYsF62ffy7TiBHtvFM6dlyqAw5Y4oIbueRA2rAKWJAqzJsFYSsHRupS10gFGuxb0MGDB+v999/3JoM86qij0m1ca621vDka0jsqvbBvMtq18z+Uls62TN9uBPuau9lU8p2uUnXSL20VjWCuifRO98IeFbFASKZgSOV0pXxtgRj7QNgIDBtZwoZAXQUWL17sJixqnh41VNfzSJdcgeAbIhu1ZgFlNgTqKmD/X7cAfvUlrut6PumSJ2CBqWCurjD/HZa8ngl/i+26sf9PBV8aFqLG55/f3H3z3cjd16R08cWr+H9iIZCLmKfduC9d6o+4sy+8bWGCsG5NmzaN74gGW1XClp+cOXOmhg8frt/+9rdV+mHdddf1Gm83McHIhCDBAvcQ0w477OC9tXS22b7qW7DPHrXId7rqZdn7jh07rrbboucWaLA2ZHoMZLUTSrTDPhj2x7/98RbmepaIh2KzCNgf/nazWMj/EWcpnkMRFLBrxoKbNT0eF8EmUeUiCdh1Y4FNAuJFAo9BMXbN2A2j/cHP3zcx6NAiNsGCVHYzVqgA1X33SdOn+w266qoybbrp6qOzi9hcisqDgF0zQaDBrhv7Oyesm/3dnusor/C2ppKyLU3Zu3dvbxJHuwmvHmSwpMEKEjaRY+Xt+++/d88urdTWW2/t7V5jjTW8eReqp7ODts++od9ss82U73SV68RrBBBAAAEEEEAAAQQQQKAuAvb9aN++fspDD5Wbo64uZ5EGgdIKhD7QYFFlG8lgP2+44Qb94he/yCh2yCGHeFHEp556qsrxJ5980osO/frXv07vtyUwZ8yY4SZO+d/MKTbBxYtuMVpb4tKCDLblO126ArxAAAEEEEAAAQQQQAABBOogYEEGW9LSpphzc8mzIRAJgdDP0fDnP/9Zs2bN0q677qpHHnlkNdRNN93UewTB5mA48sgjdZ8bV2SPPey///56++23NWXKFP3hD39Ij2iwDH7/+9/r/vvv14UXXqg+ffp4AYqbbrrJC0j0DcKFBUi3WuXZgQACCCCAAAIIIIAAAgjUIPDCC3L3M/7BkSOlDTesISG7EQiZQOgDDc8884xH9u6778r+q77tueee6bkOLrjgAm82zFtuuUUWOFh77bW9Y127dq1yWocOHXTttdfqsssu8wIN9jyMBTJGjBiRnpvBTsh3uiqV4A0CCCCAAAIIIIAAAgggUIOAG9Ct7t3l7m+kffaR+/K0hoTsRiCEAqEPNNxzzz11ZrNJKi666CJZwMEmjbTRDjVNqmGBheluRhWbZNLOC1alqF5YvtNVz5/3CCCAAAIIIIAAAggggEB1AbfYnj77TO6xbmn8eLlJSqun4D0C4RUIfaChPnQ2x8Lmm29ep1ODSSRrS5zvdLWVx3EEEEAAAQQQQAABBBBIpsCbb0qTJvltv+QSacstk+lAq6MrEPrJIKNLS80RQAABBBBAAAEEEEAAgdwE3IJ5sie/KyqkXXaRzj47t/NJjUAYBAg0hKEXqAMCCCCAAAIIIIAAAggg4ATcNHJ65x2pcWNpwgT/JzAIRE2AQEPUeoz6IoAAAggggAACCCCAQCwFPv5YGjvWb1q/ftJOO8WymTQqAQIEGhLQyTQRAQQQQAABBBBAAAEEwi1gq0vYIxPLl0tbbSX16hXu+lI7BLIJEGjIpsMxBBBAAAEEEEAAAQQQQKAIAvaYxKuv+gXZa1ttgg2BqAoQaIhqz1FvBBBAAAEEEEAAAQQQiIXA119LI0b4TTnnHGmvvWLRLBqRYAECDQnufJqOAAIIIIAAAggggAACpRfo0UNavFjaaCOpf//S14caINBQAQINDRXkfAQQQAABBBBAAAEEEECgngKTJ0tPPumfPG6c1LJlPTPiNARCJECgIUSdQVUQQAABBBBAAAEEEEAgOQI//CANHuy3t1Mn6Ve/Sk7baWm8BQg0xLt/aR0CCCCAAAIIIIAAAgiEVOC886S5c6V115UuvjiklaRaCNRDgEBDPdA4BQEEEEAAAQQQQAABBBBoiMDdd0vTp/s5jB4ttWvXkNw4F4FwCRBoCFd/UBsEEEAAAQQQQAABBBCIucC8ef+b9LFjR+mYY2LeYJqXOAECDYnrchqMAAIIIIAAAggggAACpRTo00eaOVNq0+Z/y1qWsj6UjUC+BQg05FuU/BBAAAEEEEAAAQQQQACBGgSeeUaaOtU/ePnlUvv2NSRkNwIRFiDQEOHOo+oIIIAAAggggAACCCAQHYElS6Tu3f36HnigdNJJ0ak7NUUgFwECDblokRYBBBBAAAEEEEAAAQQQqKfAwIHSv/8tNW8ujRtXz0w4DYEICBBoiEAnUUUEEEAAAQQQQAABBBCItsAbb0g33ui34dJLpU03jXZ7qD0C2QQINGTT4RgCCCCAAAIIIIAAAggg0ECB8nKpa1cplZJ++UvpjDMamCGnIxByAQINIe8gqocAAggggAACCCCAAALRFhg+XPrgA6lpU2nCBKkRd2HR7lBqX6sAl3itRCRAAAEEEEAAAQQQQAABBOon8P77fnDBzr7wQmm77eqXD2chECUBAg1R6i3qigACCCCAAAIIIIAAApERqKiQunSRVqzwAww9e0am6lQUgQYJEGhoEB8nI4AAAggggAACCCCAAAKZBa66SvrnP6WyMn9Ugz06wYZAEgQINCShl2kjAggggAACCCCAAAIIFFXgyy+lyy/3i+zd258EsqgVoDAESihAoKGE+BSNAAIIIIAAAggggAAC8RTo1k1assRfxrJv33i2kVYhUJMAgYaaZNiPAAIIIIAAAggggAACCNRD4Prrpeee808cN05q0aIemXAKAhEWINAQ4c6j6ggggAACCCCAAAIIIBAuge+/l4YM8et05pnSgQeGq37UBoFiCBBoKIYyZSCAAAIIIIAAAggggEAiBHr1kubPl9q3lwYPTkSTaSQCqwkQaFiNhB0IIIAAAggggAACCCCAQO4CU6dKDz3kn3fFFVKbNrnnwRkIxEGAQEMcepE2IIAAAggggAACCCCAQEkFfv5ZGjDAr8KJJ0q/+11Jq0PhIRdYtWqVZs2aFfJa1r96BBrqb8eZCCCAAAIIIIAAAggggIAn0L//Gpo9W1prLWn4cFAQyC5w0UUXqVOnTnr33XezJ4zoUQINEe04qo0AAggggAACCCCAAALhEHj66Sa6//6mXmXGjJHWXTcc9aIW4RS4wj1XM336dM2ZM0dvvPFGOCvZwFoRaGggIKcjgAACCCCAAAIIIIBAcgUWLZL692/pAfz619JxxyXXgpbXLnDXXXfpuuuu8xKeeuqp6t69e+0nRTAFgYYIdhpVRgABBBBAAAEEEEAAgXAI9O8vffttI625Zkpjx4ajTtQinAJPPPGEW/rUX/v0qKOO0sCBA8NZ0TzUikBDHhDJAgEEEEAAAQQQQAABBJIn8PLL0q23+u2++OJybbRR8gxocd0E3nzzTfVya5/aJJD77LOPrr32WpWVldXt5AimItAQwU6jyggggAACCCCAAAIIIFBageXLpW7dpFRK2n33lTrllJWlrRClh1bgyy+/1Jlnnqlly5Zp22231R133KFmzZqFtr75qBiBhnwokgcCCCCAAAIIIIAAAggkSsBGwH/yidwNozRq1FL37XSimk9j6ygw2y1FYqtLzJ07Vx06dNC0adPUtm3bOp4d3WQEGqLbd9QcAQQQQAABBBBAAAEESiDwr39J11zjFzxokLTFFhUlqAVFhl1g8eLFOuOMM/TNN9+odevWXpBhww03DHu181I/Ag15YSQTBBBAAAEEEEAAAQQQSILASveERJcucs/aSzvt5D8+kYR208bcBFa6C8VWlHjvvffUtGlT3X777dp+++1zyyTCqQk0RLjzqDoCCCCAAAIIIIAAAggUV2DMGOntt6XGjaWJE6UmTYpbPqVFQ2DAgAF64YUXvAkfr3HDX/bff/9oVDxPtSTQkCdIskEAAQQQQAABBBBAAIF4C3z6qXTFFX4bzz9f2nnneLeX1tVPYIyLRk2fPt07+dJLL9Xxxx9fv4wifBaBhgh3HlVHAAEEEEAAAQQQQACB4gjY6hK2yoRbOEBbbimdd15xyqWUaAn8+c9/1vXXX+9VukePHurZs2e0GpCn2hJoyBMk2SCAAAIIIIAAAggggEB8Ba69VnrpJb9948dLzZvHt620rH4Cjz/+uIbYciRuO+aYYzRs2LD6ZRSDswg0xKATaQICCCCAAAIIIIAAAggUTuA//5G7afTz79pV2mefwpVFztEUmDFjhnr37q2Kigrtu+++mjRpkjc/QzRb0/BaE2houCE5IIAAAggggAACCCCAQIwFbPT7woWSrUx40UUxbihNq5fAF198obPOOss9VrNM2223ne644w41a9asXnnF5SQCDXHpSdqBAAIIIIAAAggggAACeRdw94x67DE/2yuvlFq1ynsRZBhhgdmzZ6tTp06aO3euOnTooKlTp6pNmzYRblF+qk6gIT+O5IIAAggggAACCCCAAAIxE/jxR2ngQL9Rf/iDdOihMWsgzWmQwKJFi3T66afrP+7ZmtatW2vatGlu1Isb9sImAg1cBAgggAACCCCAAAIIIIBABoE+faSff5bWXlsaOjRDAnYlVmDlypXq3r273n//fTVt2tR7XGL77bdPrEf1hhNoqC7CewQQQAABBBBAAAEEEEi8wH33Sffc4zOMGuUHGxKPAkBaYMCAAXrxxRe9CR//9Kc/ab/99ksf44UY0cBFgAACCCCAAAIIIIAAAghUFpg/X7rgAn/PUUdJxx9f+Sivky4wevRoTZ8+3WOwJSw7duyYdJLV2s+IhtVI2IEAAggggAACCCCAAAJJFujbV/ruO7nn7qXLLkuyBG2vLnDnnXfqhhtu8Hb3dMuR9OjRo3oS3jsBAg1cBggggAACCCCAAAIIIIDAfwWef16aMsV/M3KktMEG/z3Aj8QLPOaWHxn638k6jj32WF166aWJN6kJgEBDTTLsRwABBBBAAAEEEEAAgUQJLF0qdevmN9keuT/llEQ1n8ZmEZgxY4Z69+6tiooKbz4Gm5ehrKwsyxnJPkSgIdn9T+sRQAABBBBAAAEEEEDgvwKDB0tffCE1by6NHy93IwkNAtLnn3+uM888U8uXL5etLHH77berWbNm0GQRINCQBYdDCCCAAAIIIIAAAgggkAyBf/5Tuu46v62XXCJtvnky2k0rswvMnj1bnTp10rx589xjNBto6tSpatOmTfaTOMocDVwDCCCAAAIIIIAAAgggkGyBFSukrl3lhsVLu+4qnXVWsj1ovS+waNEide7cWd9++60XXLAggwUb2GoXYERD7UakQAABBBBAAAEEEEAAgRgL2MoS770nNWkiTZwoNW4c48bStDoJrFy50s3X0U0ffPCB95iEPS5hj02w1U2AQEPdnEiFAAIIIIAAAggggAACMRT48ENp7Fi/Yf36STvsEMNG0qScBFKplPr376+XXnrJm/DRJn7cz2YHZauzAIGGOlOREAEEEEAAAQQQQAABBOIkYI9K2CMT5eXSNttIvXrFqXW0pb4Co0eP1n333eedPnz4cNlSlmy5CRBoyM2L1AgggAACCCCAAAIIIBATgQkTpNdf91eXsNcsJBCTjm1AM+644w7deOONXg7nnHOOunfv3oDcknsqgYbk9j0tRwABBBBAAAEEEEAgsQJffSWNHOk3v2dPaY89EktBw/8r8Pe//13Dhg3z3nXs2FFDhw7Fpp4CBBrqCcdpCCCAAAIIIIAAAgggEF0B+6J68WJp442lAQOi2w5qnh+Bf7r1Tc877zy38kiFNx+DzctQVlaWn8wTmAuBhgR2Ok1GAAEEEEAAAQQQQCDJApMnS08/7QuMGyetuWaSNWj7Z5995pY0PUvLly/3VpawxyeaNm0KTAMECDQ0AI9TEUAAAQQQQAABBBBAIFoCs2ZJgwf7de7USTr44GjVn9rmV2D27Nnq3Lmz5s2bpw033FDTpk1T69at81tIAnMj0JDATqfJCCCAAAIIIIAAAggkVaB3b2nuXGm99aRLLkmqAu02gYULF6qTizZ9++23atOmjaZOnaoOHTqAkwcBAg15QCQLBBBAAAEEEEAAAQQQCL/A3XdL99/v13PMGGmttcJfZ2pYGIGVK1eqW7du+vDDD91qI81kj0tst912hSksgbkSaEhgp9NkBBBAAAEEEEAAAQSSJmCjGPr181t93HHSUUclTYD2BgKpVMpdC/308ssvexM+Tpo0Sfvuu29wmJ95ECDQkAdEskAAAQQQQAABBBBAAIFwC/TpI9n8DG3bSiNGhLuu1K6wApdffrkb2eIPbRk+fLiOOeaYwhaYwNwJNCSw02kyAggggAACCCCAAAJJEnjqKblJ/vwWu3tMrb9+klpPWysL3H777brpppu8Xeeee6662zqnbHkXINCQd1IyRAABBBBAAAEEEEAAgbAILF4s9ejh1+agg6QTTwxLzahHsQUeffRR2QgG245zz88MGTKk2FVITHkEGhLT1TQUAQQQQAABBBBAAIHkCQwcKH31ldSihTRuXPLaT4t9gX/84x/q456fqaio0P77769rr73Wm58Bn8IIEGgojCu5IoAAAggggAACCCCAQIkFXntNbpi8X4lLL5U22aTEFaL4kgh89tlnOuuss7R8+XJtv/32sscnmjZtWpK6JKVQAg1J6WnaiQACCCCAAAIIIIBAggTKy6WuXSW3wID22EM6/fQENZ6mpgV++OEHderUSfPnz9eGG27o5uqYptatW6eP86IwAgQaCuNKrggggAACCCCAAAIIIFBCgWHDpI8+kvvmWpowQWrEnU8Je6M0RS9cuNALMnz33Xdq06aNF2To0KFDaSqTsFL5uCWsw2kuAggggAACCCCAAAJxF3j3XWniRL+VF14obbNN3FtM+6oLrFixwo1o6eqCTR+pWbNmmjJlirbddtvqyXhfIAECDQWCJVsEEEAAAQQQQAABBBAovsCqVf4jE+4+UzvsIPXsWfw6UGJpBVLueZl+/frplVdecSNZGum6667TPvvsU9pKJax0Ag0J63CaiwACCCCAAAIIIIBAnAXGjpVmzPAflbBHJpjzL869nbltl112mR544AHv4IgRI3T00UdnTsjeggkQaCgYLRkjgAACCCCAAAIIIIBAMQU+/1waNcovsVcv6Re/KGbplBUGgdtuu02TJ0/2qtK7d2/v8Ykw1CtpdSDQkLQep70IIIAAAggggAACCMRQwFaX6N5dWrpU2nxzqW/fGDaSJmUVePTRR2UjGGw74YQTdPHFF3uv+af4AgQaim9OiQgggAACCCCAAAIIIJBngRtukJ5/3s903DipRYs8F0B2oRZ44403dN5556miokIHHHCArr76apWVlYW6znGuHIGGOPcubUMAAQQQQAABBBBAIAECbvVCXXKJ39CzzpL23z8BjaaJaYFPP/1UZ599tsrLy90EoDvIHp9oyuQcaZ9SvCDQUAp1ykQAAQQQQAABBBBAAIG8CZx7rrRggdShgzR4cN6yJaMICMyaNUudOnXS/PnztdFGG2natGlq3bp1BGoe7yoSaIh3/9I6BBBAAAEEEEAAAQRiLXDXXdLDD/tNvOIKuZvMWDeXxlUSWLhwoTp37qzvv/9ebdu21dSpU9W+fftKKXhZKgECDaWSp1wEEEAAAQQQQAABBBBokMBPP0kDBvhZnHSSdPjhDcqOkyMksGLFCnXp0kUfffSR1lhjDd1xxx3adtttI9SCeFeVQEO8+5fWIYAAAggggAACCCAQWwG3eqF+/FFq104aPjy2zaRh1QRSbomRvm5ZkVdffVWNGjXSpEmTtM8++1RLxdtSChBoKKU+ZSOAAAIIIIAAAggggEC9BP7+d+nee/1Tx4yR1lmnXtlwUgQFRo0apQcffNCr+ciRI3X00UdHsBXxrjKBhnj3L61DAAEEEEAAAQQQQCB2Au7RfPXs6TfrN7+ROnaMXRNpUA0Ct9xyi26++WbvqC1naY9PsIVPgEBD+PqEGiGAAAIIIIAAAggggEAWgf79pW+/lVq2lK68MktCDsVK4JFHHtFll13mtenEE090K4ywxEhYO5hAQ1h7hnohgAACCCCAAAIIIIDAagIvvijddpu/e8QIuSUNV0vCjhgKvP766+rTp48qKip04IEH6uqrr1ZZWVkMWxqPJhFoiEc/0goEEEAAAQQQQAABBGIvsGyZ1K2b5OYC1N57S6eeGvsm00An8Mknn+jss89WeXm5dtxxR916661q0qQJNiEWINAQ4s6haggggAACCCCAAAIIIPA/gSFDpM8+k1vOUBo/Xu4b7f8d41U8BWbNmqXOnTtrwYIFbvTKRpo6dapat24dz8bGqFUEGmLUmTQFAQQQQAABBBBAAIG4Crz1lnTNNX7rLrpI2mqruLaUdgUCC92sn506ddL333+vtm3batq0aWrfvn1wmJ8hFiDQEOLOoWoIIIAAAggggAACCCAgrVwpde0q93y+tPPOUvfuqMRdYMWKFd7jEh9//LEbwbKGpkyZom222SbuzY5N+wg0xKYraQgCCCCAAAIIIIAAAvEUGDNG+te/pMaNpQkT/J/xbCmtMoGUm4Tjggsu0GuvvaZGjRrpuuuuc3NyuEk52CIjQKAhMl1FRRFAAAEEEEAAAQQQSJ6A+0JbFmiw7fzz/REN/jv+jauALWH50EMPec2z10cddVRcmxrbdhFoiG3X0jAEEEAAAQQQQAABBKItYKtL2CoTy5f7czK41Q3ZYi5w8803a/LkyV4rbTlLW22CLXoCBBqi12fUGAEEEEAAAQQQQACBRAhce630yit+U+2RCVttgi2+Ag8//LBGjRrlNfCkk07SoEGD4tvYmLeMQEPMO5jmIYAAAggggAACCCAQRYFvvpEuvdSvuY1q2GuvKLaCOtdVwOZjON89G2PzMxx00EGaOHGiW76U9Uvr6he2dAQawtYj1AcBBBBAAAEEEEAAAQTUo4e0aJG04YaSLWfJFl8BW1miS5cuKi8v10477aRbb71VTZo0iW+DE9AyAg0J6GSaiAACCCCAAAIIIIBAlARuv1164gm/xmPHSi1bRqn21DUXgZkzZ6pz585asGCBNt54Y02dOlWtWrXKJQvShlCAQEMIO4UqIYAAAggggAACCCCQVIHZs6WBA/3W//GP0iGHJFUi/u1euHChF2SwYMNaa62ladOmaf31149/wxPQQgINCehkmogAAggggAACCCCAQFQEzjtPmjNHWmcdaejQqNSaeuYqYI9J2IoS9tjEGm6WzylTpmjrrbfONRvSh1SAQENIO4ZqIYAAAggggAACCCCQNIHp06W//tVv9ejRUrt2SRNIRnttwkeb+NEmgGzUqJGuv/56N9kns33GqfcJNMSpN2kLAggggAACCCCAAAIRFZg3T+7m06/80UdLxx4b0YZQ7VoFRo4cKVvK0jZbzvLII4+s9RwSREuAQEO0+ovaIoAAAggggAACCCAQS4G+fSX3qL5at5YuuyyWTaRRTuCmm27SLbfc4lnYqIazzjoLlxgKEGiIYafSJAQQQAABBBBAAAEEoiTw7LPSnXf6NbYgQ4cOUao9da2rwN/+9jddfvnlXvKTTz7ZLVvKuqV1tYtaOgINUesx6osAAggggAACCCCAQIwEliyRunf3G3TAAdIpp8SocTQlLWDzMdgIBpuf4eCDD9aECRNUVlaWPs6LeAkQaIhXf9IaBBBAAAEEEEAAAQQiJTB4sPTll1Lz5tK4cZGqOpWto4CtLGErTKxYsUI777yz9+hEkyZN6ng2yaIoQKAhir1GnRFAAAEEEEAAAQQQiIHAP/4hXXed35BLLpE22ywGjaIJVQRmuok3OnXqpIULF2qTTTbRXXfdpVatWlVJw5v4CRBoiF+f0iIEEEAAAQQQQAABBEIvUF4ude0qN5Re2m03uW+8Q19lKpijwIIFC7wgw6xZs7TWWmtp2rRpWn/99XPMheRRFCDQEMVeo84IIIAAAggggAACCERcwK1qqPffl2wEvXtcX424M4l4j1atfrmLJHXp0kWffPKJeyymuZvs805ttdVWVRPxLrYCfJxj27U0DAEEEEAAAQQQQACBcAp88IE0dqxft379pO23D2c9qVX9BGzCxz59+sgmgGzkIkjXX3+99txzz/plxlmRFCDQEMluo9IIIIAAAggggAACCERToKJC7ptuyR6d2HZbqVevaLaDWtcsMHLkSD3yyCNegtGjR+uII46oOTFHYilAoCGW3UqjEEAAAQQQQAABBBAIp4A9JmGTQNrKhva6WbNw1pNa1U/gxhtv9FaVsLNtOcszzjijfhlxVqQFCDREuvuoPAIIIIAAAggggAAC0RGwZSxHjPDre8450u67R6fu1LR2gYceekg2gsG2k08+WYMGDar9JFLEUoBAQyy7lUYhgAACCCCAAAIIIBA+gR49pCVLpE03lQYMCF/9qFH9BV599VVdcMEFbhWRlH71q1+50SpuuApbYgUINCS262k4AggggAACCCCAAALFE7j5ZumZZ/zybCLIFi2KVzYlFVbg448/9laYWLFihXbeeWfv0YkmtpwIW2IFCDQktutpOAIIIIAAAggggAACxRGYOVMaPNgvq3Nn6aD/Z+8+4KQo0j6O/8lZAQU8UQFRD/FM4HnG1wgmOBMquiAoCB5ZQVBEQSQIkhRQgijZnOVU9PQMmFD0PBE9RVSCknPehbeeLmcXcVl2lpnZnplffT6wsz3dXVXfHoaZp6ueOjMx9VJL/AWWLFmijIwMrV+/XoceeqimTJmicuXKxb9iagi1AIGGUF8eGocAAggggAACCCCAQPIL2MoSa9ZIVatKPXsmf3/ogRdYt25dEGT49ddfValSJU2bNs1dY3eRKWkvQKAh7V8CACCAAAIIIIAAAgggED+Bxx+XXnjBn3/gQGn//eNXF2dOnMA2tz7pDTfcoP/9738qXbq0Jk2apNq1ayeuAdQUagECDaG+PDQOAQQQQAABBBBAAIHkFVi1SurSxbf/0kulCy9M3r7Q8hyBHTt2qEOHDvroo49UrFgxPfTQQ6pfv37ODjxKewECDWn/EgAAAQQQQAABBBBAAIH4CHTsKC1bJlWsKN1zT3zq4KyJF+jj1iidMWNGULEtZ3nBBRckvhHUGGoBAg2hvjw0DgEEEEAAAQQQQACB5BR4/XVp+nTf9nvvlapUSc5+0OrfC9johQkTJgQbu7jhKs2bN//9DvyGgBMg0MDLAAEEEEAAAQQQQAABBGIqsGGD1KaNP+VZZ0lXXBHT03OyQhJ4wSXbGDBgQFD7VVddpdtuu62QWkK1YRcg0BD2K0T7EEAAAQQQQAABBBBIMoHu3aWff5bKlpUGD06yxtPcXAVmzZrl8m100c6dO3X22WdryJAhue7HRgRMgEADrwMEEEAAAQQQQAABBBCImYD7PqoxY/zpevWSDjkkZqfmRIUkMG/ePLVq1Urbt2/Xscceq3Hjxql48eKF1BqqTQYBAg3JcJVoIwIIIIAAAggggAACSSCwdavUurXcXW/ppJOk669PgkbTxDwFFi9erIyMDK1fv16HHXaYpkyZonLlyuV5DE8iQBgqpK+B1atXB8OSQto8ZWZmBk2z9XNXrlwZ1mbSrhAK2HJIa9euVdGixDlDeHlC2aSsrKygXfaT95tQXqLQNsr+r9rgJopv2rQptG2kYeESsP+jrNjQcN5vCnZt+vYtq2++KaMSJXbq7rvXac0ab1qwsyXPUfZ+Y3f77bNxKpV169apWbNmWrp0qVs5pGKwjKWNZLDvKpR9E7D3mUhZ5daBLVKkSOTX0P3cvHmzIp/H8ts4Ag35lUrwfqVLl1aZMmUSXGv+q9uyZYu2upC1rZsb5nbmv0fsmSgB+0+4VKlSDLdLFHgK1GMf2uw/OPsPmPebFLigCeyCfSgqWbKk+8JTIoG1UlUyC9j/UZGbKbzfRH8lv/yyiEaPLh0c2KVLpurWLRn9SZL0CPt/yj4X23tOqhT7rN+pUyf98MMPwWe3Rx55RHXq1EmV7hV6P+z/qMzM7UE77LtfmG/C2f+j9v4YTSHQEI1WAve1/9zKWvackBb7TzgSaAhzO0PKl9bNsmF39maaSv8Rp/UFTVDn7QOc/QfM+02CwFOkGhvJYO81fGFMkQuagG7YZxt73Vhgk/eb6MBt8Fn79nJ3PeUCDFKHDiXcTYX0CfJZUNzu9KfK+42N7uncubM+/fTTIIAyxiXdOPXUU6N7UbB3ngL2fWrTpo3BPvZ+E+ZAg722o20fY5fzvPw8iQACCCCAAAIIIIAAAnsTGDRImjPHZZp33y6GDZP70r23I3g+zAK9e/fWjBkzgibacpYNGzYMc3NpWwgFCDSE8KLQJAQQQAABBBBAAAEEkkXgu++ke+/1rbVRDW5RAkoSC4wePVqPPvpo0INbbrklyNGQxN2h6YUkQKChkOCpFgEEEEAAAQQQQACBZBewfHa2yoRL36VataQuXZK9R+nd/ueee04DBw4MEK6++mp169YtvUHofYEFCDQUmI4DEUAAAQQQQAABBBBIbwF381vvvusNhgyRy8OU3h7J3Pv3339ft956a7DqyjnnnKP7778/mbtD2wtZgEBDIV8AqkcAAQQQQAABBBBAIBkFFi2Sevb0LW/ZUjrllGTsBW02ga+//tqNTGkdrCxw3HHHaezYsawQxktjnwQINOwTHwcjgAACCCCAAAIIIJCeAjffLLnFpPSnP0l33JGeBqnQ68WLFwd5GGxlsBo1amjy5MkqV65cKnSNPhSiAIGGQsSnagQQQAABBBBAAAEEklHAfRd1qxL4lt93n1S+fDL2gjavXbtWGRkZWrp0qSpXrqxp06apSpUqwCCwzwIEGvaZkBMggAACCCCAAAIIIJA+AsuXS127+v42aSKdf3769D2Verp161a1dHNevnPLhpQpUyYYyVDLMnpSEIiBAIGGGCByCgQQQAABBBBAAAEE0kXAlrBcsULuDrjUu3e69Dq1+rljxw516NBBn3zyiYoVK6aHH35YJ554Ymp1kt4UqgCBhkLlp3IEEEAAAQQQQAABBJJH4JVXpKef9u3t398HG5Kn9bQ0InD33Xfrn//8Z/DrgAED1KBBg8hT/EQgJgIEGmLCyEkQQAABBBBAAAEEEEhtgXXrJEsAacWmS/z97/4xfyeXwMiRI/XYY48FjbblLJs1a5ZcHaC1SSFAoCEpLhONRAABBBBAAAEEEECgcAUsL4NboCBI/GgJICnJJ/Dss89q0KBBQcObNm3qcm38lmwj+bpCi0MuQKAh5BeI5iGAAAIIIIAAAgggUNgC77wjTZjgW2F5GWxJS0pyCbz33nuyEQw7d+7Uueeemx1wSK5e0NpkESDQkCxXinYigAACCCCAAAIIIFAIAps3S61by31BlU45Rbr22kJoBFXuk8DcuXPdNWytzMxMHX/88Ro7dqyKFy++T+fkYATyEiDQkJcOzyGAAAIIIIAAAgggkOYCvXpJ338vlS4tDRkiFSmS5iBJ1v1FixYFeRg2bNigGjVqBMtYli1bNsl6QXOTTYBAQ7JdMdqLAAIIIIAAAggggECCBD79VBoxwlfWvbtUq1aCKqaamAisWbNGGRkZWrZsmQ444ABNnz5dBx54YEzOzUkQyEuAQENeOjyHAAIIIIAAAggggECaCmzfLrVqJe3YIR13nJ8+kaYUSdntrVu3qmXLlm40yvcqU6ZMMJKhZs2aSdkXGp18AgQaku+a0WIEEEAAAQQQQAABBOIuMGCA9OWXcnP5paFDpWLF4l4lFcRIYIeLDrVv316zZ892162YxowZoxNOOCFGZ+c0COxdgEDD3o3YAwEEEEAAAQQQQACBtBKYN08aONB3uVMnqW7dtOp+0nf2rrvu0quvvhr0Y6C7kOeff37S94kOJJcAgYbkul60FgEEEEAAAQQQQACBuArYVAmbMuFG3uuII6QOHeJaHSePscCDDz6oiRMnBmft2rVrkKMhxlVwOgT2KpCQQMO2bdu0fv162U8KAggggAACCCCAAAIIhFfggQekDz/0q0vYlIlSpcLbVlr2e4FnnnlGgwcPDjZe69YhvfXWW3+/A78hkCCBqAINN910k5o0aaLtlhlmt7Ju3TrZn9zKww8/rP32288Nv/pt/FVuO7ENAQQQQAABBBBAAAEEClXgxx8lN+o+KK1bSyedVKjNofIoBN59913ZCIadO3fq3HPP1X333RfF0eyKQGwFXGqX/JdXXnlFv/76q7KyslSiRInfHXjooYdq06ZNuQYhfrcjvyCAAAIIIIAAAggggEAoBdq2lTZulA45RLLlLCnJIfDVV1/JbgpnZmYGSR/Hjh3rknhG9VUvOTpKK5NGIKoRDUnTKxqKAAIIIIAAAggggAACUQlMmCDNnOkPsdH3ZctGdTg7F5LAwoUL1bx5c23YsEE1atQIlrEsy8UrpKtBtREBAg0RCX4igAACCCCAAAIIIJCmAm7Qsm67zXe+aVPprLPSFCLJur1mzZog2eOyZct0wAEHaPr06cHPJOsGzU1BAQINKXhR6RICCCCAAAIIIIAAAtEI2MoSq1dLBx6Yk6MhmuPZN/ECW7ZsUYsWLTR//nw3+qRsMJKhZs2aiW8INSKQiwCBhlxQ2IQAAggggAACCCCAQLoIuIUK9OyzvrcDBkgVK6ZLz5O3nzvcGqTt27fXp59+GuRiGDNmTJCbIXl7RMtTTYBAQ6pdUfqDAAIIIIAAAggggEA+BWwUQ8eOfudLLpHsDyX8Ar169dJrr70WNNRWlzjvvPPC32hamFYCBBrS6nLTWQQQQAABBBBAAAEEcgRuuUVuVTm5peilfv1ytvMovAIPPvigJk2aFDSwW7duuvbaa8PbWFqWtgIEGtL20tNxBBBAAAEEEEAAgXQWePNNuS+sXuCee6SqVdNZIzn6/oyb5zJo0KCgsdddd51usUgRBYEQChBoCOFFoUkIIIAAAggggAACCMRTYONGqU0bX8OZZ0pXXx3P2jh3LATeeecdde3aNTiVTZWwKRMUBMIqULwgDatQocIfDsvMzAy2lShR4g/PWbISCgIIIIAAAggggAACCIRD4I47pAULpDJlpMGDw9EmWrFnga+++ko33XST7DvXCSecoLFjx6pYsWJ7PoBnEChkgQIFGiJBhdzantdzue3PNgQQQAABBBBAAAEEEEicwEcfSaNG+fp69pQOOyxxdVNT9AILFy5U8+bNtdENQ7HlKydPnuwCRC5CREEgxAJRBRredBO59iWQcNBBB4WYgqYhgAACCCCAAAIIIJDaAtu2Sa1aSTt3SvXqSS1bpnZ/k713q92yIBkZGVq2bJkOPPBATZ8+XQcccECyd4v2p4FAVIGGY445Jg1I6CICCCCAAAIIIIAAAqkp0Lev9PXXks12HjpUKkrGttBe6C1btqhFixaaP3++ypYtG6w0UaNGjdC2l4YhsKtAVG8t/dyaN927d9+nUQ27Vs5jBBBAAAEEEEAAAQQQSIzAf/+bk4/BFis46qjE1Est0QtkZWWpXbt2+uyzz1S8ePEgJ4PlZqAgkCwCUQUaRo8erfvvv59AQ7JcXdqJAAIIIIAAAggggIATcN9bgykT27dLderIfYmFJcwCd955p15//fWgibac5bnnnhvm5tI2BP4gEFWg4Q9HswEBBBBAAAEEEEAAAQRCL2DTJGbP9lMl7HEuC8WFvg/p0sAHHnhAU6ZMCbpro8mbNm2aLl2nnykkQKAhhS4mXUEAAQQQQAABBBBAYHcBN8Vfffr4rW3byi2PuPse/B4WgaefftotN+rXG7UkkJ07dw5L02gHAlEJEGiIioudEUAAAQQQQAABBBBIHgFbXeKmm6TNmyXLI9i1a/K0Pd1a+u9//1vdunULun3++edr4MCB6UZAf1NIIKpVJyL9XrRokUqXLh35Nd8/99tvP9kfCgIIIIAAAggggAACCMRfYOxY6e23fT0u1ZrKlIl/ndQQvcB/XabONm3aBLnwTjzxRI0ZM0bFihWL/kQcgUBIBAoUaDjyyCML1Pw+bsxW7969C3QsByGAAAIIIIAAAggggED+BRYvlm6/3e/frJl0+un5P5Y9Eyfw888/q3nz5tq4caNq1aqlyZMnu4AQEaHEXQFqiocAUyfioco5EUAAAQQQQAABBBAoZAFbWWLtWqlaNcktYkAJocDq1atluRiWL1+uAw88UNOmTVPlypVD2FKahEB0AgUa0bB06dICTZ0oVapUdK1jbwQQQAABBBBAAAEEEIha4PHHpZde8ofZVH9mL0dNGPcDtmzZouuvv14//PCDypYtG4xkqGGJNCgIpIBAgQINlmehIDkaUsCLLiCAAAIIIIAAAgggEGqBlSvlVivwTbz0UumCC0Ld3LRsXFZWlv7xj39ozpw5Kl68uMa6ZBrHH398WlrQ6dQUYOpEal5XeoUAAggggAACCCCQpgKdOskNxZcqVZL69k1ThJB3u2fPnpo5c2bQSlvO8txzzw15i2keAtEJEGiIzou9EUAAAQQQQAABBBAIrcCrr0rTp/vm3Xuv3Lz/0DY1bRs2YsQITZ06Neh/jx49dM0116StBR1PXQECDal7bekZAggggAACCCCAQBoJrF8vtW3rO3zOOdLll6dR55Okq0899ZTut3VGXWnmlgLpZMNPKAikoEBUORp69eoVLLtSokSJqCkyMzNlSSSrV68e9bEcgAACCCCAAAIIIIAAAnkLdO8uLVwolSsnDRqU9748m3iBt99+W7fddltQcYMGDTRgwIDEN4IaEUiQQFQjGtq3b6/u7h2sWLFi+W7eYreAb58+fVSzZk2NHz8+38exIwIIIIAAAggggAACCORP4L335BIK+n3dvUF3cy9/x7FXYgS+/PJLN9qkrezma7169fTwww9H9Z0qMa2kFgRiJxDViIb8Vrtz5069+eabwT+gl9y6OpZVlYIAAggggAACCCCAAAKxF3CrJKp1a8l9BNdf/yo1bx77OjhjwQV++umnYBnLjRs3qlatWpo0aZLKlClT8BNyJAJJIBDTQMNKt5bOY489FizP8v3332d3v1q1asE/rpYtW2Zv4wECCCCAAAIIIIAAAgjsu0Dv3tL//ieVKiUNHSoVKbLv5+QMsRFYtWqVMjIy3Cogy1WlShVNmzZNlStXjs3JOQsCIRaISaDhww8/DEYvWHKTrVu3Bt219WAvvvhi3XjjjbrkkkuC9WFD7EDTEEAAAQQQQAABBBBIOoHPP/fBBWt4t25S7dpJ14WUbfDmzZuDm60LFixweTPKafLkyapRo0bK9peOIbCrQIEDDRs2bAiWZRkzZoz+85//ZJ/TRi9Y0sdbb73VJaEhC002DA8QQAABBBBAAAEEEIihgJvur1at5KYpS3/5i9SmTQxPzqn2ScCmjv/jH//Q5y4SZDdgx7oEGscdd9w+nZODEUgmgagDDZbIxIILtvbreltDx5WKFSuqadOmsqkR27dv15lnnumGbDFmK5leCLQVAQQQQAABBBBAILkE7J6ejWiwPO02ZcJ9n6WERKBnz5564403gtbYcpbn2HqjFATSSCCqtyObCvHqq68GPCVLllTjxo113XXX6bLLLlPp0qWD7e+//34a8dFVBBBAAAEEEEAAAQQSL/Dtt1K/fr5etzBcMKIh8a2gxtwEhg8fHtyUted69Oihq6++Orfd2IZASgtEFWiwoT9FixZVu3bt1LdvX1WqVCmlcegcAggggAACCCCAAAJhE7DVJWyVCVttwnIydO4cthamb3uefPJJDRkyJABo1qyZOnXqlL4Y9DytBYpG0/uDDjpIO3bs0KhRo1S3bt1gqsSMGTOC9WCjOQ/7IoAAAggggAACCCCAQMEE3EdxRQYR23fa3wYWF+xkHBUzgXfeeUfdu3cPztewYUMNGDAgZufmRAgkm0BUgYbPPvtMM2fO1DXXXKPVq1cHa8A2atRIhx56qMty201fffVVsvWf9iKAAAIIIIAAAgggkDQCP/8s3Xmnb65Lj6aTT06apqd0Q+17kI1eyHQZOuvVqxesyFfMkmdQEEhTgagCDTZtokGDBnriiSe0ZMkSjRw5UieccIJ+/fVXl4BmqI499thgnViztJEPFAQQQAABBBBAAAEEEIidwM03yyVklw4+WLrjjtidlzMVXOCnn37Sze7C2HKWhx9+eLCMZSR/XcHPypEIJLdAVIGGXbtauXJldejQIViyZc6cOcFjy9nws4VZXRk2bJguvPBCTZs2TRs3btz1UB4jgAACCCCAAAIIIIBAlAKTJ8slZvcH2YoT5ctHeQJ2j7nAypUrg+T4q1at0gEHHBB89yGPXcyZOWESChQ40LBrX0888cRgdMMvv/yixx9/PBj1YCMaXn/9dVkSFMvtcP3118uWxqQggAACCCCAAAIIIIBAdALLlkm33uqPadJEOvfc6I5n79gL2AgG+47z448/qmzZsnrkkUd02GGHxb4izohAEgrEJNAQ6XepUqXUtGnTII/DggUL1KdPH9WsWVMbNmzQlClT9Nxzz0V25ScCCCCAAAIIIIAAAgjkU8ANJJa7ee7umku9e+fzIHaLm0BWVlYwXeKLL75Q8eLFNWLECB1zzDFxq48TI5BsAjENNOza+Ro1arg3wd764Ycf9Oabb+raa69VmTJldt2FxwgggAACCCCAAAIIILAXgRdflJ5+2u/Uv7/kZjBTClngDpcgw77jWLHlLM8444xCbhHVIxAugeLxbk6RIkV03nnnBX/iXRfnRwABBBBAAAEEEEAglQTWrpXatfM9cismqnHjVOpdcvbFkuBbHjort99+u6666iqttQtFQQCBbIG4jWjIroEHCCCAAAIIIIAAAgggUCCBrl3lVnuTKlSQBg4s0Ck4KIYClo/Okt5bsfwMHTt2jOHZORUCqSNAoCF1riU9QQABBBBAAAEEEEghgbffliZM8B2yvAwuvzqlEAXeeuutYASDNeGCCy5Qv379CrE1VI1AuAUINIT7+tA6BBBAAAEEEEAAgTQU2LRJuukm3/HTTpPLd5aGCCHqsiV9bNu2rTIzM1W/fn099NBDKlasWIhaSFMQCJcAgYZwXQ9agwACCCCAAAIIIICAevWS5s+XSpeW7r8fkMIU+Omnn9SiRQttctGf2rVra9KkSe66uAtDQQCBPQoQaNgjDU8ggAACCCCAAAIIIJB4gdmzpQce8PX26CG3XHzi20CNXmClW1P0uuuu04oVK1S1atUgCWSlSpXgQQCBvQgQaNgLEE8jgAACCCCAAAIIIJAoge3bpRtvlHbskI4/XmrVKlE1U8/uAjaCwRI+/vjjjypfvrymTJmiQw89dPfd+B0BBHIRINCQCwqbEEAAAQQQQAABBBAoDIH+/aWvvpKKu0Xo3SqKLg9AYbSCOrOysnTzzTfLcjMUdxdj3Lhx+stf/gIMAgjkU4BAQz6h2A0BBBBAAAEEEEAAgXgKzJ2bs4Rlp07S0UfHszbOnZfA7bffrn/961/BLkNdxOess87Ka3eeQwCB3QQINOwGwq8IIIAAAggggAACCCRawKZK2DSJbdukI4+UOnZMdAuoLyIwZMgQTZ8+Pfj1jjvuUJMmTSJP8RMBBPIpQKAhn1DshgACCCCAAAIIIIBAvAQs+ePHH0tFivgpEyVLxqsmzpuXgAUYhg8fHuxiK0106NAhr915DgEE9iBAoGEPMGxGAAEEEEAAAQQQQCARAgsWKFjO0upq3VqqXz8RtVLH7gI2VcKmTFi58MIL1a9fv9134XcEEMinAIGGfEKxGwIIIIAAAggggAAC8RBo00ZyCxy4FQ2k7t3jUQPn3JuAJX1s27atLAnkSSedpNGjR6toUb4q7c2N5xHYkwD/evYkw3YEEEAAAQQQQAABBOIsMGGC9OabvpLBg6WyZeNcIaf/g4AtX2nLWG7evFlHHHGEJk2apNKlS/9hPzYggED+BQg05N+KPRFAAAEEEEAAAQQQiJnAL79I3br50zVtKv3f/8Xs1JwonwIrVqzQddddp5UrV6pq1aqaOnWqKlasmM+j2Q0BBPYkQKBhTzJsRwABBBBAAAEEEEAgjgLt20tr1sh9wZXuvjuOFXHqXAU2ufkqlvDxp59+Uvny5YMgw6E2f4WCAAL7LECgYZ8JOQECCCCAAAIIIIAAAtEJPP209Pzz/pgBA6T994/uePbeN4HMzMwgJ4PlZihevLjGjx+vY445Zt9OytEIIJAtQKAhm4IHCCCAAAIIIIAAAgjEX2DVKqljR19Po0bSRRfFv05q+L1Ajx499NZbb7nlRIto2LBhbtoK81Z+L8RvCOybAIGGffPjaAQQQAABBBBAAAEEohK45RZp6VI/ioEVFKOii8nO999/v5544ongXHfccYeuvPLKmJyXkyCAQI4AgYYcCx4hgAACCCCAAAIIIBBXgZkzpcmTfRX33CNVqRLX6jj5bgLTpk3TiBEjgq0tW7ZUe0uUQUEAgZgLEGiIOSknRAABBBBAAAEEEEDgjwIbNkht2vjtNlL/qqv+uA9b4ifwpltH1EYwWLnIzVe5995741cZZ0YgzQUINKT5C4DuI4AAAggggAACCCRGwL7jugUOVKaM5EbvUxIo8Pnnn+vmm29WVlaW/vrXv2r06NEqWpSvQgm8BFSVZgL860qzC053EUAAAQQQQAABBBIv8MEHcl9ufb133ikdckji25CuNS5YsEDXX3+9Nm/erCOOOEITJ05UqVKl0pWDfiOQEAECDQlhphIEEEAAAQQQQACBdBXYulVq3VrauVM66SSpRYt0lUh8v1esWKGMjAytckt9VK1aVZajoWLFiolvCDUikGYCBBrS7ILTXQQQQAABBBBAAIHECvTtK82bJ5UsKQ0ZIjdkP7H1p2ttmzZtCkYy/OTmq1SoUCEIMhzCUJJ0fTnQ7wQL8DaXYHCqQwABBBBAAAEEEEgfgf/8Rxo82Pe3SxfpyCPTp++F2dPMzEy1bdtW/3EXoHjx4ho/frzq1q1bmE2ibgTSSoBAQ1pdbjqLAAIIIIAAAgggkCgBl3dQrVpJ7juvjj5aatcuUTVTT/fu3fXWW2+pSJEiGj58uM4880xQEEAggQIEGhKITVUIIIAAAggggAAC6SMwdKj02Wd+qsSwYVKJEunT98Ls6WA3hOTJJ58MmnCny7x5xRVXFGZzqBuBtBQg0JCWl51OI4AAAggggAACCMRT4LvvpD59fA1uBL+OOy6etXHuiMDUqVP1wAMPBL/ecMMN+sc//hF5ip8IIJBAAQINCcSmKgQQQAABBBBAAIHUF7DVJW66SW45RalmTalr19Tvcxh6+MYbb6hnz55BUy6++GL1tSycFAQQKBQBAg2Fwk6lCCCAAAIIIIAAAqkqMHas9M47vne2ykSZMqna0/D06/PPPw9GL2S5xBgnn3yyRo0a5Vb34KtOeK4QLUk3Af71pdsVp78IIIAAAggggAACcRNYtEjq0cOfvnlz6dRT41YVJ/5NYMGCBcEylpvdEJIj3bIeEydOVKlSpfBBAIFCFCDQUIj4VI0AAggggAACCCCQWgKWEmDdOulPf5JcHkJKnAWWL1+ujIwMrVq1StWqVdO0adO0//77x7lWTo8AAnsTINCwNyGeRwABBBBAAAEEEEAgHwLTp0uvvOJ3HDhQqlAhHwexS4EFNm7cGIxk+Omnn5x1BVkiyOrVqxf4fByIAAKxEyDQEDtLzoQAAggggAACCCCQaHJPnwAAQABJREFUpgIrVkidO/vOX3aZ1KBBmkIkqNuZmZlq65bz+PLLL92yoSX0yCOPqG7dugmqnWoQQGBvAgQa9ibE8wgggAACCCCAAAII7EWgUyfJgg2VKkn33ruXnXl6nwVuu+02vf322ypSpIiGDx+uM844Y5/PyQkQQCB2AgQaYmfJmRBAAAEEEEAAAQTSUGDGDOnxx33H+/WTKldOQ4QEdnnQoEF66qmnghp79eqlyy+/PIG1UxUCCORHgEBDfpTYBwEEEEAAAQQQQACBXAQs8ePNN/snzjtPsmkTlPgJWB6GBx98MKigVatWzv43/PhVyZkRQKAAAgQaCoDGIQgggAACCCCAAAIImED37pItaVm+vHTffZjEU2DmzJnq2bNnUMUll1yiPn36xLM6zo0AAvsgQKBhH/A4FAEEEEAAAQQQQCB9Bd59Vxo3zvffjeDXwQenr0W8ez5nzhz9w60dmpWVpb/97W8aOXKkihblq0y83Tk/AgUV4F9nQeU4DgEEEEAAAQQQQCBtBbZskVq3lnbulPviKzVrlrYUce/4Dz/8ECxjucWhH3XUUXrsscdUqlSpuNdLBQggUHABAg0Ft+NIBBBAAAEEEEAAgTQVuPtu6bvv5L7wSkOGyK1+kKYQce728uXLlZGRodWrV6tatWqyHA37779/nGvl9AggsK8CBBr2VZDjEUAAAQQQQAABBNJK4LPPpKFDfZe7dZMOPzytup+wzm7cuFHNmzfXzz//rAoVKmjatGmqXr16wuqnIgQQKLgAgYaC23EkAggggAACCCCAQJoJZGZKbrED7dghHXus1LZtmgEkqLuZDrpNmzb673//qxIlSmjChAk6+uijE1Q71SCAwL4KEGjYV0GORwABBBBAAAEEEEgbgUGDpP/8RypWzI9qsJ+U2At0c0NF/v3vf7spKUU0YsQInX766bGvhDMigEDcBAg0xI2WEyOAAAIIIIAAAgikksA330j33ut71L69dMwxqdS78PTlPrdO6NNPPx006K677tJll10WnsbREgQQyJcAgYZ8MbETAggggAACCCCAQDoL2FQJmzKxdatUu7bUpUs6a8Sv75MnTw6WrrQaWrtlPdoyNyV+2JwZgTgKEGiIIy6nRgABBBBAAAEEEEgNgdGjpQ8+8KtLWCJIVleM/XV9/fXX1atXr+DEjRo1Uu/evWNfCWdEAIGECBBoSAgzlSCAAAIIIIAAAggkq8BPP0k9e/rWt2wp/fWvydqT8Lb7M7eUR7t27ZSVlaVTTjklGNVQtChfVcJ7xWgZAnkL8K83bx+eRQABBBBAAAEEEEhzARu9v2GD3NKK0h13pDlGHLo/f/58tWjRQlu2bNGf//xnPfbYYypZsmQcauKUCCCQKAECDYmSph4EEEAAAQQQQACBpBOYNElyI/qDMniwVK5c0nUh1A1etmyZMjIytHr1ah100EGaOnWq9ttvv1C3mcYhgMDeBQg07N2IPRBAAAEEEEAAAQTSUGDpUunWW33Hr7pKOvvsNESIY5c3uGEizZs318KFC1WhQgVNmzZNBx98cBxr5NQIhEdg505p2bLU/Tqeuj0Lz2uIliCAAAIIIIAAAggkoUCHDtKqVdKBB8olJkzCDoS4yZmZmWrTpo2++uorlShRQo8++qjq1KkT4hbTNARiJ/DLL3IjeYq6lWwqaePGIrE7cYjORKAhRBeDpiCAAAIIIIAAAgiEQ+D556VnnvFt6d9fqlQpHO1KlVZ07dpV77zzjooUKaIHHnhAp512Wqp0jX4gkKfAyy9L558vzZpVVIsWFdMbb5TKc/9kfbJ4sjacdiOAAAIIIIAAAgggEA+BtWuLqH17f+YLLpDcSouUGAoMHDjQBXF8FOfuu+/WpZdeGsOzcyoEwilgCWXdy11PPunbd9BBOzVs2Co3JWub27B/OBu9D60i0LAPeByKAAIIIIAAAgggkHoCffpUkA1ttpyE7jsxJYYCk1x2zVGjRgVnvOmmm4LpEzE8PadCIJQCn34qdewo/fyzb97ll0sPP5ylHTssyJCahakTqXld6RUCCCCAAAIIIIBAAQTee6+knniibHCk3X2sVq0AJ+GQXAVee+019erVK3iucePGLu8FiS9yhWJjygi4VCQaOlSywIIFGWzVmnHjpOeekw44IGW6mWtHGNGQKwsbEUAAAQQQQAABBNJN4NNPi6hrVz+E+YwzpGuvTTeB+PX3U3dLt72bj7Jjxw6deuqpevDBB4P8DPGrkTMjULgCCxb4UQyff+7bcfLJciurSEccUbjtSlTtjGhIlDT1IIAAAggggAACCIRS4L33pAsvlM44o6RLzlZcpUvv1P33h7KpSdmo77//Xi1atNCWLVv05z//OVhhomTJkknZFxqNQH4EnnhCathQsiBDUfeN2wbyzJqVPkEGM2JEQ35eKeyDAAIIIIAAAgggkHICr78u9e0rffBBTteOPXa7br11gw47jGUmclQK/mjZsmVq1qyZ1qxZoz/96U/uju40l/vCJb+gIJCCArYcbvfu0quv+s7VqiVNnSq3qkoKdnYvXSLQsBcgnkYAAQQQQAABBBBIHYGdOyVbutKWrJwzJ6df9kWgXbvtOuaY1TkbebRPAhtcmn0LMixcuFAVKlRwX7imBsGGfTopByMQUoF335W6dJGWLvUNbN5cGj1a7rUf0gbHuVkEGuIMzOkRQAABBBBAAAEECl/AkrLZcOYBA6R583Lac955UufOUv360rZtO92d95zneFRwgUwHbqtKzJ07VzZN4rHHHlOdOnUKfkKORCCkAlu3+tVpxo/3DazkBkONHStddVVIG5ygZiVloGHx4sWqWLGiy9rp0nbuoSxfvtxl8jzAzYnJOw2FzRXb6l4d+++//x7O5DfHer88K+NJBBBAAAEEEEAAgZgI2JcAt6Ki7rtPsuRsVooUkS65xAcY6tb12/g7dgI73bCRrl276l13i7eIw37ggQeCBJCxq4EzIRAOAQtauhyn+vZb355zz/XvN4ccEo72FWYrki7Q8Mknn+i2225Tnz59dM455/zOzrLYTpw4Uf/617/c8iE/q0yZMmrQoIFuueUWFS/++67Oc68KG7718ccfB4GGGjVqqEePHjr22GN/d85Y7/e7k/MLAggggAACCCCAQFwENm6U7A6jJXVcssRXYR8HbZk5W8++du24VMtJncDAgQP1zDPPBBa2hOXf//53XBBIKQGbgvXII36E1LZtcqN2pH79pG7dfCAzpTpbwM7kfbu/gCeN12H2pd8CDBZQyK085xYktUCDBRfGjBmj5m5izCuvvOLm4LlJeLuUTZs26a677nL/6SwJnhsyZEgQlLCAxHfffZe9Z6z3yz4xDxBAAAEEEEAAAQTiImBTH+yjX82acjebfJChVCm5VQ980scRIwgyxAX+t5PaZ/HRNjHdlTZt2gTTJ357ih8IpITAr7/6pW/d11I33UqyUVHuXri7GU6QYdcL7OK64S82teHRRx918+qeUCn7nyKXstRl3Rg3bpwaNWqkli1bBnscc8wxwTI6kydPVqtWrXTIb2NYxrvwtmXAtTfBatWqZe97uQtxWx0WhLAS6/2Ck/IXAggggAACCCCAQMwF3KxZWRBh1Chp3Tp/+rJlpeuvl26+WapSJeZVcsLdBF51qfYjn6NtFMPdd9+92x78ikByC/zznz6gEMnl0qGDHzVVunRy9yserU+KEQ3Tp0+X/bnQLXDcs2fPXB1muYVJN2/erAsuuOB3z9sxVuyNL1LefPNNnXjiidlBBttevnx5t3byGXr77beD4IRti/V+dk4KAggggAACCCCAQOwEFi3yIxdsBIMlerQgg0vlFYxmmD1b7osvQYbYae/5TLMddns3Wd1GHp966qlBXgbLz0BBIBUEbCqWSzviRugoSBh70EF+CcuRIyWCDLlf4aQINNjIBMtUe8cddwRTHHLrSmTKQy1brHSXcuihh6pYsWL6/vvvg62WJNLW8d19P3vStm3fvl0//fSTYr3fLk3iIQIIIIAAAggggMA+CsyfL7Vt66dB2EgGNzM2GLVg96RsGLPNlbaAAyX+AvY520YU2yhkW1nCPrfbShMUBFJB4LPP5Kbm+1VrrD+XXir9979yN8FToXfx60NSTJ04+eST9yqwdu3aYIUJW6N397LffvvJnrcS+ZnbKhO2X2QfC05YidV+wcl2+6tXr1768ssvf7e1RIkSwe8rV65UVlbW754L0y+Rtm1zE5MsKENBIL8CdqfDgn3c5civGPtF8vLYUmm83/B6iEbAXjPr3O3tDRs2RHMY+4Zc4Jtvirm75WX1/POl3N1zf8f8oIOydMMNW3TFFVvcNFu50an+T7Rdibzf2HH2WYyydwF7X27WrFnwf3vVqlU10t3itc+H6eZnn42t37ZSHSU1BOyr2PjxZdxSlWXc97IiKlt2p/r23eCmY22RJYPc169AtjpLpKxYsSLUn41t5kDk+1+kzXv7mRSBhr11wp63CGpZNxEvty8vtgym/cOP7Gc/barE7iWyXKbta+ezEqv9dq/Lfl+4cKFbCuW3tVB+2yES/bUPR/Yn7MX+gSRDO8PumG7ti/aNKt186O+eBXi/2bMNz+QuYF8cd/3ymPtebE0GgS++KOG+xJZ302EtX5cPMBx2WKYLMGxyS1Vu0W/3atyH4dj0hv+r9u640Y0nb9eunX755RfZzb6HHnpIBx54YNRfSPZeU3LsYZ+Led0kx7XaWysXLSqqO+/c390U9jeBjz9+m8vvt0aHH57lvvvs7ejonw/768b+H901MJKfHqZMoKGiGxtnkZbciq0eUalSpeAp28+Kbdu9RLaVdhNtYr3f7nXZ7y1c+uNIDoldn7eVNezNOjLCYtfnwvLYorUWkLFlQy3AQ0EgvwLr168PXjORUUP5PY790lcgcoeoaNGiuQZ/01eGnu9NwEYyWBLpyGjBve3P8+EUmDWrqAYPLuGWL8/52Fqnzg43bWK7Lr44y41ote1/vIFUkN7Yh/3I58ncbjYV5JypeowFfi0ng900s39jDz/8sI4//vhU7e5e+2WvG/tsE7lpuNcD2CG0As8+W8yNXCjpvi8Wce8vO3XrrdvVq9d2972nXEzbbO83FqyzYu839jknrMVe19EGQ3LescPaq3y2KxI9tYsVGZkQOdSGTR599NHBr7afFdu2e4lssy/4sd5v97rs93PPPfcPm+0DtQUa7Mv77v34w86FuMFeaNZWe0MNczsLkYiq9yBgH/wtmMd/xHsAYvMfBGykmgU37T9g3m/+wMOGPATsg78FGsqUKZPHXjwVVoHXX5fuvVdy+b6zS716kmV5v+AC+0BuIxtiW+yzTSTQwI2UPdvanc1OnTrpgw8+CEYT23SJc845Z88HpMEzlueNG3DJfaFXr5a6d5dsZQkrNWtKU6YUcQsGWL6R2OccsWBdJNBgn2/CHGiwYGJkxL/Z5KekTKAhskylJXKsa4uZ/laWLFkSDO0/4ogjgi32gcPyLth+uxfbZl+ca9SoEXwwieV+u9fF7wgggAACCCCAAAK/F7Apy88/L/XvL82Zk/PcaafJfbGVzjwzZxuPCk9ggFve47nnngsa0Lt3bzVu3LjwGkPNCMRA4N13/Uo1v/7qT+bSjripEnIjzGNw8jQ9RXjHZ0R5QSyKapGWN95443dHzpw5M4gOnX322dnbbQnMTz/9VKstbPVbsej1u+4VZktcWjDCSqz3+60qfiCAAAIIIIAAAgjsImBznqdOldxCY7ryypwgw3nnSS+9JD39NEGGXbgK9aGtKGG5GKy0dct+3GTr/VEQSFIBS8vXt6907bWSBRlslv0TT9hIBoIM+3pJUybQYDkYLrroIj377LOaNm2aFixYEERaJ06cqGuuuUaREQ0GdtVVVwXDvLq5dY+++OILzZ071w2T6R4EJLp06ZJtGuv9sk/MAwQQQAABBBBAAAE3FFcaN0466iipeXNp3jyX5tHleWzUSO7mkTR5slS/PlBhEfinG1N+9913B8251K3xd9ddd4WlabQDgagFvvlGLpGs3KoS/lC7L20LArqvjpQYCKTM1Amz6Ny5c5ANc/z48RozZowqV66sv//972rVqtXvqA466CC3LNIDLnrVVx07dgwCDMcdd1yQGyGSm8EOiPV+v2sEvyCAAAIIIIAAAmkqYPnP3Mc13X+/5Ga5BsXll3bLU/ocDLVrpylMiLv9ySefuPwYHYJVXE5zc1nss3Ruq72FuAs0DYFAwKZoPfqon6JlwU6X5zAY1XDbbXLfC0GKlUARl8wlZwHPWJ21kM9jiSpsmZ3DDjssCCLk1ZylS5cGSekiq1Lsad9Y77eneiwJ0bHHHqsPP/wwCJTsab/C3m6JMy15iU0zsYAOBYH8Cti/Jfv3RjLI/Iqxn60ItHbt2iDJVpUqVQBBIN8Cti65JdgiGWS+yeK+45o1ft7ziBGSuzxBsRmrTZvKrWAgVa8e9ybkWYF9DltjjXSlatWqee6bTk9+9913shEM9l5cp04dvfDCC8EKaelksLe+Rv6fImnx3qQK93n3MVQ2gN1yMlix9QLcYHideKL/PZF/WzLI5cuXB1VavsEwJ4McOnSoLKG75WTJb0mpEQ2RTtuX35qWJjQfJZJEcm+7xnq/vdXH8wgggAACCCCAQKoI2GdpCy6MGmUrf/le2erY118v3XyzRAwxvFfabhBkZGQEQYaDDz44mKJsy7BTEEg2gVdflWzUQiRNX7t20pAhcsHoZOtJcrQ3JQMNyUFPKxFAAAEEEEAAgdQWWLRIcjfCgjwMbnBSUCzZ2g03SK1b+8RrqS2Q3L1bv369mrn0+4sXL3bZ9/dzCTunBlOLk7tXtD7dBOy9x1KLPP6477kbPBBMnbj44nSTSGx/CTQk1pvaEEAAAQQQQACBlBeYP18aPFhyObnlZiMExUYttGkjtWghN6Ul5QmSvoPbt293waDW+vrrr4PpjrbaxJ///Oek7xcdSC+Bzz/3eV9+/NH321ZinTCBUVSJeBUQaEiEMnUggAACCCCAAAJpIOAW8tLAgX55uKws32E32l42RNmWjytdOg0QUqCLlsLtlltu0fvvvx/MGx85cqROOeWUFOgZXUgXAXv/cS9bDRsm2WObqmWjq2yqFiUxAgQaEuNMLQgggAACCCCAQMoKzJ7tAwwuR6BbAcx38/DDfYChSROpRImU7XpKdqx///56/vnng75Z8rdGtt4oBYEkEfj5Z7mVBaVPP/UNtiVyLeEjA3ISewEJNCTWm9oQQAABBBBAAIGUEXjvPb9E3Ouv53Spbl2/goRbYZyl4nJYkubRo27dv4cffjho783u9q9Nn6AgkCwCTz8t3Xmn3Op4csuvSj16+KUrCXYm/goSaEi8OTUigAACCCCAAAJJLfDaa1K/ftKsWTndqFfP30Vs2DBnG4+SS2DGjBnZy9dddtll6tWrV3J1gNamrYCtSmtBhVde8QQ1akhTpkhnnpm2JIXecQINhX4JaAACCCCAAAIIIBB+AZsSYaPp3ah6zZmT097TTpM6deIDfY5Icj76+OOP3XDzjtqxY4dOP/10txzpCHdH2N0SpiAQcgGXSkRduki//OIbet110kMPSfvvH/KGp3jzCDSk+AWmewgggAACCCCAwL4IZGb65I4DBkjz5uWc6bzzpM6dJZv/TElugf/9739uydEbtHXrVh199NEuK/8El1eDxBrJfVVTv/W2oo2tbvPbTJ8gsGCPLfEspfAFCDQU/jWgBQgggAACCCCAQOgE3HdOTZok3XeftGCBb57d4L7kEh9gsFwMlOQXWLp0qZo1a6a1a9eqevXqLmneNFWoUCH5O0YPUlrAxcbUvr3c8qu+m2edJU2eLB12WEp3O6k6R6AhqS4XjUUAAQQQQAABBOIrYEnUxo2ThgyRlizxdRV3nxivuMKvR1+7dnzr5+yJE1i/fr0yMjK0ePFidzd4f02dOlXVqlVLXAOoCYEoBWwK18SJ0r33yo3Akey9yR53707y2Sgp4747gYa4E1MBAggggAACCCAQfgFLpjZ6tNzcfGnFCt/eUqWkpk39nUN3s5uSQgLbt29Xq1at3HSYeSrlLvRjjz2mo446KoV6SFdSTWDZMumWW6R//9v3rE4dv2ylJaKlhE+AQEP4rgktQgABBBBAAAEEEiawfLkPLowaJa1b56stW1a6/nrJrW6oKlUS1hQqSpDATndbuIvLnjfLLRtStGhRjRw5Un/7298SVDvVIBC9gC2h27WrtHq1P9bem4YNk8qUif5cHJEYAQINiXGmFgQQQAABBBBAIFQCixZJQ4f6aRKbNvmmVawolxRQat1asseU1BS41401f+GFF4LO9enTx+XdcIk3KAiEUMDem9xL1OUO8Y2rWlV69FGfKyaEzaVJuwgU3+UxDxFAAAEEEEAAAQRSXGD+fGnQIJ/o0bK2W7FRC23aSC1aSOXK+W38nZoCjzzyiMaOHRt0rl27dsH0idTsKb1KdoEvvvB5YSLJaC0eZkEGCzZQwi9AoCH814gWIoAAAggggAAC+ywwd640cKBfqjIry5/u4IMl910zWA6udOl9roIThFxgxowZuueee4JWXn755erZs2fIW0zz0lHA3p8sX4wlpLXHNj3CHtt7FSV5BAg0JM+1oqUIIIAAAggggEDUArNn+wCDjZS3jO1WDj/cf2hv0kQqUcJv4+/UFvjoo4/UoUMH7dixQ2eccYaGDx+uIrZeKQWBEAksXCh17CjZ+5YVS/Ro0yYs8SMluQQINCTX9aK1CCCAAAIIIIBAvgTee0/q31+yJGqRUreuX0Hi739nKbiISTr8/Pbbb13ujRu0zc2VqeteBDZ9ogQRpnS49EnVx2eflRtlI23YIBcEk267TerXj2BoUl3EXRpLoGEXDB4igAACCCCAAALJLvDaa/7DuVtQILvYXUG7S9iwYfYmHqSJwK+//qpmzZq5FUXWqbpbo3Tq1KmqUKFCmvSebiaDwNq10u23Sy+95Ft72GHS5MnSWWclQ+tp454ECDTsSYbtCCCAAAIIIIBAkgjYlIjnn/cjGObMyWn0aadJnTpJZ56Zs41H6SOwfv16ZWRkaMmSJdp///3dEPRpqlatWvoA0NPQC3zwgdS5s9xr1Df12mulhx5i1ZvQX7h8NJBAQz6Q2AUBBBBAAAEEEAijQGamT+44YIA0b15OC887z394r18/ZxuP0ktg+/btuvHGG/XNN9+oVKlSmjhxoo488sj0QqC3oRWwFW8GD5bGjPG5Y1wcTKNGyY2+CW2TaViUAgQaogRjdwQQQAABBBBAoLAFtm71y1Ped58UWfrN5jQ3auRHMFguBkr6Cux0Q1w6u9vEH7jbxUWLFnVf4Ebp5JNPTl8Qeh4qge++88tWfvWVb5aNuJoyRapRI1TNpDH7KECgYR8BORwBBBBAAAEEEEiUwMaN0rhxfqm3yFDj4u7T3BVX+A/utWsnqiXUE2aBvn376sUXXwyaaI8vvvjiMDeXtqWRgBtYo3vvlbZskey9y1ZbtfwMLh5GSTEBAg0pdkHpDgIIIIAAAgiknsCaNX5d+REjpBUrfP/caHg1bepXkXA5/igIBALjx493wSgXjXKlffv2wWoTwS/8hUAhCixfLt16q/TWW74Rf/6zX7aS6V2FeFHiXDWBhjgDc3oEEEAAAQQQQKCgAvbh3IILNnfZLRoQlLJlpeuvl26+WapSpaBn5rhUFHj55ZfdHWJ3i9iVK9wwlzvuuCMVu0mfkkxg5kypa1dp1Srf8DZtpOHDJXsvo6SuAIGG1L229AwBBBBAAAEEklRg0SJp6FA/TWLTJt+JihXlkvtJrVqRkT1JL2tcm/3hhx+6FUY6yfIznOkmvQ933+SKWOIOCgKFJLB5s58aYfkXrFhgdMIEqXFj/zt/p7YAgYbUvr70DgEEEEAAAQSSSGD+fGnQIJ/o0bKyW7EP53YHsEULqVw5v42/EdhV4Ntvvw1WmNjmXjR1XSZQmz5R3CbAUxAoJIEvv/R5Y+w9zYqlCXn0UbnlVf3v/J36ArwDpf41pocIIIAAAgggEHKBuXOlgQP9UpVZWb6xBx8stWsn2brypUuHvAM0r9AEfvnlF2VkZLipNet0yCGHaNq0aapQoUKhtYeK01tgxw6fT2bIEMmW3y1Txi9j2aFDerukY+8JNKTjVafPCCCAAAIIIBAKgdmzfYDhhRf8WvLWqMMP9wGGJk2kEiVC0UwaEVKB9evXq1mzZrJgQ0U3t8aCDFWrVg1pa2lWqgvYlK+OHaVPPvE9PfFEn/Dx6KNTvef0LzcBAg25qbANAQQQQAABBBCIo8C770oDBkivv55TiRvxLrvrZ/OXWeotx4VHuQts3749WFHim2++USm3BMlEt27gEUcckfvObEUgzgLPPy+XfFRysS+XG0Tq1k3q108qWTLOFXP60AoQaAjtpaFhCCCAAAIIIJBqAq+95j98z5qV07N69fxdwIYNc7bxCIG8BCzhoyV+tASQRV1UavTo0frrX/+a1yE8h0BcBNau9QGGF1/0pz/0UGnyZOnss+NSHSdNIgECDUl0sWgqAggggAACCCSfgPtOKLvb17+/NGdOTvtPO03uy6LcCgE523iEQH4EbAnLl156Kdj13nvv1UUXXZSfw9gHgZgKuDhX8B62ZIk/7TXXSGPGsCpOTJGT+GQEGpL44tF0BBBAAAEEEAivgCVCe+IJP0Vi3rycdp53ntS5s1S/fs42HiGQX4Fx48YFq0rY/h3cXJuWLVvm91D2QyAmAm7Wju6/X3roIZ9bZr/9pJEjpeuvj8npOUmKCBBoSJELSTcQQAABBBBAIBwCW7fKzZf3y1QuWODbZHOWGzXyd/8sFwMFgYII2CiGvn37BodeeeWVuv322wtyGo5BoMAC338vtW8vffWVP8UZZ0hTpkg1axb4lByYogIEGlL0wtItBBBAAAEEEEiswMaNkrvZLFvWLTKUuLj7pHXFFT7JY+3aiW0PtaWWgOVj6OyGwlh+hv/7v//TsGHDXNI9F8GiIJAgAcu94GbtaMsWqVgxqU8fn5/BHlMQ2F2AQMPuIvyOAAIIIIAAAghEIbBmjV83fsQIacUKf6BbBEBNm/o7f9WrR3EydkUgFwFbWeKGG27Qtm3bdMwxxwRTJ4pbFIuCQAIE7H3t1lulf/3LV3bkkX7ZSvKPJgA/iavgHSqJLx5NRwABBBBAAIHCE1i+XLLgwqhR0rp1vh1ly/p5yjffLFWpUnhto+bUEfjll1/UrFkzt2zgeh3qUvpPnTpV5cuXT50O0pNQC7z5pg8yrFzpm3nTTdLw4VK5cqFuNo0LgQCBhhBcBJqAAAIIIIAAAskjsGiRnx4xfry0aZNvd8WK0o03Sq1akXE9ea5k+Fu6zkWwLMhgwYaK7kVmQYaqVauGv+G0MOkFNm+W3IImmjTJd+XAA6VHHpEuvTTpu0YHEiRAoCFB0FSDAAIIIIAAAsktMH++T/BoH7zdCPag2KiFNm2kFi24w5fcVzd8rbdpEje66JVNmyhdurT7wjdJRxxxRPgaSotSTuC///XTvuw9z8qFF0qPPSYddJD/nb8RyI8AgYb8KLEPAggggAACCKStwNy50sCBfqnKrCzPcPDBUrt20rXXyn0JTFsaOh4nAUv42KlTJ1kCyKJFi2r06NE66aST4lQbp0XAC+zYIT38sDR4sGTL89p726BBUseOcolHUUIgOgECDdF5sTcCCCCAAAIIpInA7Nk+wPDCC36teOv24Yf7AEOTJlKJEmkCQTcTLnCPS+3/8ssvB/X269fP3VF2t5QpCMRRYPFiv/zuRx/5So4/Xpo+XWI53jiip/ipCTSk+AWmewgggAACCCAQncC770r9+0szZ+YcZx+2O3SQGjeWu8Ocs51HCMRaYOzYscGqEnbeju5Wcgubl0NBII4CFky9/Xa5hKN+5IKtMDFggFSyZBwr5dQpL0CgIeUvMR1EAAEEEEAAgfwIvPaa5G4ea9asnL3r1fPDhhs2zNnGIwTiJfDiiy+6BHwuA58rTdywmdvt2x8FgTgJ2Go5d94pPfecr+CQQ3zyx3PPjVOFnDatBAg0pNXlprMIIIAAAgggsKuAmwqv55/3IxjmzMl55rTT/DDiM8/M2cYjBOIp8MEHH6hz586y/AxnnXWWhg4dGs/qOHeaC3z8sQ+i2pQJK1ddJbnBNKpUyf/O3wjsqwCBhn0V5HgEEEAAAQQQSDoBS3T2xBN+ePC8eTnNP/98H2CoXz9nG48QiLeArSxhK0xs375df/nLXzRu3DgVL87H9Hi7p+P53UvMBbGkUaN87pkKFaSRI/3KOenoQZ/jJ8A7WPxsOTMCCCCAAAIIhExg61Zp4kSfSX3BAt84y6beqJEPMJD4LGQXLA2as2TJEmVkZLj58et16KGHasqUKSpfvnwa9JwuJlrg++99rhlbvtKKjdyaOlWqVcv/zt8IxFKAQEMsNTkXAggggAACCIRSYONGubvE0pAhkvteFxS7YXzFFf6Dd+3aoWw2jUpxgXVuknyzZs3066+/uiHrlTRt2jRVrVo1xXtN9wpDwMWv1KePtGWLVKyYdPfdPj+DPaYgEA8BAg3xUOWcCCCAAAIIIBAKgTVrpGHDpBEjpBUrfJNKlZKaNpXat5eqVw9FM2lEGgps27ZNN9xwg7799luVLl1akyZNUm0iXmn4Sohvl1eulLp2ld54w9dzxBFyAS3p5JPjWy9nR4BAA68BBBBAAAEEEEg5geXLLf9CWU2YUDpYss06WLasn4fctq1UpUrKdZkOJZHAjh07gqUrP/roI3d3uZgeeugh1ScxSBJdweRo6r/+JdlSlZEga6tW0gMPSOXKJUf7aWVyCxBoSO7rR+sRQAABBBBAYBeBRYv89Ijx46VNm1xkwZWKFeUS7Un2IdseUxAobIF77rlHr7zyStCMfm5N1QsuuKCwm0T9KSSwebNfqtfy0Vg54ADJ3hMvv9z/zt8IJEKAQEMilKkDAQQQQAABBOIqMH++T/DoRp/LjUgPygEH7HDBhSy1bl2CO3hx1efk0QiMGTNGjzzySHBIp06ddP3110dzOPsikKfAV1/5aWGW+NFKw4Y+Ae6f/uR/528EEiVAoCFR0tSDAAIIIIAAAjEXmDtXGjjQL1WZleVPf/DBUrt20oUXrnEJ9sq6+e8lYl4vJ0SgIAIvvPCCbASDlauuuko9evQoyGk4BoE/CLjZOHIxLA0eLLdMqtz7nn9v7NxZspV1KAgkWoBAQ6LFqQ8BBBBAAAEE9llg9mz/Idp9b9POnf50hx/u7+RdeaVUwsUWVq3a52o4AQIxE5g1a5a6dOniXq87dfbZZ7sVUNwSKBQEYiCweLFkAYUPP/QnO+44n/DxL3+Jwck5BQIFFCDQUEA4DkMAAQQQQACBxAu8+67Uv780c2ZO3XXr+iUqGzeWihbN2c4jBMIiMG/ePDeNp5W707xdxx57rFtqdZyK2/qqFAT2UeDFF6Xbb5fcSqnByAUXywpGednqOhQEClOAd7jC1KduBBBAAAEEEMiXwGuv+eRm7qZwdqlXTy5zv5+DnL2RBwiETGDJkiXKyMhwq5+s12GHHaYpU6a4nCGk/Q/ZZUq65riXk+68U3r2Wd90W6p34kTp/POTris0OEUFCDSk6IWlWwgggAACCCS7gE2JeP55P4Jhzpyc3px2muRy6OnMM3O28QiBMAqsXbs2CDIsXbrU5QuppGnTprmlVVlbNYzXKpna9MknPshqq+xYselibpCMKlf2v/M3AmEQINAQhqtAGxBAAAEEEEAgWyAzU3r8cT/81404zy52p84CDPXrZ2/iAQKhFdjmlj+54YYb9L///c8l5iutyZMn63BLJEJBoIAC9t44bJg0cqRkyR/Ll5cefFDudVbAE3IYAnEUINAQR1xOjQACCCCAAAL5F9i61Q/9HTRIWrDAH2fZ0hs18gEGy8VAQSAZBHa4b4EdOnTQxx9/rGLFiunhhx9WPZvrQ0GggAI//OBz0fznP/4Ep54qTZ0qF7wq4Ak5DIE4CxBoiDMwp0cAAQQQQACBvAU2bvTDfi0Jv5vOHhTLk3fFFf6Dde3aeR/PswiETaBPnz6aMWNG0KwBAwaoYcOGYWsi7UkiATfjRr17S5s3ywWupF69pLvu8o+TqBs0Nc0ECDSk2QWnuwgggAACCIRFYM0aafRoacQIacUK3yrLlN60qV+m0pKbURBINgEbvTBhwoSg2Z3dmoPNmjVLti7Q3pAI2BK9XbvmrLJjQVcbxXDKKSFpIM1AIA8BAg154PAUAggggAACCMReYPlyafhwH2SwJdmslC0rtWghtW0rlyzPb+NvBJJN4HmXvbS/rb/qytVXX63u3bsnWxdob0gE3n5buuUWyd4vrVgeBsvHYHkZKAgkgwCBhmS4SrQRAQQQQACBFBCwDOk2PWL8eGnTJt+hihWlG2+UWrWS7DEFgWQVeP/9990Xw1u00y2Xcs455+j+++9P1q7Q7kIU2LLFL+X72GO+EbaShL1n2lQyCgLJJECgIZmuFm1FAAEEEEAgCQXmz5csweOkSZJLxB8UG7XQpo0fxVCuXBJ2iiYjsIvA119/rdatW2v79u067rjjNHbsWBW3RCMUBKIQmDvXTxv77jt/UIMGPkHuwQdHcRJ2RSAkArwDhuRC0AwEEEAAAQRSTcA+NA8cKD3xhJSV5XtnH5jbtZOuvVZuyb9U6zH9SUeBxYsXB3kY1q9frxo1agTLWJYjepaOL4UC99kNgnHBKem+++SCVZLlqrH3zi5dJFt5h4JAMgoQaEjGq0abEUAAAQQQCLHA7NmSS7SvF1+UG0buG2pLsLVvL115pVSiRIgbT9MQiEJg7dq1ysjI0NKlS1XZjXGf5pYHqEKSkSgE2dVW2rGAwqxZ3uIvf5GmT5eOPRYbBJJbgEBDcl8/Wo8AAggggEBoBN59Vy4RXk6GdGtY3bp+icrGjaWiRUPTVBqCwD4LbN26VS1bttR3bpx7mTJl3NSgSapVq9Y+n5cTpI/Ayy9LPXpILl4VjFzo1MlPM7MRDRQEkl2AQEOyX0HajwACCCCAQCELvPaaT14WuSNnzalXT+rYUWrYsJAbR/UIxEFgx44d7vXdUZ988omKFSsmW9Kynr3oKQjkQ2DDBqlXL+npp/3ONqVs4kTJcjJQEEgVAQINqXIl6QcCCCCAAAIJFLApEW4lv2AEw5w5ORWffroPMJx5Zs42HiGQagK9e/fWjBkzgm4NcPOEGvANMdUucdz6Y1PLLAi7cKGvwlaTGDdOOuCAuFXJiREoFAECDYXCTqUIIIAAAggkp0BmpvT44z5R2bx5OX04/3zJhv3Wr5+zjUcIpKLAQw89pEcffTToWhc3ub5Zs2ap2E36FGMBe+8cMUJ64AHJDYhR+fL+d1val4JAKgoQaEjFq0qfEEAAAQQQiLGAm44eDO21ZSoXLPAnt2zojRr5AIPlYqAgkOoCzz33nEt06jKdunLNNdfotttuS/Uu078YCNh7ZocO0hdf+JP97W/S1KnSEUfE4OScAoGQChBoCOmFoVkIIIAAAgiEQWDjRj+sd8gQybKjWynuPj3YcF/74Fy7tt/G3wikusB7772nW265xa2kslPnnnuuBg8enOpdpn8xELARYHfdJW3e7BPiWm4G+93eRykIpLIAL/FUvrr0DQEEEEAAgQIKrFkjjR7th/auWOFPYpnQr71WatdOql69gCfmMASSUODrr7/WTTfdpEw3/v3444/X2LFj3RdFPkYn4aVMWJNXrZIb8SJZslwrtsSvjWI49VT/O38jkOoCvEOm+hWmfwgggAACCEQhsHy5NHy4DzKsW+cPLFtWatFCattWqlIlipOxKwIpILBo0SJlZGRo/fr1qlGjhiZPnqyy9o+CgsAeBN55R3LpO7Rsmd/B3j9HjpQqVNjDAWxGIAUFCDSk4EWlSwgggAACCEQr4L5LyaZHjB8vbdrkj65YUbrxRsmSldljCgLpJrDGDe2xIMMy942xcuXKmjZtmg488MB0Y6C/+RSwXDaWx2bCBH+Ae8m40S9Skyb5PAG7IZBCAgQaUuhi0hUEEEAAAQSiFZg/338wnjRJ2rbNH22jFtq08aMYypWL9ozsj0BqCGx13xpbtmyp77//XmXKlAlGMtSqVSs1OkcvYi7w7bdF1b17GX33nT/1eedJ9r7KNLOYU3PCJBEg0JAkF4pmIoAAAgggEEuBuXP9EpVPPCFlZfkzH3ywz79geRhKl45lbZwLgeQSsISP7du31+zZs1WsWDGNGTNGJ554YnJ1gtYmRMC9VIKRYAMGlNf27UVkuWz695duvVWylXkoCKSrAIGGdL3y9BsBBBBAIC0F3Pcmtzyf9OKLctnzPYElKXPfqXTllVKJEmnJQqcR+J3Afffdp1dffTXYNnDgQJ1//vm/e55fEDCBX36RW4lEcguSuFJEdepk6ckni+m44+x3CgLpLUCgIb2vP71HAAEEEEgTgXff9XfZZs7M6XDdun6JysaN/bJrOc/wCIH0FXjkkUf01FNPBQC2nKXlaKAgsLvAK69IPXpItkKPjVxo3XqLC+Jmuhwe5Xffld8RSEsBAg1pednpNAIIIIBAugjY0mr9+kmzZuX0uF49qWNHqWHDnG08QiCdBSzZ4zPPPBMke/zxxx8DiqZNm6pbt27pzELfcxHYsEG6+265kQv+yT/9SXrsMenkkze7EWEMCcuFjE1pKkCgIU0vPN1GAAEEEEhdAZsS8dxzforEnDk5/Tz9dKlTJ+mMM3K28QiBdBXYvn273njjjWD0wltvveVylfyWrMSBXHjhhW71ALd8AAWBXQQ+/dQHaX/+2W+87DKfn8EWIlm9epcdeYgAAiLQwIsAAQQQQACBFBHIzJQef9wneZw3L6dTNr3cAgz16+ds4xEC6SrwzTffuLvRT+rZZ5/VypUrsxmqVaumSy+9VBdddJFq1qyp4sX5mJyNk+YP7L31gQekESOkHTskW41n+HDpppvSHIbuI5CHAO+geeDwFAIIIIAAAskgYGu3T5zol6lcsMC32OYMN2rkAwyWi4GCQDoLrFu3ziVAfVFPuGVWvvjii2wKCyY0aNBANk3inHPOCUY1rLFJ9xQEfhOwmTQ21SwyOuzkk6WpU6Ujj4QIAQTyEiDQkJcOzyGAAAIIIBBigY0bpXHjpCFDpCVLfEPtJuwVV/gkj7Vrh7jxNA2BOAvYEpWzXHISG70wY8YMbbWI3G+lTp06QXDhSrfUSuXKlSObfzd9InsjD9JWwJb/vesuadMmnzC3Z0+pd2+50S5pS0LHEci3AP9M8k3FjggggAACCIRDwG64jhrlh/KuWOHbZGu3X3ut1K6dVL16ONpJKxAoDIGFCxfq6aefDgIMixYtym7CfvvtF0yNsNELJ5xwQvZ2HiCwu8CqVVL37nJLnPpnatWSpkyRLM8NBQEE8idAoCF/TuyFAAIIIIBAoQssX+7nBY8eLbmR4EEpW1Zq0UJq21aqUqXQm0gDECgUgS1btrgvha8GUyNsFIONZrBStGhRnXbaacHohYsvvlilLCJHQSAPAVsKuEsXaelSv1Pz5j6w6+JUFAQQiEKAQEMUWOyKAAIIIIBAYQjYTVmbHjF+vB/Ca22oWFG68UapVSv/uDDaRZ0IFLaA5VuwqRHPP/+81q9fn92cww47TFdddZWuueYaN8KHIT7ZMDzYo4DNrBk40L/P2k6VKkljxkhXX73HQ3gCAQTyECDQkAcOTyGAAAIIIFCYAvPn+wSPkyZJ27b5ltiohTZt/CgGy3xOQSDdBFa4+UIWWHjcLbHy7bffZne/dOnSwYoRNjXidDfGvYhlRKUgkA8BW6WnQwfJLUgSFJcXVJMnS4ccko+D2QUBBHIVINCQKwsbEUAAAQQQKDyBuXOlAQPk7tTKJafz7Tj4YJ9/wfIwuO9TFATSSiDTrS/473//O5ga8cYbb8h+jxTLt2DBhcsuu0wVKlSIbOYnAnsVsBk2EyZI/fv7YG7JklK/flK3bnKBqr0ezg4IIJCHAIGGPHB4CgEEEEAAgUQKzJ7tAwxuFT43x9zXfPjhUvv2kkuOrxIlEtka6kKg8AW+//57PfXUU0Fyx2XLlmU36MADD3Srq1zhEqBeq6OOOip7Ow8QyK/Ar7/6XAzvveePsGWAp02TSxSa3zOwHwII5CVAoCEvHZ5DAAEEEEAgAQKWfMzuqM2cmVOZfei1obyNG/tl1XKe4RECqS2wYcMGvfzyy8HohU8//TS7s8XdmoJnn312MHqhQYMGbolBPsZm4/AgKoF//tOvKrF6tT/M3mvvv5/RYlEhsjMCexHgHXovQDyNAAIIIIBAvARee80P03VJ8rNLvXpSx45Sw4bZm3iAQMoL2CoRH3/8cRBceOWVV7R58+bsPh955JEuId/VQXLHKiytku3Cg+gFNm6U7r5b7nXmjz3oIOnRR+Vye0R/Lo5AAIG8BQg05O3DswgggAACCMRUwKZEPPecnyIxZ07OqW199k6dpDPOyNnGIwRSXeCXX34JpkU84b75/fTTT9ndtVwLjd1wHls14qSTTsrezgMECipg77c2ciHyMvv736VHHmFZ4IJ6chwCexMg0LA3IZ5HAAEEEEAgBgKWu84lyQ+WT7MM55Fy/vk+wFC/fmQLPxFIbYFtbgmVmW6ekAUX3nnnHe3YsSPosK0S8be//S2YGtGoUSOVKVMmtSHoXUIELKHuAw9II0b45Lply0rDhklt2yakeipBIG0FCDSk7aWn4wgggAACiRCwtdknTvTLVC5Y4Gu0bObue1QwgsFyMVAQSAeBuW45FQsuPOeG9KxZsya7ywe7JVWaNGkSBBhq1KiRvZ0HCOyrgI1esKlon33mz2SDYyzhI/lD91WW4xHYuwCBhr0bsQcCCCCAAAJRC9hc4HHjpCFDpCVL/OGWu85Wj7BVJGrXjvqUHIBA0gmsdtn2XnjhBTea53FZoCFSSrp1BC+44IJgasRZZ52lokWLRp7iJwIxEXCLlahXL8neiy24e8cdUp8+rN4TE1xOgkA+BAg05AOJXXIXWLasqPvwXFy1akkHHCBVriwVK5b7vmxFAAEE0kXAbtSOGuWH6a5c6XtdqpTcMnxSu3ZS9erpIkE/01XApkK865ZSsdELr7mMp9u3b8+mOOaYY4KRC1e6iNv++++fvZ0HCMRKwFaS6NFDmjHDn7FmTWnKFPLfxMqX8yCQXwECDfmVYr8/CLzxRinddtt+2dstWmyfGSJBB/u5pz8WlIg85/I9URBAAIGkF1i+XBo+XBo9Wlq3znfH5gK3aOHnApMsP+kvMR3Yi8CPP/6oJ598Uk+5W8m//vpr9t6VKlXS5Zdf7oJt16ouc4WyXXgQe4H33pO6dJF7/flzZ2RIDz0k7ZfzcTX2lXJGBBDIVYBAQ64sbMyPwNq1Rd1QtJ3audNFGFyxTOp2J8/+zJ+fnzP4fUqUyAk6RIIPuwYiItsiPyPP2XEUBBBAoLAFFi3y0yPGj5c2bfKtqVhRuvFGqVUryR5TEEhVgU3uRf/Pf/4zGL3w4YcfZnezmBvi+H//93/B1IgLL7xQJfhPO9uGB7EXsFw4gwZJY8f6c9v77sMPy42eiX1dnBEBBPInQKAhf07slYtAy5ab3Bu4ZYquKBumZn8syBB5nNfPXZbHdkMqfeQ5En3OpapcN9lIiEjwYfefkWDE7tstom0jLygIIIDAvgpYQNU+2E6aJLkk+kGxUQtt2vhRDOXK7WsNHI9AeAU+c9n1bGrESy+9pA0bNmQ3tGbNmkFw4eqrr9ZBBx2UvZ0HCMRL4Ntvfd6byGo+Z58tTZ4sHXpovGrkvAggkB8BAg35UWKfPQrYl3aLGrtRkVEVizznFojYPVCxalXOfmvX+mWJIhWtXy/ZHzdSM9/FErFZEGJPgYhIYGL3521+NQUBBBAwActnN2CA3BDxnPcklzQ/yL9geRhKl8YJgdQUWLZsmZ555pkgwDB/l6GLZd0coUsuuSTIvXDKKaekZufpVegEbCTto49K/ftL9rnS5RdV375y03rlkouGrrk0CIG0EyDQkHaXPBwdti/udqMjmpsd9h+KBRZyC1Dktc2yDUeKrWPvPicFfyLb8vPT7kxGghD2c/dAxK7PRZ63AAz/0eVHl30QSA6B2bN9gOHFF/1UMWv14Yf7O2m2kgQjw5PjOtLK6AQy3X+cb775ZhBceOutt5SVlZV9gpPcWoHXXHONLr30UpVjCE+2Cw/iL7B0qXTLLdI77/i6jj7aL1t54onxr5saEEAgfwIEGvLnxF4hELDREzb1wf5Es8y2Tc2IBCLyGjFh++z6vAUlIsWCFfbn558jW/b+04IMNtJj9yBEJBCR23bbVqbM3s/NHgggkDgBlzw/uGM2c2ZOnZbPrkMHqXFjAoo5KjxKJYFvvvkmSOz47LPPamVk+RTXwapVq6pJkyZBYsfDLdJGQSDBAm4hE3Xr5j/bWdW2mo8tI8znpwRfCKpDYC8CBBr2AsTTyS9gdxnd56LgTzS9sSmnkQBFfn/aiItIcat7uQ9n/k9kW35+2n+U+RkxsWugwgIaLC2aH132QSD/Aq++6gMMs2blHFOvntSpk9SgQc42HiGQKgLr3HIpL7ohO5Z74YsvvsjuVnE377CBe9E3dZn1zjnnHPf/DWtZZ+PwIGEClmy3d29p+nRfZbVqfurExRcnrAlUhAACUQgQaIgCi13TS6B8ecn+RJNMyEaU7joqYtccE7uPmNg1eBFJJGfClihz8WL/J7/ikVwZkeBDfgMV1j8KAgjkCNgUreee81Mk5szJ2X766T7AcMYZOdt4hEAqCOx0L/oPPvggCC7MmDHDzXV3k91/K3Xq1AmmRtgIhsr2HwsFgUIS+PxzqWNHacEC34BGjaQJE6K/iVRIzadaBNJSgEBDWl52Oh0vAbvJE/myH00dFqXfNfCwp8e7BjEsOaZ9KbJiPyPHfP+935afvy1xUqS9u/7MK1Bhz1lSTQoCqSRgU6Uef1waOFCKZC63/p1/vg8w1K+fSr2lLwhICxcu1NNPPx1Mj1hka7T+VvZz8xMt54KNXjjhhBMim/mJQKEI2A2ckSOlYcN88l2Xd1RDh0o331wozaFSBBCIQoCvC1FgsSsC8RKw/zjtT/Xq+a/BpmbsGniwQMPuv0eCD7v+3LIlpw4bSfHLL/5Pzta9P7I8GbsGJnZ/nFugwo6hIBA2Abt5O3GiX6YycqfMRgjZ3TKbImG5GCgIpIrAFvcfwKtuTtCTbsmU999////ZOw84qaqzD7806QJSRKRJsWBDRRE1IAqiggULaj4ToyYmMfaeqNEYTUyMvZckxq7Yu4iKiCgCClIsgNJ7E5AO853/uZ6ZO8sCuzC7O+V5f7+zc+e2Oee5M7Nz/vctTqSO1OpK7k1/sHPbkbhwtPNDr06ppXy55Dk9DuXFkhfDiBHRMCT4Pvmk2S675PSw6DwECoYAQkPBXGoGmm8ElGxSE3q10piEhrjwEF8uTqhQ+Ie8JyRsBHNhvKYWJmZh/aYe5QUhQaJevYbWqFFla9x442JFXKiQ1wUGgUwTUHLXBx+M7ozNnBmdXe9RVY/4wx/M2rbN9CtyPghUHIHRo0f70IiXXnrJVW9amuxICxcbePLJJ/vwiObNmyfXswCBiibgnG3smmvMlC9L4u+VV0alK6nuU9FXhteHQMkJIDSUnBV7QiAvCNSoYbbDDlEr6YB000tiQ3FCRFyoKLqskJBgck1XOao5c6rat9+GtZt/VB6JknhMxPepVy/6YbL5s7NHoRHQe/iee8zuvNNs/vxo9Lp5e9ppUeby0ngVFRo7xptbBFQp4kWXcESJHa/fdhcAAEAASURBVFVBIlgN90/gqKOO8t4L8mKQNwMGgWwhoO/oq64ye+21qEctW5o9/rhZ167Z0kP6AQEIlJQAQkNJSbEfBAqYgH6H1q8ftdatSw5CoRlx8UE/IKZOXeqSjdVyHhFV/LZ4wkxtV4uXFtXdDLUpU0r+usqVsbHSohIk4h4TcYFCIgyWnwTmzTO7/Xaze++NvHE0SoUrnXGG2W9/a97DJj9HzqgKicBa9+U5aNAgHxoxwNVj1fNgyreg0Ijjjz/e6tatG1bzCIGsIaAKPxdemArn/PnPze67T56QWdNFOgIBCJSCAEJDKWCxKwQgUDoCCntQ+Sm1YPPmrXSiRQ2rVs2pARsxefbGBYqSLEuMCKbkUbpbHe5Yh/Wbe9TEc1NCRBAl4kKFBA2FsWDZSUA57lRf/eGHzYKHjUSzs84yO/vsSDzLzp7TKwiUnMBElwX4ueee88kd586dmzywUaNG1rdvX+exc5qLayewPQmGhawioJsS//yn2f33R92SsCCBQUIDBgEI5C4BhIbcvXb0HAJ5S0A329TkMllS0407eUPERYm4t4TWF92udWvWpF5BE1E1l4y9xCaRQRPXIEKUVKioXbvEL8GOW0Bg0iSzm282e+wxs1A+VnlBzjkn8mKA/xZA5ZCsIrDMqauvOf9yhUaMCNnyXA+rumQjhx56qPde6Nmzp3+eVR2nMxCIEVAo5XnnmY0bF61UiIRCJUrz/z92OhYhAIEsIoDQkEUXg65AAAJbTkCJ/NzNO99KcxYlBYyLE5taDkKF8lUEU5JMCRpqEyaEtZt/VF6AuDgRX457TBRdr7AQbOME9GP1b38z5zoelULTns2aRfkXlIeB8JiNs2NL9hNQlYhhw4b50AiJDCtWrEh2ul27dj6p40knnWRNmjRJrmcBAtlK4L//NfvrX82FU0Zls2+4IUr6iJdgtl4x+gWB0hFAaCgdL/aGAATyjIDubKuVJuG6QjMkNpTEYyIIFxIp4qVF9cNK1Q5CxYOSYFWuDLmUbkyICKJE0e2FEI49fHgkMLzyirmSfRHNNm2iChKqJEGm8pK8w9gnWwnMcnWI+7s0/PJemBJLWFPHZcs95phjvPdCp06dsrX79AsCaQQU3XPJJWYffBCtVlSPylaqfCUGAQjkDwGEhvy5lowEAhAoJwLyKtBkXq00ppuPQXgo7jF4TMS3aV2YOOtRz9W++67kr6xJdhAh9FhUiIhvi2/Phcn54MFmN91k5vLeJa1Dh8gV182/yJ+RpMJCrhFY7WJ+lNBR4sKHH37oSgw79ylnqhLRuXNnLy706dPHatasmWtDo78FTOCdd8wuuywS6oXhd7+LygwrRxIGAQjkFwGEhvy6nowGAhDIYgKaD6jJlb+kFsQFiQ/FCRFFRYngZRHzqPZ5KGbPNlMrjckToqgIERciitu27bblU1r0rbcigUFZyoPtu6/ZBReYubB0DAI5S2Cci/951sX+vPDCC+4z71TFn6yZ++JQWIQqR7Rq1Sqs5hECOUFA/5Ouuy7yXFCHFd3z73+bOa0MgwAE8pQAQkOeXliGBQEI5AcBhUuosoVaaUyhGXERYmNCRRAmtF3hIAoLCabqH2qTJ4c1m39UroxNlRbdmFChnBWbM4kubu7lczB8/nlq74MPjgSGQw5JrWMJArlEQILCSy+9ZE8//bRLivdTVjw3gG1c6Z4jjjjCiwvdunVzHjqVc2lY9BUCnsDo0ZGXWfDE6907EhniFalABQEI5B8BhIb8u6aMCAIQgIBp4t60adRKikMT+S0pLaqEmsFU/WPevKiFdSV5VJ6M4CFRNLSjbt2qLr9FLXviidppCTd79IgEBuJ6S0KYfbKNgEIhBrvYH4VGvOP8yRUqEWz33Xf34sIJJ5zgqtrUD6t5hEBOEVC0zz33RKER+t8gjz6VGz733JwaBp2FAAS2kABCwxaC4zAIQAAC+UZA3hMKfVArjWe2SoSWxGOiaOiHfngGk1ihNnVqWBN/3MY9UYvCMpR74fzzzZSLAYNArhFQMkeFRjz33HOmJI/BGjhXoL59+3qBQUIDBoFcJqAy0fqeVqJe2T77mD31lNmuu0bP+QsBCOQ/AYSG/L/GjBACEIBAmRJQ0kjF25a2ot6yZRuGdxQN99DzBQvW25Il690P1bUuU3kNa9u2TIfDySGQcQLLly+3N99803svfPLJJ8nzKxSia9euXlw48sgjXXUU92HCIJDjBBTi9qc/mek7XgL25Zeb3Xgj1X9y/LLSfQiUmgBCQ6mRcQAEIAABCGSCgKvMZ2otWmz6bCtWrHIhHUutiiv30bBhjU3vzFYIZBGBkSNHenHh1VdfdZMuN+v6yVq3bm2nnHKK9evXz4U4uRgnDAJ5QEB5fv74RzOVGZbpu/3xx81cehEMAhAoQAIIDQV40RkyBCAAAQhAAAJlQ2Du3Lm+YoRyL0ycODH5IrVc/b7eLgueBIYuXbok17MAgXwgMHSo2YUXms2cGY3GFUex++83l2MkH0bHGCAAgS0hgNCwJdQ4BgIQgAAEIAABCPxEYK1LODJw4EDvvfD++++76i2p8i37uWylKkl57LHHOg8e58KDQSCPCCiH6S23RKKCEgorx8+995qdfnoeDZKhQAACW0QAoWGLsHEQBCAAAQhAAAKFTuDrr7/2iR1fcEHpCxYsSOJo4hKWnHTSSV5gaEtSkSQXFvKLwIQJUdnKsWOjcf3sZ1GoRGmSCecXEUYDAQjECSA0xGmwDAEIQAACEIAABDZBQPlCXnFB6E8//bSNGjUquWfVqlWtZ8+ePjTisMMO8zlFkhtZgECeEXj0UbO//tVc6WEz99a3v/zF7KqrzFx+UwwCEICAJ4DQwBsBAhCAAAQgAAEIbIJAwvmED3VB6Mq7oOoRKzW7+sl2dfX6lHfhxBNPdMlKG4bVPEIgLwnMm2eu+o+ZixDytvPOZk8+adapU14Ol0FBAAJbQQChYSvgcSgEIAABCEAAAvlLYPr06fbcc8/5Nm3atORAt3WB6Mcdd5wPjejYsWNyPQsQyGcC775rdumlKjkcjfKcc8xuv93M5TnFIAABCGxAAKFhAySsgAAEIAABCECgUAmsWrXK3nrrLe+9MGTIEJM3g6xSpUp28MEHe+8FVY+oXr16oSJi3AVGYMWKKDRCpSpljRubPfKIuQSn0XP+QgACECiOAEJDcVSyYN3ChQtt/fr1WdCT4rsQMmqvdumG1VcMAiUloB/tP7hi25UJ5CwpsoLfL3wX6nuH75uCfzuUCoCqQSxbtsyWL1++2ePGjRtnL774ohcZlIchWLNmzbz3wvHHH29alv3444++hX14zB8C4ftGI+L7xmzcuCou90Idmzy5ir/Ihx++2u66a6k1aZKw+fPz57pnYiT6vtHv4nhoVSbOyznyl0AQsjVCJRSWoJ2ttsIpjmH+V9I+IjSUlFQ571e7du2sLoOlN5vZKpcAqKqprxgESkpAIkPNmjX9e6ekx7BfYRPQDzd95+gfMN83hf1eKO3oJRjI82CbbbYp9lD9sHv55Zetf//+9u233yb30TG9evWyk08+2Q466KCs/vGX7DQLGSGwZs2apDBVyN83utf14INVXGhEFVu7tpLVqJGwv/1trf3+97oJxu++4t5sEjX1u7hGjRrFbWYdBDYgoIn7kiVL/Hp932TzTTj9H9X3Y2kMoaE0tMpxX/3IyeYvKv3wl+kDgftoOb4x8uClNFnUl1W1atXyYDQMoTwIhDuMfN+UB+38eg15Hui7Jv5/Sj/sBg0a5EMjBgwY4CZRa5ODVr4FJXbs27ev1a1bN7mehcIhoP9RwQMm/r4pHAJmLjWJXXCB2bBh0aiVhuTJJytZhw76v83/7o29FySIIzRsjA7riyMQ//+jeV82Cw1VqlQpdf8QGoq76qyDAAQgAAEIQCCvCEyaNMmeffZZ770wd+7c5NgaNWrkhYXTTjvNdtlll+R6FiBQiAReesnsj380U/SQvLiV/PGmm8zdIChEGowZAhDYGgIIDVtDj2MhAAEIQAACEMhaAvJoeP31133uheHDhyf7qbuOhx56qK8a0bNnT0K5kmRYKFQC8t6WwOAiibw1b2722GNm3bsXKhHGDQEIbC0BhIatJcjxEIAABCAAAQhkFYFhzuf7mWeesddee83n9wida9eunfXr18/nXmjSpElYzSMECprAp5+anX++2cyZEQb3EbEHHjBr0KCgsTB4CEBgKwkgNGwlQA6HAAQgAAEIQKDiCcyaNcuHRSg8YvLkyckOKcHWsa4O36mnnmqdOnVKrmcBAoVOQHndbrnF7L77zJVxNdt2W7O77zb75S8LnQzjhwAEMkEAoSETFDkHBCAAAQhAAALlTkAZsN955x2fe0EJHkPiUCX069y5s/Xp08dUlrIBt2bL/drwgtlNYOJEs/POMxszJurnwQebPf642U47ZXe/6R0EIJA7BBAacuda0VMIQAACEIAABByB8ePH+9CIF154wRYvXpxkssMOO/iwCHkvtGrVyhYuXOjL6SZ3YAECEPC5F/7yF7OVK81cInm7/vooP4OWMQhAAAKZIoDQkCmSnAcCEIAABCAAgTIjIEHhJZcSX7kXxo4dm3wdlcs94ogjfGhEt27dSl1+K3kiFiCQ5wTmz4+qSAwcGA20fXuVrTTbf/88HzjDgwAEKoQAQkOFYOdFIQABCEAAAhDYHAGFQgwePNiHRrz99tu2evXq5CG77767FxdOOOEEq1+/fnI9CxCAwIYEJC5cconZggXRtl//2uyOO8xcChMMAhCAQJkQQGgoE6yFcVIl3ho16nvbb7/9rEWLFtxFKozLzighAAEIlDmBKVOmeM+F/v37m/7XBJOgIGFBoRESGjAIQGDTBFasMPvrX83+979ov0aNzB5+2Fzukk0fx1YIQAACW0sAoWFrCRbw8UOGDHE1l2/2BKpXr24777yz7bLLLmmPzV0h5sqVKxcwJYYOAQhAAAIlIbDCzYjeeOMNLzB86urtJZQG35n+h3Tt2tWLC7169TKFSmAQgMDmCSjC6NxzzSZNivZ1Hx979FGzpk03fyx7QAACENhaAggNW0uwgI9ftmyZ1alTx/S4atUql7l4jG9xJDVr1vTCQxAh2ruAwF133dV23HFHU1ZwDAIQgAAECpvAyJEjfWjEK6+84v+fBBqtW7e2U045xSd3VJJHDAIQKBkBF3Fk999v9s9/mq1da1ajhtk//mF2/vnmfnuV7BzsBQEIQGBrCSA0bC3BAj7+zDPPtJ///Pe2fPly++abb+zbb7/1j1qe6OomSYDQHarRo0f7FkdVq1atNAEiCBHNmjVDgIiDYhkCEIBAHhKYO3euqWKEEjvq/0Uw/W84+uijvfdCly5dwmoeIQCBEhKYMcPsggvMnFOQt733jhI+EmlUQoDsBgEIZIwAQkPGUBbuiSQOqHXv3j0NwvTp09PEBwkR+kH5448/enFi1KhRLsfDqLRjarusREF0iIdhcDcrDRNPIAABCOQcgbXu1upAl5FO4sIHH3zg7rS6W60/mXL9KO/Cscce6z3lwnoeIQCBkhN4+eWoTOWSJZHngpI//u1v5sKNSn4O9oQABCCQKQIIDZkiyXk2IKD8DGqHHXZYcptibqdNm5YUICQ+BAFCnhESIb744gvfkge5BYVoSHiIiw9a3n777eO7sQwBCEAAAllGQF5uzz77rPdgmK/6ej9ZkyZN7KSTTvICQ9u2bcNqHiEAgVISWLrU7E9/MnvxxehAF53qkz8efngpT8TuEIAABDJIAKEhgzA51eYJKC9Dy5YtfevRo0fyAAkQU6dOTRMg9ON0kstgpPALhWEojlctbnXr1i1WgNAPWAwCEIAABCqGwFI381HOhaeffjrNc61q1aqm7355L0iErlKlSsV0kFeFQJ4QGDYsyr2gkAmZ0+7soYfMGjSInvMXAhCAQEURQGioKPK8bhoBCRCtWrXyrWfPnsltqqEuAUKig1rIAyEBQgko9WN2xIgRviUPcgvbbrutTzpZNAyjcePG8d1YhgAEIACBDBGQYDx06FAfGvHmm2/aypUrk2dWEmAldjzxxBOtYcOGyfUsQAACW0ZgzRqzW281u+cecxVazNx9F7vrLrNf/WrLzsdREIAABDJNAKEh00Q5X0YJqKxZa5d5XE1lzYJJgJg8ebIXHoL4EDwgVq9ebUtcgOJnn33mWzhGj6rBrh+8QYDQo1ojFZbGIAABCECg1ASUj6d///4+PEKhccHkcXb88cd774WOHTuG1TxCAAJbSUDlKs87z+zLL6MTHXSQ2eOPm7Vps5Un5nAIQAACGSSA0JBBmJyq/AhIgGjj/qOqHXnkkckXXrdunRcg4t4PEiLkAbHGyf+LFy92mZg/9S15kFto4HwMiwoQygGx3XbbxXdjGQIQgAAEHAF5lL311lvee2HIkCHujqq7pepM3mkHuVmPQiN69+5t1atX9+v5AwEIZIbAE0+YXXedOY8hc6FHZn/+s9nVV0fLmXkFzgIBCEAgMwQQGjLDkbNkCQHF+yqpmJpKpAWTAPHdd98lQy+CEPH99997AWLRokX2ySef+BaO0aNcfEMSypCIsn379ggQcUgsQwACBUNA5YpVNeJll95enmPBWrRoYSeffLL169fPtIxBAAKZJbBggdlll5kNGBCdt107M4kOnTtn9nU4GwQgAIFMEUBoyBRJzpPVBCRASCBQ0122YCqvtjEBQtsWuP/sijlWi5uSTYawiyBA6LlCMzAIQAAC+URA34MvunT2Ehi+/vrr5NDkrSBBV7kXDjnkEO/NkNzIAgQgkDEC779vdvHFZqFoy9lnm91xhypyZewlOBEEIACBjBNAaMg4Uk6YSwSUAT0IBn369El2XSLDxIkTkx4QIQ/ElClTfO33uXPnmppchuMmASJ4QOi8QYRQckoMAhCAQK4QkBfYoEGDvLgwwN1C1XdisL333tuHRvTt29cloHMZ6DAIQKBMCLiiW3bjjWaPPhqdXnlUH37YzH30MAhAAAJZTwChIesvER2sCAISIJSzQS1uyvMgAUKhF2oTJkzwjxIg9MM8CBAfffRR/DBr2rRpUnQIQoS8K/iRnoaJJxCAQAUTUD6bZ5991id31PdZMIWRnXDCCV5gKPq9GPbhEQIQyByBsWOjhI/uZ4a3I46IBIcddsjca3AmCEAAAmVJAKGhLOly7rwjUK1aNdttt918iw9OlS6CABG8HyREKAO7BIjZs2f79uGHH8YPsx3cL4bg9RAEiHYu8BIBIg0TTyAAgTIk8OOPP9prr73mvReGDx+efCWFnHXv3t2HRhzhZjkSYDEIQKBsCbiiWvbAA2b//Ke5HFLmEqqa3Xyz2YUXKtlq2b42Z4cABCCQSQL8asgkTc5VsAS22WYb69Chg29xCMrMHrweggChRwkQKtE5a9Ys3+SiHLcdd9wxTYBQGIY8IGrXrh3fjWUIQAACW0xg2LBhXlx4/fXXbfny5cnzKJmu8i4ouaPCwTAIQKB8CMycGQkKIS3UnnuaPfWU2R57lM/r8yoQgAAEMkkAoSGTNDkXBIoQULK0PdwvBLW4rXR1qSQ4hBaqYEiAUJm4GTNm+Pa+MkD9ZCob17x5c59TIng/hASXtWrVCrvxCAEIQGCjBORd9dxzz/nwiMmTJyf3q+Oyyh1zzDE+NKJTp07J9SxAAALlQ+DVV82uusrshx8iz4WLLjL7+98jj4by6QGvAgEIQCCzBBAaMsuTs0GgRARq1Khhe+21l2/xA1a4zE9x8SEIEBIeJEBIiFB77733kodJgFA5uXjySQkRCsGoWbNmcj8WIACBwiSg3DJK6KiqEfKekjdVsAMPPNB7LygZLoJloMIjBMqPwNKlZtdcY/b889FrNmtm9r//mfXoUX594JUgAAEIlAUBhIayoMo5IbCFBCQMKKO7Wtzk1iwBQsJDEB/0qNALCRBTp071beDAgcnDJEC0bNnSh2DE80DILVpCBwYBCOQ3gfHjx3txQaUpFy1alByscsMoLELhEa1bt06uZwECEChfAkqJct55ZtOnR6974olmDz1ktt125dsPXg0CEIBAWRBAaCgLqpwTAhkmoDuNHTt29C1+6mXLliVzQBQnQKgahpruZgarXLmytWrVKpkDInhCSIBQqAcGAQjkLoHFixfbSy+95EMjxowZkxyIEtn26tXLh0Z069bN9D2AQQACFUNA1WJvu83s7rvNeRiZucglu+suszPPrJj+8KoQgAAEyoIAQkNZUOWcECgnAoqr3meffXyLv+RS54sZPCDCo4SIOXPmeLfp77//3tTefvvt5GHKMB8XIEIeiDZt2piSXWIQgEB2ElAohErqKjRCn2lVwQm2++67e8+FE92t0vr164fVPEIAAhVE4LvvIi+G0aOjDrjoJXviCTOn9WMQgAAE8ooAQkNeXU4GA4GIgMpj7rfffr7FmUiA+Prrr5PhF0GEmDt3ri/D+Z37BaT21ltvJQ+TALHTTjslPSBCGIYECN0lxSAAgYohIG+lZ5991vr3728zla7+J5Og0LdvX++9UDQRbdiHRwhAoPwJPPmk2XXXmbl0TM6ryOzaa6Pm/s1iEIAABPKOAEJD3l1SBgSBjROQALH//vv7Ft/rB5fmOh56EZbnzZvnBYiJEyea2htvvJE8rGrVql6ACKEXcQFC2zAIQCDzBJQwVp9DeS98+umnPkeLXkWhEF27dvXigkIk8ELKPHvOCIEtJbBwodmll5oLY4zOIO8FeTHImwGDAAQgkK8EmA3k65VlXBAoBYF69erZAQcc4Fv8MMV7ywMieD4EAWLBggW21gWZTpgwwbeiAoTyPQQBIjwq6RwCRJwuyxAoOYHPP//ciwuvvPKKKTdLMIU7Kaljv379TEkeMQhAILsIuEIvplKVTrf3pjwMysegvAwYBCAAgXwmgNCQz1eXsUFgKwnIBVvl79TittDdngmiQ/xR6yVAaJ3aa6+9ljxMYRYSICQ4dOjQwTeJEHqu8AwMAhBIJ6CQphdeeMELDPIoCqbqNL179/beC/psqsIMBgEIZBeBlSvNbrrJ7D//ifqlShKqKKHKEhgEIACBQiCA0FAIV5kxQiDDBLZzv5i6dOniW/zU8nQIIkMQIOQNodJ6a9as8d4R8pCIJ6GUi7cEiJB8MnhAqDQnAkScLsuFQEBCncrUKvfC+++/74W7MG7lXTn11FPt2GOPdXdDuR0auPAIgWwjMG5clPDR/fvz1qOH2f/+Z9asWbb1lP5AAAIQKDsCCA1lx5YzQ6DgCDRs2NAOOugg3+KD153ZEH4x2qXanjx5ss/5oNwQypD/1Vdf+RY/RqU227Vrl5aEUmJEixYtKM0XB8VyXhCQMCdxQR4M8+fPT46pSZMm7g7oiV5g0OcBgwAEspdAImH24INmN99sTlw3VzLa7G9/M7v4YnOeR9nbb3oGAQhAoCwIIDSUBVXOCQEIpBHQZEntkEMOcXGq83yZPYVSSIAIHhBBiNDjkiVLbNWqVTbO3RZSi1uNGjWsffv2XoAIj/KCQICIU2I5FwioCoxyLjz99NM2atSoZJeVy6SHuwUq74Xu3buT2yRJhgUIZC+BWbOiXAxDhkR93GMPM1WZ2Guv7O0zPYMABCBQlgQQGsqSLueGAAQ2SSAIED/72c/S9ps9e7YXIIL4IDFCiSc1MVvpAl/HjBnjW/wgxa1LeAihF6EKRvPmzYlhj4NiuUIJJNwtz6FDh3rvBSVR1fs5mN6zEhdOOOEEa9SoUVjNIwQgkOUElI7oyivNnJOe91y44ILIq8Hp4hgEIACBgiWA0FCwl56BQyB7CTRt2tTUunXrltbJmTNnpgkQEiIkQCgLv8r+ffnll77FD6pVq5YXH4IAER6buWBZkujFSbFclgSmT59u/fv3t+eee86mTp2afCmVnD3uuOPstNNOs44dOybXswABCGQ/ARWAueYac5/tqK8q/PLoo2ZHHJH9faeHEIAABMqaAEJDWRPm/BCAQMYISBxQkzt53GbMmFGsAPHjjz/a8uXLvVt63DVdx9auXTsZghG8H/So82MQyAQBhf+89dZbvmrExx9/bOvXr/enlcClXCbyXjj66KNN4UAYBCCQWwSGDzeT50LQDfv2NXv4YTOXqgiDAAQgAAFHAKGBtwEEIJDzBHbccUdTO+yww5JjkYu67iLHwy+0rDKBEh8kQkh8KCpAKJt/8HqICxDysMAgUBICSniqxI4vvfSSzzcSjlEYT79+/XxTThEMAhDIPQKuMIzdcYfZnXeaEw/NVYCJnp99du6NhR5DAAIQKEsCCA1lSZdzQwACFUZAd401mVM7/PDDk/2QACHXdYkOoYUcEIqXVxjG559/7lvyILcgF3cJD3HxQYLE9ttvH9+N5QIlsHDhQl8x4plnnvFlXAMGVU856qijvPeCkqESrhPI8AiB3CPw/fdm559v9sUXUd87dzZ74glzFZJybyz0GAIQgEBZE0BoKGvCnB8CEMgqAprotWrVyreePXsm+ya39mnTpm1QBUMeEHKBVyLKESNG+JY8yC1su+22xQoQSnSJ5TeBdevW2aBBg3xoxIABA2ytbnX+ZHvvvbcXF44//nj/HgnreYQABHKTgCsOY3/+szmPOHMlls2uvjp67orEYBCAAAQgUAwBvh6LgcIqCECg8AhUdr8cgwBxRCyTlwSIKVOmpOWAkAfEpEmTbPXq1d41frgL1lWLW/369dO8H4InBNUE4pRyc/m7777zoRFK7KgSrcEauuBsVYxQ7oVdd901rOYRAhDIYQLOWckuv9zs7bejQey0U+TF4NKsYBCAAAQgsAkCCA2bgMMmCEAAAhIgdnK/LNWOPPLIJBDdzd6YALFmzRpbvHixDRs2zLfkQW6hQYMGfhKqsIuQC0KPmqRi2UtAOT1eczXsFBoRF5WqVKlihx56qBcXJFBV5fZm9l5EegaBUhL48EOziy4yJyhGB55xhtnddyuUrpQnYncIQAACBUgAoaEALzpDhgAEtp6AJpht2rTxTTH4weQ+P3ny5DQPCOWCkAeEti1atMg++eQT38IxepTQEISH4P2gRwkTWMURkFgkceH111/3SURDT9q2bWunnHKKnXzyyUaYTKDCIwTyg4BzVnPJHuvYU09F49HX8IMPmvu858f4GAUEIACB8iCA0FAelHkNCECgYAjojnY7lxlMrXfv3slxS2SQy71EB4VehASUWqdtCxYsKFaAaNy48QYChAQJhWZgZUNg9uzZ1r9/fx8e8b2yv/1kqkhyzDHHeIFh//33D6t5hAAE8oCAKki4gjE2YEAVe/nl7VzS4OgnsooZPfaYucpGeTBIhgABCECgHAkgNJQjbF4KAhAoXAISICQQqPXp0ycJQiKDvB2CABEe5RWhbfPmzfPt448/Th6jBd1FDx4Q4VEeEEpOiZWegMJdlNBR3gsfOn9phcYE6+xSyyvvgq5brVq1wmoeIQCBHCfgvl5t8GCz9983l9jVXMibBlTFj6patYT9/e+V7JJLzFWL8av4AwEIQAACpSCA0FAKWOwKAQhAINMEJEBIIFDT3fJgmvhKgAjeD0GAUF4ITYKVhFBtyJAh4RD/qHKb4XxBgNCjynNiGxIYP368FxdefPFFH9YS9mjatKn169fPey+0bt06rOYRAhDIYQLSD131Yi8sSFwYOzZ9MBIU9thjvXXrttx9/lfYz37WOH0HnkEAAhCAQIkJIDSUGBU7QgACECg/AtWqVfNJI4tWL1ClC5XclAAh8SEuQKhCxpw5c3wbrNt0Mdthhx02qIKh8I5CFCB++OEHe+mll7zAMGbMmCQlMe/Vq5cXF5TgUYlAMQhAILcJuEgo56UUeSzIa2HJkvTxbLed2eGHmynVjlqDBmts4cKlzosBN4Z0UjyDAAQgUDoCCA2l48XeEIAABCqUwDbbbGMdOnTwLd6RVatWpQkQwRNi2rRpJgFi1qxZvg3SL+2YNWvWbAMBon379la7du3YXrm/KAYfffSRFxfednXqJNgE23333b24oNKUJN8MVHiEQG4ScM5gSa8Ffd0V57Ww777mqghFwsKBB7pgiShawg/YfZViEIAABCCQAQIIDRmAyCkgAAEIVDSB6tWrmybManFbuXKlTZgwIekBETwhJEAkEgmbOXOmbx988EHyMN3J29FlPguhFwrF0LIEiFzLUaBQk2effdYnd9RYgymZZt++fX3uhT322COs5hECEMhBAjNmpHItyHvBVaNNs0aNzHr2TIkLLscuBgEIQAACZUwAoaGMAefz6WfOrOySJ21jrnS8tWiRzyNlbBDIXQI1atSwPffc07f4KFasWOEFiBB6EQSI6dOnewFCj2rvK5D5J5MA0cJ92IMAoccgQNSsWTPsVuGPGtsbb7zhvRc+/fRTPx51SqEQXbt29d4LR7rbmfIOwSAAgdwjIIek4cPN3nsvCov4+uv0MSjqqVMnc5V/zP9GOeAAff7T9+EZBCAAAQiULQGEhrLlm9dnHzy4uv35z7VcM2ve3NwPeLNDDjE76CAz7hbk9aVncHlAQMLAXnvt5Vt8OMuXL0/L/RCECHkDyANi6tSpvg0cODB5mCbwRQUIeUEoB4SEjvKyz12WN1WNePXVV23p0qXJl23VqpUXF04++WRTqAgGAQjkHgHnhOVubkSJHF0UlDk9Mc1cHlzvtaA8CwqLUO4FDAIQgAAEKo4AQkPFsc/5V3ZzDmvadL3Nnl3Z3fk0e+qpqGlgu+2WEh26dDGXcC7nh8sAIFAQBBQa0bFjR9/iA162bFkyBCN4P0iEkACh/AcKUVB79913k4dJgGjZsuUGVTDatm1rCvXIhKn85/PPP+/DIxQiEkxCSm93O1NlKQ90QdgkdgtkeIRAbhBwUV/mHJJ8Ikc5VrkcuGnmCvaYPBWC18J++1GGMg0QTyAAAQhUMAGEhgq+ALn88mefvdzOPXedm2hs590X5cKouwyqQ/3VV1F7+OHIXVGJl+TtoKblDM0xchkffYdAThGoU6eO7bPPPr7FOy7PgZADIggQepztUr1LgJg8ebJv77zzTvKwKi7zmrwMQuhFKMcpAaIk4Qxr16513znvee8FhXboebD93GzjlFNOseOOO87UZwwCEMgdAk6r9KUn5bmgyr0SG+ImhySFa8prQY8u1QoGAQhAAAJZSgChIUsvTC51S3nU1C680NzEwuyzz6K4SQkPuhsh98YRI6J2xx2RyNC5s7n61JHwoNx18YzPuTR2+gqBQieg8pj7OvVQLW4SICQ4xMUHLc+dO9fWuWL23333nW+qABFMAkTr1q3TqmBIjAjhDpMmTbJ7773XXnjhBZs/f344zJo0aWInnnii915QuAYGAQjkBgH9PtDvBHksqDldMs1cxVnnkZQqPemcrTAIQAACEMgRAggNOXKhcqWbSrakHwVqV19tpjJR8nKQ6KDmQqj9usGDowzRGte225odfHDK44F5Qq5cbfoJgY0TkADRyWVjU4vbDz/8kBQfQv4HPQYBQmKC2ptvvpk8TAJEI5c2fs6cOcl1VZ3fdI8ePby40L17d9NzDAIQyH4C7uOdzLXwySfRb4J4r5VculevKM+CvBYIvYzTYRkCEIBA7hDgl1nuXKuc7Gn16uYmA1HTAJYsie5aBOFBIRZa99ZbUdM+7uZkWmJJV2UPgwAE8oRAvXr1XFz1Ab7Fh7TYxVwV9X6QACHPBXlABJFBYRYKjZAHg8QHDAIQyG4CLr+sffxxJC4oh6xyOsVNxV90syEkcXRFcjAIQAACEMgDAggNeXARc2kI8l44/vioqd+6QanccUF4UFZp51ntkrtFTfu4sO1kfgd5SpBJWlQwCOQXgfou2Lqzi6lSi9vChQvtyy+/9JUwFEZx6KGHxjezDAEIZCEB5WVVKMQHH0ShEWvWpHdyp51SuRZ0M6J27fTtPIMABCAAgdwngNCQ+9cwp0egclSnnx41DcSFbduAAZHw8OGHZi6hvHOjjtr//hcNVXc7lFRSOR7239/MJcnHIACBPCWwnVMWJT506NDB5XKpkqejZFgQyG0CqiY7dGgq14IrRpNmqnKr/9kqOynPBVWmwiAAAQhAIL8JIDTk9/XNudG1aWP2u99FTeUzR41KeTvI9VI/ZsaMidr995uLyzYXA57yeFCiKCWPwiAAAQhAAAIQKDsCCn2Ux4KakkDHir/4F9X/8xAOcfjhZq7iLAYBCEAAAgVEAKGhgC52rg21UiVzpfSidtll0Y8YJY4KYRb6YbN6deSWqazV//pX9EPmoINSwoPumug8GAQgAAEIQAACW07A5XH1JSdDSEQsN6s/qbwLu3aNxIXevaOwxy1/NY6EAAQgAIFcJ4DQkOtXsID6L+8FuV6qXX+9mRJMKbwiCA8ujNuX0gzPhaZBg2h/hVqotWpVQMAYKgQgAAEIQGALCcircNy4yGNB4oLKVKuEddxc2pRkOITSpyhEAoMABCAAAQiIAEID74OcJaC7J3LLVJMtWmSmjNZBaJg4MVr36qtmarJmzaI7LhIrunQxU44IDAIQgAAEIACB6H+myk8PGhTlW3BFX9KsTh1zCVmj/7tHH23WunXaZp5AAAIQgAAEkgQQGpIoWMh1AvJeOPnkqGksM2akEkvqR5OeK0HVM89ETfu4SnlpFS1UFQODAAQgAAEIFAIBeSgo75E8FvR/cuRIM3kyxE0hiEHUV2iEylFiEIAABCAAgc0RQGjYHCG25yyBHXc0O/PMqGkQ48envB10x0YeEN98E7V//9uscmUzJZMMYRb77YcbaM5efDoOAQhAAALFEliwwEz/A0OuBf0vjJsE98MOi0Ii5LXQokV8K8sQgAAEIACBkhFAaCgZJ/bKAwKuOp4rkWd2/vlRnKniTUOYhZJMKufD559H7a67zKpXNzvggJTwoLKaVNfLgzcCQ4AABCBQQATWrYsqOMljQeKCqjkVNf1/CxUiJLZTvakoIZ5DAAIQgEBpCSA0lJYY++cFAXkvSERQ++Mfo+oVQ4akhAe5j65aZfbRR1HToOvWNVNFC+V30A+x9u3zAgWDgAAEIACBPCMwd26ULFmlJyUwqGJE3OrXN1PJyRASofxFGAQgAAEIQCCTBBAaMkmTc+UsAcWcylVU7aabzJYujTJtB48HhV1o3TvvRE0Dbdw4JTocfLBZ8+Y5O3w6DgEIQAACOUxg7VqzL76IPBbktTB2bPpgVOZZoYFHHhmJCxLN8dBLZ8QzCEAAAhDILAGEhszy5Gx5QkDeC8ceGzUNSXeH4hUtpkwxmzfP7MUXo6Z9dtopFWahH3Hbbae1GAQgAAEIQCDzBGbNSnktqNSzxPC4NWxo1qNHSlygylKcDssQgAAEIFDWBBAaypow588LAk2amP3851HTgCZPTlW00A+8OXPMvv8+ao8/Hg15jz1SwoNCNGrXjtbzFwIQgAAEIFBaAmvWmCm3UKgQIU+7uCkkcN99U+EQnTtHSY7j+7AMAQhAAAIQKC8CCA3lRZrXySsCrVubnXNO1DSw0aNT+R2U62HJksh1Ve6rDzxgVtV90vQDULkdlONBLqyUCMurtwSDgQAEIJBxAirLHJI4KmfQjz+mv4RC+Hr2TCVybNQofTvPIAABCEAAAhVFAKGhosjzunlFYO+9zdQuucRMsbLDhqWEBy0rseRnn0XtttuisplduqQ8HlQNQ3ejMAhAAAIQKFwC4X9FEBe+/TadhfIq7L9/SljQsvIvYBCAAAQgAIFsI4DQkG1XhP7kPAF5Lyg5pNqf/2y2YkVUszwkllRpsZUro2STygguUwZweTuEpnwPGAQgAAEI5D8B5fyRsKAmrwX9z4hb06ZmRxwRiQu9epk1aBDfyjIEIAABCEAgOwkgNGTndaFXeUSgZk0z/ThUky1enPJ2kPigO1Za9/rrUdM+KjUWymgqsaR+aGIQgAAEIJD7BCQ0y9NNuRYkNk+alD4midUHHphK4rjPPngtpBPiGQQgAAEI5AIBhIZcuEr0Ma8IyHvhxBOjpoHNnGn27ruR+KA7WtOmReuefdZMTda+fSq/g36A1qsXrecvBCAAAQhkPwElC9b3u8SFoUMjr7Z4r3fcMRKjVX5S3gt8x8fpsAwBCEAAArlIAKEhF68afc4rAvJeOOOMqGlgX3+d8nhQRYuFC80mTIjaf/8b3dlSPogQZtGpk5m8JjAIQAACEMgOAgp/kKAgjwWJCwqPiFu1ambyVjvqqMhzQd/pGAQgAAEIQCCfCCA05NPVZCx5QWDXXc3U/vAHs/XrzT7/PCU8fPyx2fLlZsrzoHbPPVH1CiUEC8LDXntFVS7yAgaDgAAEIJAjBCZOTAkLn35qtnp1esdbtoy8FiQu9OhhVrdu+naeQQACEIAABPKJAEJDPl1NxpJ3BFSJQh4LaldeGf1w1V2ykFhSNdX1Y1YChNo//mFWp050pywID7vskndYGBAEIACBCiegUpP63pXXgr6TVYoybtWrRwKwwiEkLuy+e3wryxCAAAQgAIH8JoDQkN/Xl9HlGYFttjE79NCo/fWvZsuWRXG/QXgYOzZaN2CAmZpMddWD6KAEk82bR+v5CwEIQAACpSPwzTdRKMSgQVFCxzVr0o9v0ybltXDYYWa1a6dv5xkEIAABCECgUAggNBTKlWaceUlA3gt9+kRNA5w/32zgwJTHgxKQad3LL0dN+8h9N17RQkIEBgEIQAACGxJYutRsyJBUhYhZs9L3qVHDrGvXyGNBXgt4kKXz4RkEIAABCBQuAYSGwr32jDwPCUg0OPXUqGl4SkAWr2gxe7bZ1KlmTz4ZNe3ToUPK46Fz5yj0QusxCEAAAoVIYNy4yFNMSRyHDzdbty6dQrt2qSSO3buTjDedDs8gAAEIQAACEQGEBt4JEMhjAq1amf3611HTMMeMSXk7fPSR2Q8/mI0fH7WHHjKrUsVMNduDx8O++0bJJvMYEUODAAQKnMDixWb6PlQ4hMSFuXPTgdSqFYWryWPh6KPNFB6BQQACEIAABCCwaQIIDZvmw1YI5BWBPfc0U7voougu3WefpYQHZUlfudJMCSbVbr/dTG7B8nJQjgeJD0pmpgSVGAQgAIFcJZBImCmfjUQFJXIcOTKq8BMfjyr/hCSO3bqZKbEjBgEIQAACEIBAyQkgNJScFXtCIK8IyHuhS5eoXXNNJDLorl5ILPnFF9G6Dz80U5PVq2d28MGpUIu2baP1/IUABCCQzQQWLjQbPDgSFiQuLFiQ3lvlu1HyRokL8lqQNxgGAQhAAAIQgMCWE0Bo2HJ2HAmBvCIg74WePaOmgSmsQnf8gvDw9dfRujffNFOTNW2aCrOQALHDDtF6/kIAAhCoSALr15uNHq3vqho2dOg2flmeDHGTh5bCISQuKKFjtWrxrSxDAAIQgAAEILA1BBAatoYex0IgjwnIe6Fv36hpmMq2Hipa6I6gkkoquWT//lHTPvJwCPkdDjzQrEEDrcUgAAEIlD0BVdiR95UEUj0uWqTXdAkWfjJ9p8lrQeKCGqV+AxkeIQABCEAAApkngNCQeaacEQJ5SUDeCr/4RdQ0wG+/TXk76Ee9fuRPmhS1Rx81q1QpygcR8jvsvz/Z2fPyjcGgIFBBBFQNQiFeEj4lLnz5ZXpH9B3UocNa69UrYcceW82HfVXlV086JJ5BAAIQgAAEyogA/3LLCCynhUC+E9h5ZzO13//eTC7J+sEfwixUd/7HH6Mf/vrxf999kVtyp06qalHLDj+8kmmZH/35/i5hfBDILIE5c6LqEKoQIYFTIV5xkxdVjx6pkIhq1RZb7dq1rWZN4iLinFiGAAQgAAEIlDUBhIayJsz5IVAABHTnUKUw1S6/3GzNGrNPPkkJD6puEdZ98kltLzy43/4+GaU8HtSU5V3nwSAAAQgEAmvXRlUh5LEgcUHVIuKm7wyV5A3hEArZUqLbYPK0wiAAAQhAAAIQKH8CCA3lz5xXhEDeE1BSNSVXU/vLXyLvBt19lMfD22+vsfHjqzqPh0o+54PyPsi22y6V30HCQ8uW0Xr+QgAChUVg5sxUrgVVili2LH38DRuaHXFElMRRiRybNEnfzjMIQAACEIAABCqeAEJDxV8DegCBvCcg7wWVjFO74oqFtn59A/voo22SoRbK7aDyc6+8EjUBUaK2kFhSFS0aN857TAwQAgVJYPVqs+HDU6UnVeEmbpUrmw+1ChUiDjjATOswCEAAAhCAAASylwBCQ/ZeG3oGgbwloDuS/fpFTYOcNs3s3XcjjwcldlOFi+nTzZ5+OmraR6EVIcxC7tF162otBgEI5CIBfb71WVf76COz5cvTRyEvBXktSFzo1ctM3xkYBCAAAQhAAAK5QwChIXeuFT2FQN4SaNHC7KyzoqZBjhuXyu8g1+nFi810l1PtkUeiu5mKyw7Cw377mVWvnrd4GBgEcp7AqlVmw4alKkRMnJg+JOVV6Nw5CoeQuKDPNDlb0hnxDAIQgAAEIJBLBBAaculq0VcIFAiB3Xc3U7vgAjOVsBsxIiU8DB1qtnJllCBu5EizO+80q1HDTOUzQ6jFnnviWl0gbxWGmcUEJk+OEjjKa0GVaPS5jZtK5spbQXkW9Fi/fnwryxCAAAQgAAEI5DIBhIZcvnr0HQIFQCDc6dTdzj/9yUx3RjVpCaU0JTZoAiP3azXZttuaHXRQ5PEg8aFdu2g9fyEAgbIjsGKF2aefmoUKEd99l/5aKmfbpUuqQkTHjunbeQYBCEAAAhCAQP4QQGjIn2vJSCBQEAQUInH44VHTgJcsidyxg/Awfny07u23VeEiQrL99qkwCyWW3HHHgkDFICFQ5gQkJshjQeKCStpKCIybkrrKW0HhED17RiJgfDvLEIAABCAAAQjkJwGEhvy8rowKAgVDQN4Lxx0XNQ16zhzzZTMlPGgCNHlytO6FF8zUZG3apIQH3WFVaU0MAhDYPAElbfz44ygkQp8xJXKN2zbbmEnMCxUiFMaEQQACEIAABCBQeAQQGgrvmjNiCOQ1AXkv/N//RU0DVdK54O0waJDZvHlmugur9thjEYo99kjld1DpvFq1ovX8hQAEzCZMiDwWJNwpoaPKUcatVatUEkd5G9WpE9/KMgQgAAEIQAAChUgAoaEQrzpjhkABEVB+BrXf/tYskTAbPTolPCinw7JlZmPHRu3++80UR66M96poofwOiiOvVq2AgDHUgiegz4S8FhQOoTZzZjoShS917ZoSF3bbLX07zyAAAQhAAAIQgABCA+8BCECgYAioXJ6EA7VLLzVbuzZKXhc8HsLdWj2q3Xpr5N1w4IGpUIsOHSi7VzBvmAIa6FdfRaFG8lr47LPosxEfftu2kbCgChGHHYbXT5wNyxCAAAQgAAEIbEgAoWFDJqyBAAQKhIC8F+S5oHbddWaKPx88OOXxIO8HrQt3doWlQYPUMTqudesCgcUw84qAkqjKo0fCgtrs2enDq1nTrFu3VK6FnXdO384zCEAAAhCAAAQgsCkCCA2bosM2CECgoAgoN4Pu2KrJFi6MRIbg8aBY9UWLzF57LWrap1mzyI1cooOS4DVporUYBLKLgMKGxo2LRAUJZyoLu25deh8lJui9r0SOhx5qVqNG+naeQQACEIAABCAAgZISQGgoKSn2gwAECo6AqlGcdFLUNPjp09MrWsyYEcWvP/OMmZpMk7XgJaGKFqqKgUGgIghIFIt7LSgRatxq1zbr3j0SF44+2mynneJbWYYABCAAAQhAAAJbTgChYcvZcSQEIFBgBJo3N/vVr6KmoSuuPXg7fPhh5O3w7bdmav/5j1nlymZ7750SHjp14i5xgb1lynW48lr48svIC2fQILPPPzdbvz69C0rcKI8FNSV0VDlKDAIQgAAEIAABCGSaAEJDpolyPghAoGAIaNKmdt550YRO7uhBeFDW/hUrzL74Imp3322mbP37758SHvbay6xKlYLBxUDLgIDCeyRyKRxC4oKex61u3Sh5YxAXWraMb2UZAhCAAAQgAAEIlA0BhIay4cpZIQCBAiMg7wWJCGpXXWW2apXZ0KEp4WHEiGjdkCFmajJNAhVeoTKaCrcg4V7Ehb8bJyAPBYlXEhUkLihhqTwZ4rbnnqkkjnpfUZ41TodlCEAAAhCAAATKgwBCQ3lQ5jUgAIGCIyDvBcW/q914o9nSpdHkMHg8KDGf1g0YEDUBatw45e0g8WHHHQsOGwMuhoByK0hYUHUIeS8sXpy+U716Zj16pMQF3jfpfHgGAQhAAAIQgED5E0BoKH/mvCIEIFCABOS9cMwxUdPw585NeTtIfJg82UwTypdeipr2ad06JTyoooWSU2L5T0DVIJRfQR4LEhfGjEkfc6VKZh07pipEyCtGpVoxCEAAAhCAAAQgkC0E+GmSLVeCfkAAAgVFQGUwTzstahr499+nV7SQECHxQe2JJ7SH2e67p4SHzp3NVDUAyw8Cs2envBYGDzZbsiR9XBKZevaMxAWVoGzaNH07zyAAAQhAAAIQgEA2EUBoyKarQV8gAIGCJaDSgr/5TdQUc6+72CHMQhNPhVko3ELtwQejO9j77JPK77DvvsTi59KbZ80aM+XtkMeC2vjx6b2X18J++6XCISQskTg0nRHPIAABCEAAAhDIXgIIDdl7begZBCBQoAQ0yVRFCrWLLzZbu9bss89SwsOnn0aJJYcPN1O77TazmjXNNBlV8j/ld5D3g86DZQ+BGTNSSRyVEHTZsvS+NWpkdsQRkbjQq1eUsyN9D55BAAIQgAAEIACB3CCA0JAb14leQgACBUxA8fcHHRS1a6+NymZ+9FFKeFAVApXSVMJANVmDBtH+Eh7U2rSJ1vO3/AisXh0JRPJYUL6Fb79Nf21VKjnggFSuhU6dzLQOgwAEIAABCEAAArlOAKEh168g/YcABAqOgLwXdOdbTbZoUeR+H0ItvvkmWvfGG2ZqsmbNUvkdlFiSGP+IS6b/TpsWiQoSfCQGSQCK2/bbm8lb4aijoutHgs84HZYhAAEIQAACEMgXAggN+XIlGQcEIFCwBOS9cMIJUROEmTNTiSV1J3369Gjdc8+ZqcnatUuFWahqgUokYqUnsHKl2bBhqQoRkyaln0N5FQ48MBIWJC4orwYhLemMeAYBCEAAAhCAQP4RQGjIv2vKiCAAgQInIO+FX/4yakIhD4fg7aA77QsXmk2cGLVHH40mvsoHEcIs9t8/yvmgY7ENCahCiDhKxBk61ExiQ9zEX5Uh1FQpon79+FaWIQABCEAAAhCAQP4TQGjI/2vMCCEAgQInsMsuZmrnnmu2fr2ZcjoE4UFJCZcvNxs9Omr33mu2zTZmyhcQhIeOHQu74oHCHyQohAoRkyenv6GqVYvyYchjQeLC3nunb+cZBCAAAQhAAAIQKDQCCA2FdsUZLwQgUNAElGxQZRPVrrjCTAkLP/kkJTyouoXWaWKt9s9/mtWpY6bwiiA87Lpr/iOUx0cQFkKVj/ioW7RIJXHs0cOsbt34VpYhAAEIQAACEIBAYRNAaCjs68/oIQCBAicg74Vu3aJ2ww1RycUPP0wJD2PGROvefddMTaYyjEooqTKaEh806c51k1eHvDtChQjltYibOGm88liQ54LKh2IQgAAEIAABCEAAAsUTQGgongtrIQABCBQkAXkv9O4dNQGYNy/KRRBCLb77zmz+fLNXXoma9mnZMuXtIOGhYUOtzX5T7orgtaCEjmvWpPd5p50iYUHiwuGHm9Wunb6dZxCAAAQgAAEIQAACxRNAaCieC2shAAEIQMARaNzY7JRToiYgU6akKlpokj57ttnUqWZPPRU17bPbbinhQRUXJF5kgy1dGnktKInjoEFRJY54v2rUMOvaNVUhQnktMAhAAAIQgAAEIACB0hNAaCg9M46AAATII6C7AAAp/0lEQVQgULAEWrUyO/vsqAnC2LGpMAuFXCxZYvbVV1F7+OEoiaRKOob8DsoNoTCE8rLx41PhECNGmK1dm/7KKvMZwiG6d6faRjodnkEAAhCAAAQgAIEtI4DQsGXcOAoCEIAABByBPfaI2oUXmq1bZzZ8eEp4UDLJVavMNMFXu+MOM3kNHHBAKr+DjleCykzZDz+YDR4ceSzI42LOnPQz16plduihkdeCBAYJDRgEIAABCEAAAhCAQGYJIDRklidngwAEIFCwBKpUMVOohNrVV5utXBmFKoT8Dp9/Hq2TEKAmq18/vaJFaSf+iUTkVaFwCAkLI0dGJTyjs0d/FQKhBI5qCo2Q2IFBAAIQgAAEIAABCJQdAYSGsmPLmSEAAQgUNAFN6FX6UU22eHHkaRCEB4VYaN1bb0VN+zRtmgqzULjFDjtobbotWmSmMA0JC8q1oOSUcVNOiMMOS4VEtG4d38oyBCAAAQhAAAIQgEBZE0BoKGvCnB8CEIAABDwBeS8cf3zUtGLWrCjMQt4IEh+UVFLJJZ9/Pmrap21beUhUcyEa1W3atGr26admX3xhJk+GuKncpDwWFA6hMpTlmQci3g+WIQABCEAAAhCAAATMEBoy/C5Y6XyFV7mg5Hr16mX4zJwOAhCAQH4RkLfC6adHTSObMCGV30HeCgsWmE2apKZ/VenfqdtuG5WcDOJCixb5xYbRQAACEIAABCAAgVwmgNCQoav3lfMBfuKJJ2yYK8YuoaGVS81+5ZVX2p577pmhV+A0EIAABPKbQPv2Zmq/+13ksTBqVCQ8DBiwzj77rJI1b77e+vSp6j0XDjrIrFq1/ObB6CAAAQhAAAIQgECuEkBoyMCVW758uV177bVWt25du+mmm/wZH3nkEbv44ovt/vvvdz+c3S9nDAIQgAAESkygUiUzlcVUO/fcVfaDKydRtWpVa9y4cYnPwY4QgAAEIAABCEAAAhVDoHLFvGx+verDrlj83Llz7eabb7bOnTv7dvvtt1sl90v5mWeeya/BMhoIQAACEIAABCAAAQhAAAIQgMAmCCA0bAJOSTcNHDjQ3XXbx7bffvvkIXVc2vNDXMr0D1ygsfI2YBCAAAQgAAEIQAACEIAABCAAgUIggNCwlVd53rx5rjzbYttpp502OJPWrVmzxqZMmbLBNlZAAAIQgAAEIAABCEAAAhCAAATykQA5GrbyqipuWFZclYltlRbdWdjHP4n9UQ6HESNGxNaoJNs2/rkEDIkU2Wrr16/3XVPiyzlz5mRrN+lXFhLQe2fRokVZ2DO6lK0EEj/Vsly7di3fN9l6kbK0X/q+0f/gJUuWZGkP6Va2EQjfN3rk9022XZ3s7o++b/S7+Mcff8zujtK7rCEQvm/UIYXhK+w+W005CfU7rDSG0FAaWsXsqy8UmUIlilrt2rX9qtWrVxfd5J8vXLjQv6niG4PQoC8rtVywXOlnLrAslD7ynimUK535cfLeyTzTfD+jfsjFf8zl+3gZX+YI8H2TOZaFcia+bwrlSmd+nNn+3tmS/6MIDVv5Pqlfv74/g1SeohbW1ahRo+gm//y8886z0047LW2b/qnJ00EeEuHcaTtkyZMVK1Z41VZZ4IsTWbKkm3QjCwno7qJEOL13MAiUhIAEXX3nVK5c2YKnWEmOYx8ILF261KpXr570FoQIBDZHQN6k4Y50Nv8O29w42F7+BPS+qVKlim3sd3/594hXzHYC69atM/2fkun3jX7nZKvpf6n6Wxrjl35paBWzb6NGjfza4twyw7qN/TDef//9Nzhj8H7Ql1TNmjU32J4tK/SPWD/+9YWazf3MFl70I0VAnwt++Kd4sLR5AlLRg9DA983mebFHioB++MtTkPdNiglLmyagH/p638iFmffNplmxNZ2Akr9Xq1aN9006Fp5tgoBCEYLQoO+bbBYadIOwtP3LXtlkExclmzZpwiTvg+ISPmqdJuKtWrXKpi7TFwhAAAIQgAAEIAABCEAAAhCAQJkRQGjIANpevXr5pI7xBHe6+zZ48GBf4lJiBAYBCEAAAhCAAAQgAAEIQAACECgEAggNGbjKJ598snexu+yyy2zUqFE2btw4u+KKK7x7yUUXXZSBV+AUEIAABCAAAQhAAAIQgAAEIACB3CCA0JCB69S0aVO78847fQmt888/384991x/1uuvv95CDocMvAyngAAEIAABCEAAAhCAAAQgAAEIZD0BkkFm6BLttdde9vzzz/uay0o81aBBgwydmdNAAAIQgAAEIAABCEAAAhCAAARyhwBCQ4av1fbbb5/hM3I6CEAAAhCAAAQgAAEIQAACEIBA7hAgdCJ3rhU9hQAEIAABCEAAAhCAAAQgAAEIZD0BhIasv0R0EAIQgAAEIAABCEAAAhCAAAQgkDsEEBpy51rRUwhAAAIQgAAEIAABCEAAAhCAQNYTQGjI+ktEByEAAQhAAAIQgAAEIAABCEAAArlDAKEhd64VPYUABCAAAQhAAAIQgAAEIAABCGQ9AYSGrL9EdBACEIAABCAAAQhAAAIQgAAEIJA7BBAacuda0VMIQAACEIAABCAAAQhAAAIQgEDWE0BoyPpLRAchAAEIQAACEIAABCAAAQhAAAK5QwChIXeuFT2FAAQgAAEIQAACEIAABCAAAQhkPQGEhqy/RHQQAhCAAAQgAAEIQAACEIAABCCQOwQQGnLnWtFTCEAAAhCAAAQgAAEIQAACEIBA1hNAaMj6S0QHIQABCEAAAhCAAAQgAAEIQAACuUMAoSF3rhU9hQAEIAABCEAAAhCAAAQgAAEIZD0BhIasv0R0EAIQgAAEIAABCEAAAhCAAAQgkDsEEBpy51rRUwhAAAIQgAAEIAABCEAAAhCAQNYTQGjI+ktEByEAAQhAAAIQgAAEIAABCEAAArlDAKEhd64VPYUABCAAAQhAAAIQgAAEIAABCGQ9AYSGrL9EdBACEIAABCAAAQhAAAIQgAAEIJA7BBAacuda0VMIQAACEIAABCAAAQhAAAIQgEDWE6ia9T0s0A7ec889VqNGjawd/erVq23NmjVWpUqVrO5n1gIs4I4tX77cqlev7t87BYyBoZeCgL5r9J1TqVIlq1WrVimOZNdCJ7BixQqrVq2aVa3Kz51Cfy+UdPzr1q2zlStX+t1r165d0sPYDwL+faPfxfrOwSBQEgLr1683/Z+S6feNfudkqw0fPtz22GOPUnWP/7ylwlX2O+sL6he/+IV/If2wzlabM2eOzZ8/3+rWrWstW7bM1m7SrywkoC/RtWvXmn7MYRAoCYGFCxfarFmzvEDVrl27khzCPhBIEtB3jX7MYRAoCYFly5bZlClTvBi+6667luQQ9oFAkoC+bxKJRPI5CxDYFIFVq1bZxIkT/S76vtE8MFtt7733tk6dOpWqe5Xch4FPQ6mQsbMI3HrrrfbQQw9Z9+7d7YEHHgAKBCAAgTIj8Nxzz9m1115r7du3t9dff73MXocTQwACEPj444/trLPO8ncXv/jiC4BAAAIQKDMCkydPtl69evnzf/rpp9agQYMye62KODE5GiqCOq8JAQhAAAIQgAAEIAABCEAAAhDIUwIIDXl6YRkWBCAAAQhAAAIQgAAEIAABCECgIgggNFQEdV4TAhCAAAQgAAEIQAACEIAABCCQpwQQGvL0wjIsCEAAAhCAAAQgAAEIQAACEIBARRAgGWRFUM+D11RG5mnTplnDhg1tt912y4MRMQQIQCBbCajKzYQJE0yl5vbZZ59s7Sb9ggAE8oDA4sWLbezYsT77e5cuXfJgRAwBAhDIVgIqbTly5Ejfvc6dO+ddaVSEhmx959EvCEAAAhCAAAQgAAEIQAACEIBADhIgdCIHLxpdhgAEIAABCEAAAhCAAAQgAAEIZCsBhIZsvTL0CwIQgAAEIAABCEAAAhCAAAQgkIMEEBpy8KJlS5eXL1/u8zRkS3/oBwQgkJ8E1qxZYzNmzDDFMmIQgAAEypKAvmeUFwaDAAQgUF4EVq5cabNnz7ZEIlFeL1kur0OOhnLBnH8vsnbtWrvkkkts0aJF9vjjj+ffABkRBCBQ4QRmzZpld999tw0ZMiT5z7dDhw7+u2eXXXap8P7RAQhAIH8IzJ8/32655RYbOnSoH1T9+vXtyCOPtN///vdWuTL35fLnSjMSCGQfgcsuu8yGDRtmAwcOtOrVq2dfB7ewR3xzbiG4Qj5Mdxf1z/iLL74oZAyMHQIQKEMCUvcvv/xy+/zzz+3EE0+0u+66y8466yx/p/Giiy7ijmMZsufUEChEAvfee6+NHj3af89I4DziiCPsmWee8b93CpEHY4YABMqHwAsvvOBFhvJ5tfJ9FYSG8uWd8682btw4/0/4rbfeyivFLecvDAOAQJ4RGDRokKmM7mmnnWYXXnihL2t55pln2gUXXGDLli2zl19+Oc9GzHAgAIGKIqDyubqT2K9fP9P3TMeOHe3888+3Aw44wN555x2T8IlBAAIQyDSByZMn23333WcNGzbM9Kmz4nwIDVlxGXKnE3Ih1I/82267zdq2bZs7HaenEIBAzhHYb7/9/F3FeMcPPPBA/1Q5GzAIQAACmSBQs2ZNu/jii+2EE05IO92uu+5q8uJcvHhx2nqeQAACENhaAvpuueGGG0y/dbp167a1p8vK46tmZa/oVNYS0N3EY445xnszPPzww1nbTzoGAQjkNgHFRqsVtY8++sivat26ddFNPIcABCCwRQSaN29uanGbO3eu93Jo06aNNW3aNL6JZQhAAAJbTeDf//636XvmX//6lz322GNbfb5sPAFCQzZelSzu00knnZTFvaNrEIBAPhOQ+7JipnX3sXfv3vk8VMYGAQhUEAHlhXnggQfs66+/tvbt29s//vGPCuoJLwsBCOQrAeW5e+qpp+zGG2+07bbbLl+HaYRO5O2lZWAQgAAE8ofAqlWr7JprrrGJEyeakkFuv/32+TM4RgIBCGQNAZW21PdLnTp1fAnvDz74wNatW5c1/aMjEIBAbhNQCLoEBnltdu3aNbcHs5ne49GwGUBshgAEIACBiiXwww8/2FVXXWVjx471ySCPPvroiu0Qrw4BCOQtgaOOOsrUJG7ec889vuJN3bp1iw3lylsIDAwCECgzArfeeqsvmatE1/luCA35foUZHwQgAIEcJqC7i0rSNmvWLLvuuuusR48eOTwaug4BCOQKAdWyP/300+2VV16xIUOGIDTkyoWjnxDIYgKq3qcKN0o0q7LdwbRedsstt1izZs18hb+wLZcfERpy+erRdwhAAAJ5TGD69Ok+TGL58uV2xx132N57753Ho2VoEIBARRHQD/8RI0b4UrrKAROsXr16VqNGDZ+wLazjEQIQgMCWEli/fr3tuOOOtnTpUhs9enTyNKGyzZgxY3x1v+SGHF9AaMjxC0j3IQABCOQjASV+lCeDHlVjmioT+XiVGRMEsoOAPKfeeOMN/z1z6qmnJjv13nvv2YoVK6xDhw7JdSxAAAIQ2FICe+65p09qXfR43Ux54YUXfPUJeVPliyE05MuVZBwQgAAE8oiASj3Nnj3b9tprL3v99dc3GFnLli3t2GOP3WA9KyAAAQiUlkCfPn3sxRdftMcff9wSiYTtu+++piSQr776qjVo0MDOOOOM0p6S/SEAAQgUPAGEhoJ/CwAAAhCAQPYR0J1E2Zdffulb0R526tQJoaEoFJ5DAAJbREAhErfddpuPj5YHVTB9z1x66aVebAjreIQABCAAgZIRqOSU20TJdmUvCEAAAhCAAAQgAAEI5C8BxUrPmzfPJ2SrXbt2/g6UkUEAAhAoYwIIDWUMmNNDAAIQgAAEIAABCEAAAhCAAAQKiUDlQhosY4UABCAAAQhAAAIQgAAEIAABCECgbAkgNJQtX84OAQhAAAIQgAAEIAABCEAAAhAoKAIIDQV1uRksBCAAAQhAAAIQgAAEIAABCECgbAkgNJQtX84OAQhAAAIQgAAEIAABCEAAAhAoKAIIDQV1uRksBCAAAQhAAAIQgAAEIAABCECgbAkgNJQtX84OAQhAAAIQgAAEIAABCEAAAhAoKAIIDQV1uRksBCAAAQhAAAIQgAAEIAABCECgbAkgNJQtX84OAQhAAAIQgAAEIAABCEAAAhAoKAJVC2q0DBYCEIAABHKawNNPP21LliyxI4880lq1apXTY8mWzi9dutRefvllGz9+vM2YMcPq1q1re+21l2/77befbbPNNtnS1azpx8iRI23EiBG277772v7771+h/frmm2/89Zs8ebItXLjQWrRoYbvttpudfPLJtu2221Zo37b0xb/44gv77LPPrGPHjta5c2d/mmHDhtmoUaPsgAMOsH322WdLT81xEIAABCBQTgQQGsoJNC8DAQhAAAJbT+Daa6+1SZMm2SuvvILQsJU4V61aZVdccYU98sgjtnz58mLPtscee9hTTz1le+65Z7HbC3Xla6+9Zn/5y1/smmuuqTChYerUqXb22WfbwIEDi70MF110kZ1//vl2ww03WNWq2flzb+XKlXbzzTfbLrvsYqeddlpyHG+99ZZdffXV/v0ZhIaXXnrJ/vGPf9hNN92E0JAkxQIEIACB7CWQnf95spcXPYMABCAAAQjkPIH58+dbr1697PPPP7fKlSvb6aef7u8US1CQAKE7ys8995x/1B17eZL07ds358edLwP49NNP7ZhjjjFdx4YNG9o555zjJ9/y8pEA8eKLL1r//v3t73//u8kTQMJcnTp1sm74EkMefPBB+/e//53Wt9q1a1uTJk2yss9pHeUJBCAAAQhslABCw0bRsAECEIAABCCQnwTOPfdcLzLstNNO9sQTT9hBBx2UNlCJEJdccoldeumlds899/g7y8cdd5wXJdJ25Em5E1Do0KmnnupFBl2n//73v7bDDjsk+6HQgpNOOsl7CBx//PH2/vvv++uoCX222erVq32XKlWqlNa1Cy+80NQwCEAAAhDIXQIkg8zda0fPIQABCEBgIwRWrFjh49U3snmD1evXr/f5CfRYEpszZ85Gww02drwmiHIVL86UJ2Hx4sXFbSp23YIFC+zHH38sdtvmVr799tv+bneVKlXs+eef30BkCMcrN8Mdd9xhbdq0sYkTJ/q75GFb0Uf1f82aNUVXJ5/LS2LmzJnJ52W1sHbtWhObkpiuxfTp062k13xz55R3QZg4b27frdmuUIgpU6bYjjvuaC+88EKayBA/rzwebrvtNr/qoYcesk8++SS+eauX161bZ9OmTSvVmDf3PtnaTun6632WSCRKdCp9jvW5xCAAAQhAIPMEEBoyz5QzQgACEIBAORK46667rGXLlqa47jfffNOHANSrV8+7lMuV/NZbb93oxOOZZ54x3fWV+3nz5s2tZs2adsIJJ5gS7BU1TUp/8YtfWIMGDaxp06Y+aeLOO+/sz190shr6NGDAAD+pV/jBdtttZ/Xr17dDDz3UJ7XT+bVdMeg6p5pi1d97772iL+2fa0L029/+1vezUaNGPtGf9tdrlXRipRPpDrjsjDPO8MkM/ZON/JEYoRj666+/3icYDLuJqZir/7/5zW88P7m6F70L/eSTT1qHDh1MrvCaGGv8Rx99tH377bfhVP5RQoTO17Zt27T14cnjjz/ut//xj38Mq/yjPDK6d+9umjD27t3bn79x48bWqVMn+/Of/1ysGKPJuZIM1qpVyydOVEiBkjoqvGBL7LHHHvNijF5XyRflHTJ06NDkqSTAKNeFxjdmzJjk+viCzqHtykuwKdPkXixkui7iuinT9Tj88MP9Lv/73/+Su24pb41FuSl23313z099FkeJUX/7299ME/24bex9oveMjlV4juyyyy7zz+VdI5MXjbYr9KMkJvFMnzFdS73P9Fnq06ePfffddxscPm/ePB9qomulz7G+K/TZP/PMM23RokUb7M8KCEAAAhDYQgLuxwkGAQhAAAIQyAkCbiKqW5UJNylM9tfd4fXrXJb9hEt6l3AZ9xM///nPEy4zfcK5ZPttLolkcv+w4CZ3fnv16tUTLv9AwoUTJNyEw+/vJo0Jl3Qy7JpwFRkSbhLjt7lJVuL3v/99wrmvJ5x44Nd169Yt4SZvyf1Dn5yIkXA5EBJuQpzQspuU+f31/NFHH004r4GEm+QmXCK8ROvWrf02vY67K5s8lxbUl7Bd4/vDH/6QcEJBwrnM+2PcpCrhxI60Y4p7oj6GPri4+OJ2KdE68dR16NGjh38Udz13yQeTx5911ll+nRNv/Ni1zYkqyXUuuV9yX+eB4tdXq1YtuS6+cN999/ntLhdBfLVnu/322yfatWuXcGJR4rzzzkv86U9/SjgBxu+v90TcnAeHv+bqkxN8EhdccEHi4IMPTjhBxe9/5513xnff6PJ1113n93eTYf/oJrkJN6lPHHHEEf65xuGSaCaP//Wvf+3Xu+SbyXXxhUMOOcRvHzx4cHz1Bssffvih30+sly1btsH24laEayVOwbaEt47RZ0qvLdbHHntsQuMKnxmt1/sybuG1i75PdB31mQv89BnQcycY+MNdwscNeF155ZV+nbbFLbyGmJ944omJiy++OOHEJ7+vq6CScMJdcnfnxZLQtVJf9Rl0wmHil7/8ZUJstE5jKSnX5ElZgAAEIACBYgnoLggGAQhAAAIQyAkCmxIaNFHQBD8+4f7Xv/7lJxCaGDm39uQYP/jgg4QmJlrvEh8m1+tYCQI6V79+/fx6dxc34e6Q+3WaxLi7tsn9nZdDYu+99/bb4hOgIDToPFdddVVyf3fHNCkYaNstt9yS3KaJnCsn6c9VdMLr3OD9+t/97ncJ9SeYc9dPHHjggX7bf/7zn7B6o4/uDq/fV6/tEkFudL/NbQiTO53n8ssvTzhvi8SECRMS4iGTiKBt7u5y4ssvv0w73d133+23OQ+IhHjItmTiq+Mk4uh1NEGMC0PORT/RtWtXvy3OWKKC9nfeLzo8ac5LwK93d+bT3j/JHYosBKFB59L1jb/nnOeAP5fGFyatH3/8sV+nfjqvhLSzqd86j97bmzPnjeL3lcBUUgvXQq/hwm38YVvC21Un8a/tvHjSPks6ofNA8Nsk4OjcwTb3PnFeBP64ou/dkgoNw4cP98KR82BIjB49Orysf3TeEf7cEqGCCPjuu+/6dfrMxE2fqV133dVv0/XDIAABCEBg6wkQOuH+82IQgAAEIJD7BNxEwbuexxPLuTvWPoGh4vZnzJiRHKRctOUG7u6Sejf6sEHH/vOf//RhAlp2/2bt1VdftREjRpibsPhtCicIJjft+++/3z+98cYbN8jBoFAAN2kKu3vXfrn6yxT2oGSLwWrUqOHd//U87vKtCgMqpyhXcuVMiJcqdEKJuYmuP4VCBTZngYEqTcj9vaiJidiEJjf90JTPoajJZV7u7e7OsecjHrLQFzEpWhrTeR34ihdz5871YR9Fz7klz+XOr74Ekwt9yE/wwAMPhNU+J4OexJMn6vn//d//mZvs+goICk8oqbVv397++te/Wvw95+6Q22GHHWYan0IiZAqn0PVW+I3zSkg7fQiFUCjL5izkuVDISEnNCWHJXWfNmpVcLu2C3hsKM1KojhPp0g5X1RLl9HAigyk0oaht7H1SdL/SPld5UX1GFbax1157pR2ua6oQGeUXCYzFX6brr+OC6TOl9/yzzz5rzrskrOYRAhCAAAS2ggBVJ7YCHodCAAIQgED2EFCcvSbQcdOESJNfJa3TJCiYyjfKNHEqapo8ulCJ5Opx48b5ZeeGnzbJDzt06dLFNPH7/vvvfe6B+IRH+QmK9ils1wQwPkHV+Zo1a+ZP6+6Eh9PbyJEj/bJyORSXuM55Qfg4eU2iFi5c6HNBJA8usqDJoMzdgfd5DVq0aJG2hxgpD0VxJo6qZhA3jSUuvGibJurKcaGxqTpCceZCW+ydd95J5qoobp/SrHOhJxvsLi6KwZdooySEEkOcC78vpfizn/3MfvWrX9lRRx1lLoTC5+ZQjH5pzbnqF/ueUIUOVXsYNWpU8pR6PeWY0IQ2iE3aqEmwWEmg2JwpH4JM4ympxd9LyiOxpea8aUwtbsrJ4DwybMiQIV5oUDLMonkatH9x75P4ebZ0OXw2lJOjOIHjyCOP9NfAeTv4l5AApM+j8/IwvQf0/lTlDn3m9X5RwyAAAQhAIDMEEBoyw5GzQAACEIBABRMId9OLdiNMzsIdTE2EgnhQdKJd9Fg9/+qrr/zq1q1b+8fi/gShQRPsICRov+LuPIeJuRI6FrW4t0LY5kIS/GL//v19YsmwvrhH7StBYmPmXPeTm+SlUXT8EiKKJnTUXXS9dnEmL4+ipomnJpy6aywvjeIscCku6WZx+29qnZJQKolncaZkoLNnzzZNNHWnWl4OqtgwcOBAcyEcvqmPmoDqDrgEi6LiT3HnDes29p6Q94ksnvRSQoLuwCsZ5b333uvZuJAKP1GX8KC+bs7Ce1yMS2pff/2131XXWgkQt8YkcMjzw+WS8J8hiTjydIhb+JzF1xX3Polv35JlVWlRpQ+ZEkFuysJnSNdFCSjlgSH2ajL1T6Kjyr6G9+amzsc2CEAAAhDYPAGEhs0zYg8IQAACEMgBAsVN0ovrtsoaKuu+JpRh0l/cfmFdKFm4qfOHyak8BeK2uaoA8X03thzKWMr9fnN3XHXXflOmO/wSAORC7+LbzSXgS9tdk26FZ8RNk7GNCQ3FjW9reBU3SVVfik5m4/3bmJihfUIYRLh2mmjLk8Ll6PATfi1rsqxqJWoKD3n66afNJQiNv8RGl8N5i+4QvFjifZO3iksW6aujKBRGHjIhtELeDiWxcP2XL1/uPWhKMikOQpkqXxS10vCWYOHyXiQ9B/Ta8tyQZ07Pnj3NJYf04SJFX0PPi3ufFLdfadaFz4WugUvOuslD4yKOvFBcrg5TxRld848++siHV7h8Lvbwww97IULXCYMABCAAga0jgNCwdfw4GgIQgAAEcoyA4vddlnkfOqCcBcXdlR40aJAve6c8BnKrlk2ePNk/FvcnbNsa1/Tizqt14fU1UVR8/NaYJsAKW1DZwddff93ntMj0JFB5KSS8iK0EgqLx/Op/UV5hYh6EgaJjDPsXXa/n8rjQccWJRgqZ0fp4ngK9lko+hrKPututu9zKKyGXepUXVQnOklhx7vo6Tl4TMrGIm8IzVIZVr6cyqvJu0PtRk9+SmHIduESGprwdyn/hKoekHaa8EPISUViATOUaQw4RVxklue+W8Nb7RuN1SVK9GBXPcyHhLngXFBXbki+a4QUJN/JWkuiikJR4fzb3UhLcLrroIt8ULqQwF+XaGDZsmPc6QWjYHEG2QwACENg8gfRg1s3vzx4QgAAEIACBnCegxJEyTTCKmnItKJb/gAMOMMW3u3KSfhfdhS7uDvCYMWP8XXGFHYQ7zkXPuTXPledBJnf/4C0QP58mtQoLULy53Mk3Z64EoZ/cqt+aUIc7wxs7blOT/OKOURiDxBtNON94443idvEJNrVB+S1kYqe7/+KrPBNFLbj/F12v5wqFKZpgUesVGjJnzhwTP/Vp6tSpPseE7r7H8whIyLn66qu9h4GO++yzz/RQIpMoUZzpvSIrmghTd/1dSVRzZRy9oKEkpfJsKI3YowSmMlce1YYOHeqXwx8JGPI60Bi1zVVJ8aEjEqm0HKy0vCUihLwmrsLEBpN6hSIFgWFjYlF47fjjxjyB4vtsbFnHhs/mxt5nruqFKU+DWMlcNRc71OXkcKVH/XP90Xujd+/eyX00zvj7I7kjCxCAAAQgUCoCCA2lwsXOEIAABCCQDwRcSUY/DFWEUAx/3ORCrcmS4vY1KTzllFP8nWkl9rv99tvju3ohQpUtNEHWHd969eqlbc/EEyUsVPZ8ufjrzm1c7NDy2Wef7WPN5UFQv379zb6kXN6VI0CmWHtNxBRKoDvDwTR+jVfiheLZZWJRUvvTn/7kd1VFjFApIRyrSbhCFHQ3Ol5pIdz9V9WPuClZorwANmXiEk+QKPEkVPQISR51B1zjlGDjShimnU6eF0FgKE3VAQlVSu4YN032XRlFXyUkPj7to5AMvU8kYIX+Fd0nfq7ilpVLQPkeNLHXe1ShLuE9oVAYMXelS314gMYpjw5XFtOLOfHzlYa3hIkgCijkJm4SS+KJLOXdUFILuTVCNZSSHhf2C+8zPaq6RNwkPunzrfCYkItEzLReuTKKhuOEfA3KcbKxkJj4+VmGAAQgAIFNEyB0YtN82AoBCEAAAnlIQHcw5br+4osv2j777OMn04rfV3y+YtobNGhgoSyiJoeamCseXZNDTSKVvE/eA8pdoAlOnz59fGnEskClieJ9993n71IrmeEnn3ziPRE0sX7llVe8q7z6G1zkS9IHTQzlPq/kd6oYoAmrJldioddT8sRQpUPjv+SSSyxM6kpyfokfumusib3Oqcm1EvFpMq8SgvJekOAQvDV0zt/+9rcm0eaWW27x+ynBn0IENAE85phj/P4be+0vv/zSe5PodTQhVvy9vCDUj3AnX+NTuIHGrNfS5Fsijia5mozKk0UeFvEQg429Xliv2P9fufwKuqOuyge6G65xK1xEYS7xHA3hGAkf8goYO3asTzwoD4TS2iOPPOLvxD/44IN+fHL7lxilZJ/yVImLLhKfFEKi/sWtNLz12dA1UKlXMZUXhq6rRAeNXcKJBCwxlLAUT4gaf82iy/J8kalEqj5Xv/71r9PEJ79xE3/0GQ6fY41f72sldpSANmDAAC/A/OY3v0mGyYi9PtdK0inxQR49O++8s3+f6hgJKkEA2sTLsgkCEIAABEpCwKngGAQgAAEIQCAnCLi7sAn3vy3hJtjJ/t5www1+nburnVwXX9hll138dufeHV+dcHftE25Sm3ATMb9d51Vzk+6EmwSm7asnWucSMibcBD25v87tYr0TLsFk2v6b6pObgPrj3YQ37Rg9cRNIv81N5jbY5ioNJJxLfMJNmJOvr/66yXLCTbQ32L8kK9zEMHHFFVck3N3+tHM6sSHh3NITbmKWcELKBqdyLul+/+uuu26DbWGFcz9PuDvKCeflkTy3CxFIuHKCCRdyEHZLe3RCSsIltEzu73JeJLTOhUH4deecc07a/uFaOPEl4SaMyeNcLoOEmzAmXKhJ2v564ibnCTfJTO4rhk6oSbiEggl3N36D/YtboXHrOOeSn7jssssSTlBIns+F5STUn02Zm4j7/a+//vpN7bbZbS7Phn9POHEl+frqlxMG/HicQJVwlSr8Nr13i34GSsPbhU8knHdP2us4ESrhJvcJlxsi4cQCv82VwEz2e3PvE+d5knAeNcn3tN5vMr1vNA69N4O5kBG/Ttvi5rwUEq6CSELvFR0Tmp477xj/OY/vr/e8E0rSPsd6H7nwkoQT3eK7sgwBCEAAAltBoJKOdV/KGAQgAAEIQKBgCSjPgSoxKF6/YcOGm+SgEIPx48ebSg2WJgHdJk9aio3K06C79XL9VnJAeTNsremngEJIdOdbng5Kghnc2rf23Dpe+RGUSFB3uotLDhl/Dbm3h7KQIZdGfHt8Wd4X2l/u+robrf4rbGJzx+kYeTJof13DklRviL9u0WVdE3lV6C65Eo1uzuStMXLk/7d3R7kJwzAYgNFu38PtDpyhJ1j/ShESUjehiph6X186QMPJlz4Zx/net8OMX/X/+p/fPk8Fw31rVprmj6lqyDiGc57XZVlu2RKUCpHnvhGveGcM6aGRKp6ccJJqgKzB2SvVM+kDke0tZ74vz3C2GKXZYypoUsVydK3ruj+X8cnz/kqfjKPv9D4BAgQIPAQkGh4W/iJAgAABAgQuJPCcaLjC0LMtJWX+adiY8v5ZVxI97zgVZdb4xSFAgACBawkcp3qvNQ+jJUCAAAECBAh8pEB+rU/1RO6jZ8S4zxqwJMMsaXEIECBAIAISDZ4DAgQIECBAgMAbBbKtIdslxrX1mtgbUY7X7gQIECBAoJuAREO3FTUfAgQIECDwTwRSFZD+Emf29c+gykkIOZYypzNszTBvWwPRGWHFIECAAAECZQJ6NJTRC0yAAAECBAgQIECAAAECBPoJfPWbkhkRIECAAAECBAgQIECAAAECVQISDVXy4hIgQIAAAQIECBAgQIAAgYYCEg0NF9WUCBAgQIAAAQIECBAgQIBAlYBEQ5W8uAQIECBAgAABAgQIECBAoKGAREPDRTUlAgQIECBAgAABAgQIECBQJSDRUCUvLgECBAgQIECAAAECBAgQaCgg0dBwUU2JAAECBAgQIECAAAECBAhUCUg0VMmLS4AAAQIECBAgQIAAAQIEGgpINDRcVFMiQIAAAQIECBAgQIAAAQJVAhINVfLiEiBAgAABAgQIECBAgACBhgI/66I4vURe9nsAAAAASUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p1 <- ggplot(df, aes(x = groups, y = estimate)) +\n", + " geom_line() +\n", + " geom_ribbon(aes(ymin = lwr.ci, ymax = upr.ci), alpha = 0.1, col = \"blue\", fill = \"blue\") +\n", + " theme_bw() +\n", + " xlab(\"Income Groups by Quartiles\") +\n", + " ylab(\"ATE\") +\n", + " ggtitle(\"Under conditional ignorability\")\n", + "\n", + "ggsave(\"figures/401K-groups-noconfounding.pdf\", p1, width = 4.5, height = 4.5)\n", + "p1" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "90b555b3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:06.692037Z", + "iopub.status.busy": "2026-09-11T14:55:06.691679Z", + "iopub.status.idle": "2026-09-11T14:55:07.062469Z", + "shell.execute_reply": "2026-09-11T14:55:07.061975Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RV_alpha (alpha = 0.05, threshold theta = 0)\n", + " partially linear : 5.5%\n", + " nonparametric : 4.7%\n" + ] + } + ], + "source": [ + "rv.plm <- robustness_value(dml.401k.plm)\n", + "rv.npm <- robustness_value(dml.401k.npm)\n", + "\n", + "cat(sprintf(\"RV_alpha (alpha = 0.05, threshold theta = 0)\\n partially linear : %.1f%%\\n nonparametric : %.1f%%\\n\",\n", + " 100 * rv.plm[\"ate\"], 100 * rv.npm[\"ate\"]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "99f710a6", + "metadata": {}, + "source": [ + "## The Main Confounding Scenario\n", + "\n", + "The main scenario assumes that omitted firm characteristics explain as much outcome variation as the\n", + "maximum employer match accumulated over three years, which implies approximately $C_Y^2 = 0.04$. On\n", + "the treatment side it allows an additional 2.5 percentage points of eligibility variation beyond the\n", + "observed controls, corresponding to $q_\\alpha \\approx 0.03$. We take $|\\rho| = 1$, the conservative\n", + "case in which the two induced errors are perfectly aligned.\n", + "\n", + "What $q_\\alpha$ measures depends on the target: for the partially linear model it is the\n", + "nonparametric partial $R^2$ of the treatment with the omitted covariates, and for the\n", + "binary-treatment ATE a gain in average inverse-variance precision.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8a03f2af", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.064135Z", + "iopub.status.busy": "2026-09-11T14:55:07.063812Z", + "iopub.status.idle": "2026-09-11T14:55:07.069566Z", + "shell.execute_reply": "2026-09-11T14:55:07.069163Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "cf.y <- 0.04 # C_Y^2\n", + "cf.d <- 0.03 # q_alpha\n", + "rho2 <- 1 # |rho| = 1\n", + "\n", + "bounds.plm <- dml_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "eb62473e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.071060Z", + "iopub.status.busy": "2026-09-11T14:55:07.070750Z", + "iopub.status.idle": "2026-09-11T14:55:07.077621Z", + "shell.execute_reply": "2026-09-11T14:55:07.077164Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "bounds.npm <- dml_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b6c06ec7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.079155Z", + "iopub.status.busy": "2026-09-11T14:55:07.078850Z", + "iopub.status.idle": "2026-09-11T14:55:07.090590Z", + "shell.execute_reply": "2026-09-11T14:55:07.090101Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "cb.plm <- confidence_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)\n", + "cb.npm <- confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6a24c23a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.092294Z", + "iopub.status.busy": "2026-09-11T14:55:07.091969Z", + "iopub.status.idle": "2026-09-11T14:55:07.102416Z", + "shell.execute_reply": "2026-09-11T14:55:07.102009Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 2 × 5
ModelShort ATEBias boundATE bounds95% conf. bounds
<chr><chr><chr><chr><chr>
Partially linear9,0384,195[4,845, 13,231][2,636, 15,514]
Nonparametric 8,2534,533[3,709, 12,796][1,695, 14,851]
\n" + ], + "text/latex": [ + "A data.frame: 2 × 5\n", + "\\begin{tabular}{lllll}\n", + " Model & Short ATE & Bias bound & ATE bounds & 95\\% conf. bounds\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t Partially linear & 9,038 & 4,195 & {[}4,845, 13,231{]} & {[}2,636, 15,514{]}\\\\\n", + "\t Nonparametric & 8,253 & 4,533 & {[}3,709, 12,796{]} & {[}1,695, 14,851{]}\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 2 × 5\n", + "\n", + "| Model <chr> | Short ATE <chr> | Bias bound <chr> | ATE bounds <chr> | 95% conf. bounds <chr> |\n", + "|---|---|---|---|---|\n", + "| Partially linear | 9,038 | 4,195 | [4,845, 13,231] | [2,636, 15,514] |\n", + "| Nonparametric | 8,253 | 4,533 | [3,709, 12,796] | [1,695, 14,851] |\n", + "\n" + ], + "text/plain": [ + " Model Short ATE Bias bound ATE bounds 95% conf. bounds\n", + "1 Partially linear 9,038 4,195 [4,845, 13,231] [2,636, 15,514] \n", + "2 Nonparametric 8,253 4,533 [3,709, 12,796] [1,695, 14,851] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dollars <- function(v) formatC(round(v), format = \"d\", big.mark = \",\")\n", + "\n", + "row <- function(bounds, cb, label) {\n", + " b <- coef(bounds)[, \"ate\"]\n", + " data.frame(\n", + " Model = label,\n", + " `Short ATE` = dollars(b[\"theta.s\"]),\n", + " `Bias bound` = dollars(b[\"bias.bound\"]),\n", + " `ATE bounds` = sprintf(\"[%s, %s]\", dollars(b[\"theta.m\"]), dollars(b[\"theta.p\"])),\n", + " `95% conf. bounds` = sprintf(\"[%s, %s]\", dollars(cb[\"ate\", 1]), dollars(cb[\"ate\", 2])),\n", + " row.names = NULL,\n", + " check.names = FALSE\n", + " )\n", + "}\n", + "\n", + "table2.3 <- rbind(row(bounds.plm, cb.plm, \"Partially linear\"),\n", + " row(bounds.npm, cb.npm, \"Nonparametric\"))\n", + "\n", + "table2.3\n" + ] + }, + { + "cell_type": "markdown", + "id": "89ff8ba9", + "metadata": {}, + "source": [ + "Even granting an adversarially aligned confounder as strong as three years of maximum employer match,\n", + "the lower confidence bound on the ATE remains positive in both specifications." + ] + }, + { + "cell_type": "markdown", + "id": "d6f07816", + "metadata": {}, + "source": [ + "### Group Effects Under the Scenario\n", + "\n", + "The same groups again, now with the sensitivity bounds in red and the wider confidence bounds in\n", + "blue.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b0a3776", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.104074Z", + "iopub.status.busy": "2026-09-11T14:55:07.103749Z", + "iopub.status.idle": "2026-09-11T14:55:07.236165Z", + "shell.execute_reply": "2026-09-11T14:55:07.235716Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "bds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0)),\n", + " c(\"lwr.bound\", \"upr.bound\"))\n", + "cbds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0.95)),\n", + " c(\"lwr.cbound\", \"upr.cbound\"))\n", + "\n", + "df2 <- cbind(df, bds[-1, ], cbds[-1, ])\n", + "\n", + "p2 <- ggplot(df2, aes(x = groups, y = estimate)) +\n", + " geom_line() +\n", + " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), alpha = 0.1, col = \"red\", fill = \"red\") +\n", + " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), alpha = 0.1, col = \"blue\", fill = \"blue\") +\n", + " theme_bw() +\n", + " xlab(\"Income Groups by Quartiles\") +\n", + " ylab(\"ATE\") +\n", + " ggtitle(\"With confounding\")\n", + "\n", + "ggsave(\"figures/401K-groups-confounding.pdf\", p2, width = 4.5, height = 4.5)\n", + "p2" + ] + }, + { + "cell_type": "markdown", + "id": "0ba2ae32", + "metadata": {}, + "source": [ + "The posited confounding scenario substantially widens the range of plausible effects but leaves most of the adjusted region positive. Only the second quartile has a small excursion below zero." + ] + }, + { + "cell_type": "markdown", + "id": "651c1a8d", + "metadata": {}, + "source": [ + "## Benchmarking\n", + "\n", + "Rather than posit $C_Y$ and $C_D$ in the abstract, we can calibrate them against a covariate we do observe. The procedure is as follows: drop the covariate from $X^o$, refit, and read off the gains it supplied. Latent confounding is then described relative to the chosen benchmarking variable." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b4a7321f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.238274Z", + "iopub.status.busy": "2026-09-11T14:55:07.237941Z", + "iopub.status.idle": "2026-09-11T14:56:25.265989Z", + "shell.execute_reply": "2026-09-11T14:56:25.265444Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "set.seed(2)\n", + "invisible(capture.output(\n", + " bench.plm <- dml_benchmark(dml.401k.plm,\n", + " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", + "))\n" + ] + }, + { + "cell_type": "markdown", + "id": "085c0865", + "metadata": {}, + "source": [ + "`gain.Y` is the implied $C_Y^2$ and `gain.D` the implied $q_\\alpha$. Income is a strong predictor of both eligibility and financial assets; IRA participation and two-earner status are considerably weaker.\n", + "\n", + "The `rho` column records the alignment each covariate actually exhibits. None is anywhere near $\\pm 1$, which is a first indication of how conservative the worst-case analysis is.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9837c2cf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.271430Z", + "iopub.status.busy": "2026-09-11T14:56:25.271070Z", + "iopub.status.idle": "2026-09-11T14:56:25.284321Z", + "shell.execute_reply": "2026-09-11T14:56:25.283894Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.14100.0503-0.3266
pira0.04760.0075-0.1980
twoearn0.04810.0106 0.2931
\n" + ], + "text/latex": [ + "A matrix: 3 × 3 of type dbl\n", + "\\begin{tabular}{r|lll}\n", + " & gain.Y & gain.D & rho\\\\\n", + "\\hline\n", + "\tinc & 0.1410 & 0.0503 & -0.3266\\\\\n", + "\tpira & 0.0476 & 0.0075 & -0.1980\\\\\n", + "\ttwoearn & 0.0481 & 0.0106 & 0.2931\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 3 × 3 of type dbl\n", + "\n", + "| | gain.Y | gain.D | rho |\n", + "|---|---|---|---|\n", + "| inc | 0.1410 | 0.0503 | -0.3266 |\n", + "| pira | 0.0476 | 0.0075 | -0.1980 |\n", + "| twoearn | 0.0481 | 0.0106 | 0.2931 |\n", + "\n" + ], + "text/plain": [ + " gain.Y gain.D rho \n", + "inc 0.1410 0.0503 -0.3266\n", + "pira 0.0476 0.0075 -0.1980\n", + "twoearn 0.0481 0.0106 0.2931" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "out <- summary(bench.plm)$benchmarks\n", + "round(out[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" + ] + }, + { + "cell_type": "markdown", + "id": "70083858", + "metadata": {}, + "source": [ + "## Contour Plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e65ef369", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.285890Z", + "iopub.status.busy": "2026-09-11T14:56:25.285593Z", + "iopub.status.idle": "2026-09-11T14:56:25.289940Z", + "shell.execute_reply": "2026-09-11T14:56:25.289562Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "bumps.plm <- list(pira = c(0.010, 0.009),\n", + " twoearn = c(0.020, -0.002),\n", + " inc = c(0.010, 0.009),\n", + " inc4 = c(0.015, -0.005))\n", + "\n", + "bumps.npm <- list(pira = c(0.005, 0.009),\n", + " twoearn = c(0.020, 0.005),\n", + " inc = c(-0.010, 0.009),\n", + " inc4 = c(0.015, 0.000))\n", + "\n", + "contour_panel <- function(fit, bench, rho2, title, bumps = bumps.plm) {\n", + " ovb_contour_plot(fit,\n", + " which.bound = \"lwr\",\n", + " cf.y = NULL,\n", + " rho2 = rho2,\n", + " col.contour = \"blue\",\n", + " label.bump.x = 0.015,\n", + " label.bump.y = 0.003,\n", + " lim.x = 0.16)\n", + " title(main = title)\n", + "\n", + " add_bound <- function(cfd, cfy, label, bump.x, bump.y, ...) {\n", + " sensemakr::add_bound_to_contour(\n", + " r2dz.x = cfd, r2yz.dx = cfy,\n", + " bound_label = label,\n", + " bound_value = confidence_bounds(fit, cf.y = cfy, cf.d = cfd, rho2 = rho2),\n", + " label.bump.x = bump.x, label.bump.y = bump.y, ...)\n", + " }\n", + "\n", + " add_bound(0.03, 0.04, \"Max Match\", 0.015, 0.003, point.pch = 25, point.bg = \"red\")\n", + "\n", + " # one-times each observed benchmark\n", + " add_bound(bench[\"pira\", \"gain.D\"], bench[\"pira\", \"gain.Y\"], \"1 x Part. in IRA\",\n", + " bumps$pira[1], bumps$pira[2])\n", + " add_bound(bench[\"twoearn\", \"gain.D\"], bench[\"twoearn\", \"gain.Y\"], \"1 x Two Earners\",\n", + " bumps$twoearn[1], bumps$twoearn[2])\n", + " add_bound(bench[\"inc\", \"gain.D\"], bench[\"inc\", \"gain.Y\"], \"1 x Income\",\n", + " bumps$inc[1], bumps$inc[2])\n", + "\n", + " # a quarter as strong as income\n", + " add_bound(bench[\"inc\", \"gain.D\"] * 0.25, bench[\"inc\", \"gain.Y\"] * 0.25, \"1/4 x Income\",\n", + " bumps$inc4[1], bumps$inc4[2])\n", + "\n", + " invisible(NULL)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "2522b94d", + "metadata": {}, + "source": [ + "### Partially Linear Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "445dba1a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.291459Z", + "iopub.status.busy": "2026-09-11T14:56:25.291151Z", + "iopub.status.idle": "2026-09-11T14:56:25.693510Z", + "shell.execute_reply": "2026-09-11T14:56:25.693103Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “paste(\"PLM, |\", rho, \"| = 1\")”" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 7, repr.plot.height = 7)\n", + "\n", + "pdf(\"figures/401K-contour-plm-lower.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3f14d055", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.695962Z", + "iopub.status.busy": "2026-09-11T14:56:25.695610Z", + "iopub.status.idle": "2026-09-11T14:56:26.093514Z", + "shell.execute_reply": "2026-09-11T14:56:26.093039Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “paste(\"PLM, |\", rho, \"| = 1/2\")”" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "rho2.half <- (1/2)^2\n", + "\n", + "pdf(\"figures/401K-contour-plm-lower-rho2.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))" + ] + }, + { + "cell_type": "markdown", + "id": "8c69bfb3", + "metadata": {}, + "source": [ + "Under worst-case alignment a confounder as strong as income crosses the zero contour, whereas the matching scenario and the one-quarter-income benchmark do not.\n", + "\n", + "The second panel makes a different assumption: errors in the outcome regression and in the Riesz representer are only half as aligned. The lower endpoint then rises substantially. This comparison separates the strength of a confounder from the degree of adversity with which its two effects align, and none of the observed covariates exhibits alignment close to the worst case.\n" + ] + }, + { + "cell_type": "markdown", + "id": "69a6db36", + "metadata": {}, + "source": [ + "### Nonparametric Model\n", + "\n", + "The gains are target-specific, so this specification needs its own benchmarks. Here `gain.D` is read as a relative gain in propensity-score precision rather than as a reduction in residual treatment variance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "629fe818", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:26.095791Z", + "iopub.status.busy": "2026-09-11T14:56:26.095475Z", + "iopub.status.idle": "2026-09-11T14:57:52.635257Z", + "shell.execute_reply": "2026-09-11T14:57:52.634727Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13370.1324-0.221
pira0.02310.0000 0.000
twoearn0.00820.0000 0.000
\n" + ], + "text/latex": [ + "A matrix: 3 × 3 of type dbl\n", + "\\begin{tabular}{r|lll}\n", + " & gain.Y & gain.D & rho\\\\\n", + "\\hline\n", + "\tinc & 0.1337 & 0.1324 & -0.221\\\\\n", + "\tpira & 0.0231 & 0.0000 & 0.000\\\\\n", + "\ttwoearn & 0.0082 & 0.0000 & 0.000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 3 × 3 of type dbl\n", + "\n", + "| | gain.Y | gain.D | rho |\n", + "|---|---|---|---|\n", + "| inc | 0.1337 | 0.1324 | -0.221 |\n", + "| pira | 0.0231 | 0.0000 | 0.000 |\n", + "| twoearn | 0.0082 | 0.0000 | 0.000 |\n", + "\n" + ], + "text/plain": [ + " gain.Y gain.D rho \n", + "inc 0.1337 0.1324 -0.221\n", + "pira 0.0231 0.0000 0.000\n", + "twoearn 0.0082 0.0000 0.000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set.seed(2)\n", + "invisible(capture.output(\n", + " bench.npm <- dml_benchmark(dml.401k.npm,\n", + " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", + "))\n", + "\n", + "out.npm <- summary(bench.npm)$benchmarks\n", + "round(out.npm[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "2aacd3da", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:57:52.642707Z", + "iopub.status.busy": "2026-09-11T14:57:52.642270Z", + "iopub.status.idle": "2026-09-11T14:57:53.089117Z", + "shell.execute_reply": "2026-09-11T14:57:53.088690Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “paste(\"NPM, |\", rho, \"| = 1\")”" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pdf(\"figures/401K-contour-npm-lower.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5f1eab78", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:57:53.091609Z", + "iopub.status.busy": "2026-09-11T14:57:53.091253Z", + "iopub.status.idle": "2026-09-11T14:57:53.519948Z", + "shell.execute_reply": "2026-09-11T14:57:53.519500Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title “paste(\"NPM, |\", rho, \"| = 1/2\")”" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pdf(\"figures/401K-contour-npm-lower-rho2.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)" + ] + }, + { + "cell_type": "markdown", + "id": "14dd7abd", + "metadata": {}, + "source": [ + "The nonparametric contours are slightly more conservative, but the message is similar. Under perfect adversarial alignment, income-strength confounding can overturn the finding; the contextual matching scenario cannot. At $|\\rho| = 1/2$, even the lower confidence endpoint for the main scenario remains comfortably positive.\n", + "\n", + "The treatment-side gains for IRA participation and two-earner status come out near zero here, placing both markers on the vertical axis. This follows from what `gain.D` measures in this model: a covariate that barely moves propensity scores away from the middle of $[0,1]$ leaves average precision almost unchanged, however much it predicts the outcome.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1ffa2b69b17e05820dd4ad0d88c69a18fba1700f Mon Sep 17 00:00:00 2001 From: chansen776 Date: Fri, 25 Sep 2026 15:04:38 -0500 Subject: [PATCH 14/32] PM-4 Simulation Figures Code to reproduce the illustrative simulation figures in Chapter PM-4. dml_illustration_sim.py is the main wrapper. --- PM4/README_PM4_simulation.md | 150 +++++++++++++++++ PM4/SimCrossFitting.py | 9 + PM4/SimOrthogonality.py | 9 + PM4/dml_illustration_sim.py | 311 +++++++++++++++++++++++++++++++++++ 4 files changed, 479 insertions(+) create mode 100644 PM4/README_PM4_simulation.md create mode 100644 PM4/SimCrossFitting.py create mode 100644 PM4/SimOrthogonality.py create mode 100644 PM4/dml_illustration_sim.py diff --git a/PM4/README_PM4_simulation.md b/PM4/README_PM4_simulation.md new file mode 100644 index 00000000..6b8f10da --- /dev/null +++ b/PM4/README_PM4_simulation.md @@ -0,0 +1,150 @@ +# PM-4 figures: one learner throughout + +`dml_illustration_sim.py` fits **the same neural-network regressor +in every nuisance regression**, with identical architecture and hyperparameters. + +## Run + +```sh +python -m pip install -r notebooks/requirements-pm4.txt +python notebooks/dml_illustration_sim.py +``` + +`python notebooks/SimOrthogonality.py` and `python notebooks/SimCrossFitting.py` are equivalent +entry points for making just one figure; both reuse the same simulation cache. + +Defaults are 500 repetitions, n=1,000, p=10, and up to eight parallel processes. +Use `--reps 1000` to extend the saved run, or `--jobs 2` to reduce CPU use. +The simulation checkpoints every 25 completed repetitions and resumes on a +subsequent invocation. Replot cached results with `--plot-only`. + +Results are saved in `notebooks/figures/pm4_uniform/` as: + +- `SimOrthogonality.pdf` and `.png`; +- `SimCrossFitting.pdf` and `.png`; +- `simulation.npz`, `simulation.csv`, `summary.json`, and `plot_settings.json`. + +The uploaded figures in `notebooks/figures/pm4/` are not replaced by this script. +Changing simulation parameters requires a different `--output-dir`. Replotting +is possible with newer library versions; resuming a simulation requires the +versions recorded in its metadata so one run cannot mix implementations. + +## Shared design + +In both figures, each repetition draws independent controls X1,...,X10 and +independent errors V and epsilon, all standard normal. The partially linear +model is + +```text +Q = X2^2 - 1 +D = X1 + 0.2 Q + V +Y = 0.5 D + 2 Q + epsilon. +``` + +Thus the true treatment effect is 0.5. The nonlinear component is relatively +weak in treatment prediction but important in outcome prediction. This is a +standard weak-confounding-signal example: small errors in predicting treatment +can matter for an unprotected estimating equation. The population nuisance +functions enter only data generation and optional prediction-error diagnostics. + +## One learning algorithm + +Every fitted nuisance function uses `sklearn.neural_network.MLPRegressor`: + +- two hidden layers, each with 128 ReLU units; +- Adam, learning rate 0.001, minibatches of 128 observations; +- L2 penalty `alpha=0.01`; +- 100 training epochs, no validation-based early stopping; +- feature standardization and target centering learned on the training set only; +- targets are not divided by their standard deviations, so the same penalty and + squared-error objective apply in original outcome/treatment units. + +The network has 18,049 parameters for ten inputs, compared with 500 observations +in each training fold. Treatment and outcome are fitted in **separate scalar-output +networks**; there is no special multi-output architecture. Within a training set, +the two fits use the same random seed, giving matched initialization and batch +order. They do not pass fitted weights or predictions to one another. All these +settings are also used for the full-sample fits in Figure 2. + +## Estimators and why the nonorthogonal estimator is defensible + +Let m0(X) = E[D|X], ell0(X) = E[Y|X], and V = D - m0(X). In a partially linear +model, V is a valid instrument for D in the outcome equation: + +```text +E[(D - m0(X)) (Y - beta D)] = 0. +``` + +This moment identifies beta because E[V g0(X)] = E[V epsilon] = 0 and +E[V D] = E[V^2] > 0. Its directional derivative with respect to m is +`-E[h(X) g0(X)]`, which generally does not vanish. It is therefore a valid, +nonorthogonal moment, rather than an inconsistent estimator constructed to fail. +It is also the treatment-residual IV moment already computed in the original +`Figure1.m` (`rfsss`), although that script's main plotted comparison used its +other nonorthogonal estimator. + +For the two cross-fitted estimators, randomly divide the observations into two +folds. Train the same network procedure for D and Y on one fold, predict on the +other, reverse the roles, and pool all held-out predictions. Write v = D - m_hat. + +1. **Nonorthogonal, cross-fitted:** `sum(v * Y) / sum(v * D)`. + This uses the learned treatment residual as an instrument and does not + residualize the outcome. It needs no g_hat or preliminary coefficient. +2. **Orthogonal, cross-fitted:** `sum(v * (Y - ell_hat)) / sum(v**2)`. + This is the chapter's partialling-out estimator. +3. **Orthogonal, without cross-fitting:** use formula 2 with nuisance predictions + made on the same observations used to train both networks. + +Figure 1 compares estimators 1 and 2, using **exactly the same treatment +predictions**. Figure 2 compares estimators 3 and 2. The blue distribution is +identical in both figures. No extra learner is used in any stage. + +The nonorthogonal estimator also has higher variance even with oracle m0, since +it leaves g0(X) in the outcome. Its broader histogram is not exclusively an +effect of nuisance estimation. The visible displacement from zero is the +regularization-bias contrast; the narrower blue distribution is an additional +benefit of partialling out the outcome. + +## Plotting and reproducibility + +The completed validation run used 500 fresh-seed repetitions. With true effect +0.5, it produced: + +| Estimator | Mean estimate | Mean estimation error | +| --- | ---: | ---: | +| Nonorthogonal, cross-fitted | 0.5824 | +0.0824 | +| Orthogonal, without cross-fitting | 0.6245 | +0.1245 | +| Orthogonal, cross-fitted | 0.4864 | -0.0136 | + +Both comparisons show a rightward displacement relative to the cross-fitted +orthogonal estimate. The latter retains modest finite-sample bias; the plot does +not correct it. All score denominators stayed away from zero (minimum normalized +nonorthogonal denominator 0.814; minimum full-sample orthogonal denominator 0.108). + +Both plots use raw estimation error (estimate minus 0.5), the same bins and both +axis limits, a zero reference line, and the same blue cross-fitted estimates. + +Each repetition has its own seed, independent of worker scheduling. The final +validation seed 726031 is different from the exploration seed 395721. All +500 validation repetitions are retained. Network iteration-limit warnings are +recorded in the diagnostics because a fixed epoch budget is intentional. + +Prediction diagnostics distinguish training error, held-out error including +outcome noise, and error relative to the true conditional means. Score +denominators are saved to make numerical instability visible rather than +silently filtering repetitions. Monte Carlo standard deviations in the summary +describe the saved distributions; they are not estimated sampling standard +errors and never enter the plots. + +## Exploration record + +Pilots compared random forests, boosted trees, and neural networks across +linear, smooth nonlinear, interaction, and weak-confounding designs. Uniform +tree learners often made the cross-fitting comparison small; several network +configurations left appreciable bias after cross-fitting. A joint-output network +gave a strong cross-fitting example, but is not needed in the selected design. + +The selected weak quadratic design gave both contrasts using two ordinary +scalar-output networks and identical settings everywhere. A clean standalone +implementation was checked against the corresponding pilot before running the +fresh-seed validation. diff --git a/PM4/SimCrossFitting.py b/PM4/SimCrossFitting.py new file mode 100644 index 00000000..a6f148c7 --- /dev/null +++ b/PM4/SimCrossFitting.py @@ -0,0 +1,9 @@ +"""PM-4 Figure 2: orthogonal estimation with and without cross-fitting. + +Arguments are shared with dml_illustration_sim.py. All nuisance predictions come from +feasible fitted learners; no oracle predictions or manufactured fitting errors. +""" +from dml_illustration_sim import main + +if __name__ == "__main__": + main(figures=(2,)) diff --git a/PM4/SimOrthogonality.py b/PM4/SimOrthogonality.py new file mode 100644 index 00000000..792f6791 --- /dev/null +++ b/PM4/SimOrthogonality.py @@ -0,0 +1,9 @@ +"""PM-4 Figure 1: nonorthogonal versus orthogonal, both cross-fitted. + +Arguments are shared with dml_illustration_sim.py. The simulation cache is also shared +with SimCrossFitting.py, so making the second figure does not repeat the experiment. +""" +from dml_illustration_sim import main + +if __name__ == "__main__": + main(figures=(1,)) diff --git a/PM4/dml_illustration_sim.py b/PM4/dml_illustration_sim.py new file mode 100644 index 00000000..f96924b5 --- /dev/null +++ b/PM4/dml_illustration_sim.py @@ -0,0 +1,311 @@ +"""PM-4 figures using the same neural-network learner for every nuisance fit. + +Run `python notebooks/dml_illustration_sim.py` to simulate and draw both figures. +All figures show raw estimation errors. No standard errors are estimated. +See README_PM4_uniform.md for the design and the nonorthogonal moment equation. +""" +from __future__ import annotations + +import argparse +from dataclasses import asdict, dataclass +import csv +import json +import os +from pathlib import Path +import platform +import time +import warnings + +import numpy as np +import sklearn +from joblib import Parallel, delayed, parallel_config +from sklearn.exceptions import ConvergenceWarning +from sklearn.neural_network import MLPRegressor +from sklearn.preprocessing import StandardScaler +from threadpoolctl import threadpool_limits + +DEFAULT_OUTPUT = Path(__file__).resolve().parent / "figures" / "pm4_uniform" +ESTIMATORS = ("nonorthogonal_crossfit", "orthogonal_insample", "orthogonal_crossfit") +COLUMNS = ESTIMATORS + ( + "m_train_mse", "ell_train_mse", "m_heldout_mse", "ell_heldout_mse", + "m_function_mse", "ell_function_mse", "full_m_train_mse", + "full_ell_train_mse", "nonorthogonal_denominator", "orthogonal_denominator", + "full_orthogonal_denominator", "training_iteration_limits", +) + + +@dataclass(frozen=True) +class Config: + n: int = 1000 + p: int = 10 + beta: float = 0.5 + seed: int = 726031 + width: int = 128 + epochs: int = 100 + alpha: float = 0.01 + learning_rate: float = 0.001 + treatment_curvature: float = 0.2 + outcome_curvature: float = 2.0 + + +def generate_data(config, rng): + """Independent Gaussian controls, one weak nonlinear confounding component.""" + x = rng.standard_normal((config.n, config.p)) + q = x[:, 1] ** 2 - 1 + m = x[:, 0] + config.treatment_curvature * q + g = config.outcome_curvature * q + d = m + rng.standard_normal(config.n) + y = config.beta * d + g + rng.standard_normal(config.n) + return x, d, y, m, config.beta * m + g + + +def fit_nuisances(x_train, d_train, y_train, config, seed): + """Two separate scalar-output fits of exactly the same learning algorithm. + + Inputs are standardized using this training sample only. Targets are + centered on training means, but not rescaled: both networks minimize squared + error in the variables' original units with the same L2 penalty. The shared + random_state makes initialization and minibatch order reproducible and equal. + No coefficients, weights, or predictions are passed between the networks. + """ + scaler = StandardScaler().fit(x_train) + scaled = scaler.transform(x_train) + models, means = [], [] + limits = 0 + for target in (d_train, y_train): + model = MLPRegressor( + hidden_layer_sizes=(config.width, config.width), activation="relu", + solver="adam", alpha=config.alpha, batch_size=min(128, len(x_train)), + learning_rate_init=config.learning_rate, max_iter=config.epochs, + early_stopping=False, tol=0, n_iter_no_change=config.epochs + 1, + random_state=seed, + ) + mean = float(target.mean()) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always", ConvergenceWarning) + model.fit(scaled, target - mean) + limits += sum(issubclass(w.category, ConvergenceWarning) for w in caught) + models.append(model) + means.append(mean) + + def predict(x): + z = scaler.transform(x) + return tuple(model.predict(z) + mean for model, mean in zip(models, means)) + + return predict, limits + + +def score_estimates(y, d, m_hat, ell_hat): + """Valid nonorthogonal IV score and orthogonal partialling-out score. + + At the population m0, V = D-m0(X) has conditional mean zero and is a valid + instrument for D in Y = beta D + g0(X) + epsilon. Thus E[V(Y-beta D)]=0. + Its derivative with respect to m in direction h is -E[h(X)g0(X)], generally + nonzero. Partialling out Y as well gives the Neyman-orthogonal score. + """ + v = d - m_hat + nonorth_denom = float(v @ d) + orth_denom = float(v @ v) + if abs(nonorth_denom) <= np.finfo(float).eps or orth_denom <= np.finfo(float).eps: + raise ValueError("Numerically singular score denominator; no repetition was discarded.") + return ( + float(v @ y / nonorth_denom), + float(v @ (y - ell_hat) / orth_denom), + nonorth_denom / len(d), orth_denom / len(d), + ) + + +def mse(a, b): + return float(np.mean((a - b) ** 2)) + + +def one_replication(rep, config): + # Separate deterministic streams for data and model fitting; independent of + # worker scheduling. This reproduces the pilot stream layout on a fresh seed. + seeds = np.random.SeedSequence([config.seed, rep]).spawn(10) + rng = np.random.default_rng(seeds[0]) + model_seeds = [int(s.generate_state(1)[0]) for s in seeds[1:]] + with threadpool_limits(limits=1): + x, d, y, m0, ell0 = generate_data(config, rng) + folds = np.array_split(rng.permutation(config.n), 2) + m_hat, ell_hat = np.empty(config.n), np.empty(config.n) + training_mse = np.zeros(2) + limits = 0 + for k in range(2): + test, train = folds[k], folds[1 - k] + predict, count = fit_nuisances(x[train], d[train], y[train], config, model_seeds[2 * k]) + m_hat[test], ell_hat[test] = predict(x[test]) + mt, lt = predict(x[train]) + training_mse += 0.5 * np.array([mse(d[train], mt), mse(y[train], lt)]) + limits += count + + naive, cf, naive_denom, cf_denom = score_estimates(y, d, m_hat, ell_hat) + predict, count = fit_nuisances(x, d, y, config, model_seeds[4]) + full_m, full_l = predict(x) + full_v = d - full_m + full_denom = float(full_v @ full_v) + if full_denom <= np.finfo(float).eps: + raise ValueError("Numerically zero training residual variation; no repetition was discarded.") + full = float(full_v @ (y - full_l) / full_denom) + row = np.array([ + naive, full, cf, *training_mse, mse(d, m_hat), mse(y, ell_hat), + mse(m_hat, m0), mse(ell_hat, ell0), mse(d, full_m), mse(y, full_l), + naive_denom, cf_denom, full_denom / config.n, limits + count, + ]) + if row.shape != (len(COLUMNS),) or not np.isfinite(row).all(): + raise ValueError(f"Invalid result in repetition {rep}; no repetition was discarded.") + return rep, row + + +def metadata(config): + return { + "simulation_version": 1, "config": asdict(config), "columns": list(COLUMNS), + "python_version": platform.python_version(), "numpy_version": np.__version__, + "sklearn_version": sklearn.__version__, + } + + +def save_cache(path, meta, rows): + temporary = path.with_suffix(".tmp.npz") + np.savez_compressed(temporary, metadata=json.dumps(meta, sort_keys=True), results=rows) + for attempt in range(10): + try: + os.replace(temporary, path) + return + except PermissionError: + if attempt == 9: + raise + time.sleep(min(0.2 * 2 ** attempt, 2.0)) + + +def simulate(config, reps, jobs, directory, plot_only=False): + directory.mkdir(parents=True, exist_ok=True) + path = directory / "simulation.npz" + meta, saved_meta = metadata(config), None + rows = np.full((reps, len(COLUMNS)), np.nan) + if path.exists(): + with np.load(path, allow_pickle=False) as saved: + saved_meta = json.loads(str(saved["metadata"])) + if any(saved_meta[k] != meta[k] for k in ("simulation_version", "config", "columns")): + raise ValueError("Different cached configuration; choose a new --output-dir.") + previous = saved["results"] + if len(previous) > reps: + raise ValueError(f"Cache has {len(previous)} repetitions; increase --reps.") + rows[:len(previous)] = previous + pending = np.flatnonzero(~np.isfinite(rows).all(axis=1)) + if saved_meta is not None: + if len(pending) and meta != saved_meta: + raise ValueError("Software versions differ; use a new --output-dir to simulate.") + meta = saved_meta + if plot_only and len(pending): + raise ValueError("The simulation is incomplete; omit --plot-only to compute missing repetitions.") + if len(pending): + done, start = reps - len(pending), time.monotonic() + print(f"Running {len(pending)} repetitions ({done} cached).", flush=True) + try: + with parallel_config(backend="loky", inner_max_num_threads=1), Parallel( + n_jobs=jobs, return_as="generator_unordered" + ) as pool: + for rep, row in pool(delayed(one_replication)(int(r), config) for r in pending): + rows[rep] = row + done += 1 + if done % 25 == 0 or done == reps: + save_cache(path, meta, rows) + print(f" {done}/{reps} complete ({time.monotonic()-start:.0f}s)", flush=True) + finally: + save_cache(path, meta, rows) + return rows, meta + + +def write_results(rows, meta, directory): + with (directory / "simulation.csv").open("w", newline="", encoding="utf-8") as f: + writer = csv.writer(f) + writer.writerow(("replication",) + COLUMNS) + writer.writerows((r, *row) for r, row in enumerate(rows)) + summary = { + "metadata": meta, "repetitions": len(rows), + "estimators": { + name: {"mean": float(rows[:, k].mean()), + "bias": float(rows[:, k].mean() - meta["config"]["beta"]), + "monte_carlo_sd": float(rows[:, k].std(ddof=1))} + for k, name in enumerate(ESTIMATORS) + }, + "mean_diagnostics": {name: float(rows[:, k].mean()) for k, name in enumerate(COLUMNS) if k >= 3}, + } + (directory / "summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8") + return summary + + +def plot_figures(rows, config, directory, figures=(1, 2), bins=40): + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + from matplotlib.ticker import MaxNLocator + error = rows[:, :3] - config.beta + lo, hi = min(float(error.min()), 0.), max(float(error.max()), 0.) + pad = max(.035 * (hi - lo), .01) + edges = np.linspace(lo - pad, hi + pad, bins + 1) + ymax = 1.12 * max(np.histogram(error[:, j], edges, density=True)[0].max() for j in range(3)) + plot_cases = { + 1: (0, "Nonorthogonal, cross-fitted", "SimOrthogonality"), + 2: (1, "Orthogonal, without cross-fitting", "SimCrossFitting"), + } + style = {"font.family": "sans-serif", "font.size": 12, "axes.labelsize": 14, + "axes.spines.top": False, "axes.spines.right": False, "axes.linewidth": .9, + "xtick.labelsize": 11, "ytick.labelsize": 11, "pdf.fonttype": 42, + "savefig.facecolor": "white", "figure.facecolor": "white"} + with plt.rc_context(style): + for number in figures: + j, label, filename = plot_cases[number] + fig, ax = plt.subplots(figsize=(8., 4.6)) + fig.subplots_adjust(left=.10, right=.985, bottom=.17, top=.79) + for col, color, name in ((j, "#E69F69", label), (2, "#6BAED0", "Orthogonal, cross-fitted")): + ax.hist(error[:, col], bins=edges, density=True, alpha=.65, color=color, + edgecolor="white", linewidth=.4, label=name) + ax.axvline(0, color="#4A4A4A", linestyle=(0, (5, 4)), linewidth=1.25, zorder=3) + ax.set(xlim=(edges[0], edges[-1]), ylim=(0, ymax), ylabel="Density", + xlabel=r"Estimation error, $\widehat{\beta}-\beta$") + ax.xaxis.set_major_locator(MaxNLocator(nbins=7)) + ax.yaxis.set_major_locator(MaxNLocator(nbins=5)) + ax.tick_params(direction="out", length=4, width=.9) + ax.legend(loc="lower center", bbox_to_anchor=(.5, 1.025), frameon=False, + fontsize=11, handlelength=1.6, labelspacing=.5) + for suffix in ("pdf", "png"): + fig.savefig(directory / f"{filename}.{suffix}", dpi=220) + plt.close(fig) + (directory / "plot_settings.json").write_text(json.dumps({ + "quantity": "estimate minus true beta; no studentization", + "bin_edges": edges.tolist(), "y_limits": [0, float(ymax)], + "all_repetitions_included": True, + }, indent=2) + "\n", encoding="utf-8") + + +def main(figures=(1, 2)): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--reps", type=int, default=500) + parser.add_argument("--jobs", type=int, default=min(8, os.cpu_count() or 1)) + for name in ("n", "p", "seed", "width", "epochs"): + parser.add_argument(f"--{name}", type=int, default=getattr(Config(), name)) + for name in ("alpha", "learning_rate", "treatment_curvature", "outcome_curvature"): + parser.add_argument(f"--{name.replace('_', '-')}", type=float, default=getattr(Config(), name)) + parser.add_argument("--bins", type=int, default=40) + parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT) + parser.add_argument("--plot-only", action="store_true") + args = parser.parse_args() + if min(args.reps, args.n, args.p) < 2 or min(args.width, args.epochs, args.bins) < 1 or args.jobs == 0: + parser.error("Invalid sample, model, bin, or process count.") + if args.seed < 0 or args.alpha < 0 or args.learning_rate <= 0 or not all(np.isfinite(getattr(args, k)) for k in ( + "alpha", "learning_rate", "treatment_curvature", "outcome_curvature" + )): + parser.error("Invalid seed, regularization, learning rate, or DGP coefficient.") + config = Config(**{k: getattr(args, k) for k in asdict(Config()) if k != "beta"}) + rows, meta = simulate(config, args.reps, args.jobs, args.output_dir, args.plot_only) + summary = write_results(rows, meta, args.output_dir) + plot_figures(rows, config, args.output_dir, figures, args.bins) + for name, stats in summary["estimators"].items(): + print(f"{name}: mean={stats['mean']:.4f}, bias={stats['bias']:+.4f}") + print(f"Saved figures and results to {args.output_dir.resolve()}") + + +if __name__ == "__main__": + main() From 634e78f09c8a10632ea2677a7044f2b5229fc2f6 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Mon, 28 Sep 2026 10:52:21 +0200 Subject: [PATCH 15/32] Remove unused imports --- CM2/python-colliderbias-hollywood.ipynb | 2 -- 1 file changed, 2 deletions(-) diff --git a/CM2/python-colliderbias-hollywood.ipynb b/CM2/python-colliderbias-hollywood.ipynb index 0daa3703..8e2fa6e6 100644 --- a/CM2/python-colliderbias-hollywood.ipynb +++ b/CM2/python-colliderbias-hollywood.ipynb @@ -51,8 +51,6 @@ "import statsmodels.formula.api as smf\n", "import networkx as nx\n", "from pgmpy.base import DAG\n", - "from pgmpy.models import DiscreteBayesianNetwork as BayesianNetwork\n", - "from pgmpy.inference import CausalInference\n", "import pylab as plt" ] }, From 91827b37847daaf61ae53b3170682f382914e618 Mon Sep 17 00:00:00 2001 From: chansen776 Date: Tue, 29 Sep 2026 17:35:57 -0500 Subject: [PATCH 16/32] Update AC-1 notebooks Align birthweight examples to same sample exclusion and specification Fix broken hdmpy in python sensitivity analysis notebook. --- AC1/python-proxy-controls.ipynb | 4 +++- ...n-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb | 3 ++- AC1/r-proxy-controls.irnb | 3 +++ 3 files changed, 8 insertions(+), 2 deletions(-) diff --git a/AC1/python-proxy-controls.ipynb b/AC1/python-proxy-controls.ipynb index 2342af48..d8eb4034 100644 --- a/AC1/python-proxy-controls.ipynb +++ b/AC1/python-proxy-controls.ipynb @@ -611,8 +611,10 @@ "data = data[data.wt != 999]\n", "data = data[data.parity != 99]\n", "data = data[data.parity != 9]\n", + "data = data[data.race != 99]\n", "data = data[np.logical_and(data.number != 98, data.number != 99)]\n", "data = data[np.logical_and(data.inc != 98, data.inc != 99)]\n", + "data = data[data.age != 99]\n", "data.shape" ] }, @@ -624,7 +626,7 @@ }, "outputs": [], "source": [ - "X = np.array(patsy.dmatrix('0 + C(race) + age + C(sex)', data))\n", + "X = np.array(patsy.dmatrix('0 + C(race) + age', data))\n", "D = np.array(patsy.dmatrix('0 + number', data))\n", "Q = np.array(patsy.dmatrix('0 + C(ed)', data))\n", "S = np.array(patsy.dmatrix('0 + parity', data))\n", diff --git a/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb b/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb index a7fe59d9..87debb25 100644 --- a/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb +++ b/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb @@ -318,9 +318,10 @@ }, "outputs": [], "source": [ + "import sys\n", + "sys.path.insert(1, \"./hdmpy\")\n", "import hdmpy\n", "\n", - "\n", "class RLasso(BaseEstimator):\n", "\n", " def __init__(self, *, post=True):\n", diff --git a/AC1/r-proxy-controls.irnb b/AC1/r-proxy-controls.irnb index ab423a7f..d9caac7d 100644 --- a/AC1/r-proxy-controls.irnb +++ b/AC1/r-proxy-controls.irnb @@ -328,6 +328,9 @@ "source": [ "# Filter data to exclude entries where income, number of cigarettes smoked,\n", "# race, age are not asked or not known\n", + "data <- data[data$wt != 999, ]\n", + "data <- data[data$parity != 99, ]\n", + "data <- data[data$parity != 9, ]\n", "data <- data[data$race != 99, ]\n", "data <- data[!(data$number %in% c(98, 99)), ]\n", "data <- data[!(data$inc %in% c(98, 99)), ]\n", From abe22d11ed7fe820f043e619341b9114331595a7 Mon Sep 17 00:00:00 2001 From: chansen776 Date: Wed, 30 Sep 2026 12:25:10 -0500 Subject: [PATCH 17/32] Update python-debiased-ml-for-partially-linear-iv-model.ipynb Fix hdmpy import for current google colab --- AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb | 3 +++ 1 file changed, 3 insertions(+) diff --git a/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb b/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb index d1383f4f..867ce3c0 100644 --- a/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb +++ b/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb @@ -327,6 +327,9 @@ }, "outputs": [], "source": [ + "import sys\n", + "sys.path.insert(1, './hdmpy')\n", + "\n", "import hdmpy\n", "\n", "\n", From ed60dc23833ada1032baae6d66f86a530c2e098c Mon Sep 17 00:00:00 2001 From: chansen776 Date: Wed, 30 Sep 2026 13:25:48 -0500 Subject: [PATCH 18/32] AC-2 AJR notebooks Updates to AJR notebooks in AC-2 to run on google colab --- ...sed-ml-for-partially-linear-iv-model.ipynb | 1208 ++++++++--------- ...ased-ml-for-partially-linear-iv-model.irnb | 1029 +++++++------- 2 files changed, 1119 insertions(+), 1118 deletions(-) diff --git a/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb b/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb index 867ce3c0..dc80026b 100644 --- a/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb +++ b/AC2/python-debiased-ml-for-partially-linear-iv-model.ipynb @@ -1,616 +1,616 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "qjaFmAyEzcLz", - "papermill": { - "duration": 0.008106, - "end_time": "2021-04-23T10:41:33.911944", - "exception": false, - "start_time": "2021-04-23T10:41:33.903838", - "status": "completed" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "qjaFmAyEzcLz", + "papermill": { + "duration": 0.008106, + "end_time": "2021-04-23T10:41:33.911944", + "exception": false, + "start_time": "2021-04-23T10:41:33.903838", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Double/Debiased ML for Partially Linear IV Model\n", + "\n", + "This is a simple implementation of Debiased Machine Learning for the Partially Linear\n", + "IV Regression Model, which provides an application of DML IV inference.\n", + "\n", + "\n", + "Reference:\n", + "\n", + "- https://arxiv.org/abs/1608.00060\n", + "- https://www.amazon.com/Business-Data-Science-Combining-Accelerate/dp/1260452778\n", + "\n", + "The code is based on the book.\n" + ] }, - "tags": [] - }, - "source": [ - "# Double/Debiased ML for Partially Linear IV Model\n", - "\n", - "This is a simple implementation of Debiased Machine Learning for the Partially Linear\n", - "IV Regression Model, which provides an application of DML IV inference.\n", - "\n", - "\n", - "Reference:\n", - "\n", - "- https://arxiv.org/abs/1608.00060\n", - "- https://www.amazon.com/Business-Data-Science-Combining-Accelerate/dp/1260452778\n", - "\n", - "The code is based on the book.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G3YNyr-ezcL4", - "papermill": { - "duration": 0.006963, - "end_time": "2021-04-23T10:41:33.926085", - "exception": false, - "start_time": "2021-04-23T10:41:33.919122", - "status": "completed" + { + "cell_type": "markdown", + "metadata": { + "id": "G3YNyr-ezcL4", + "papermill": { + "duration": 0.006963, + "end_time": "2021-04-23T10:41:33.926085", + "exception": false, + "start_time": "2021-04-23T10:41:33.919122", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "\n", + "# Partially Linear IV Model\n", + "\n", + "We consider the partially linear structural equation model:\n", + "\\begin{eqnarray}\n", + " & Y - D\\theta_0 = g_0(X) + \\zeta, & E[\\zeta \\mid Z,X]= 0,\\\\\n", + " & Z = m_0(X) + V, & E[V \\mid X] = 0.\n", + "\\end{eqnarray}\n", + "\n", + "\n", + "Note that this model is not a regression model unless $Z=D$. The model is a canonical\n", + "model in causal inference, going back to P. Wright's work on IV methods for estimating demand/supply equations, with the modern difference being that $g_0$ and $m_0$ are nonlinear, potentially complicated functions of high-dimensional $X$. \n", + "\n", + "\n", + "The idea of this model is that there is a structural or causal relation between $Y$ and $D$, captured by $\\theta_0$, and $g_0(X) + \\zeta$ is the stochastic error, partly explained by covariates $X$. $V$ and $\\zeta$ are stochastic errors that are not explained by $X$. Since $Y$ and $D$ are jointly determined, we need an external factor, commonly referred to as an instrument, $Z$ to create exogenous variation\n", + "in $D$. Note that $Z$ should affect $D$. The $X$ here serve again as confounding factors, so we can think of variation in $Z$ as being exogenous only conditional on $X$.\n", + "\n", + "\n", + "The causal DAG this model corresponds to is given by:\n", + "$$\n", + "Z \\to D, X \\to (Y, Z, D), L \\to (Y,D),\n", + "$$\n", + "where $L$ is the latent confounder affecting both $Y$ and $D$, but not $Z$.\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "# Example\n", + "\n", + "A simple contextual example is from biostatistics, where $Y$ is a health outcome and $D$ is indicator of smoking. Thus, $\\theta_0$ is captures the effect of smoking on health. Health outcome $Y$ and smoking behavior $D$ are treated as being jointly determined. $X$ represents patient characteristics, and $Z$ could be a doctor's advice not to smoke (or another behavioral treatment) that may affect the outcome $Y$ only through shifting the behavior $D$, conditional on characteristics $X$. \n", + "\n", + "----\n", + "\n", + "\n", + "\n", + "# PLIVM in Residualized Form\n", + "\n", + "The PLIV model above can be rewritten in the following residualized form:\n", + "$$\n", + " \\tilde Y = \\tilde D \\theta_0 + \\zeta, \\quad E[\\zeta \\mid V,X]= 0,\n", + "$$\n", + "where\n", + "$$\n", + " \\tilde Y = (Y- \\ell_0(X)), \\quad \\ell_0(X) = E[Y \\mid X] \\\\\n", + " \\tilde D = (D - r_0(X)), \\quad r_0(X) = E[D \\mid X] \\\\\n", + " \\tilde Z = (Z- m_0(X)), \\quad m_0(X) = E[Z \\mid X].\n", + "$$\n", + " The \"tilde\" variables (e.g. $\\tilde Y$) above represent original variables after taking out or \"partialling out\"\n", + " the effect of $X$. Note that $\\theta_0$ is identified from this equation if $V$\n", + " and $U$ have non-zero correlation, which automatically means that $U$ and $V$\n", + " must have non-zero variation.\n", + "\n", + " \n", + "\n", + "-----\n", + "\n", + "# DML for PLIV Model\n", + "\n", + "Given identification, DML proceeds as follows\n", + "\n", + "Compute the estimates $\\hat \\ell_0$, $\\hat r_0$, and $\\hat m_0$ , which amounts\n", + "to solving the three problems of predicting $Y$, $D$, and $Z$ using\n", + "$X$, using any generic ML method, giving us estimated residuals\n", + "$$\n", + "\\tilde Y = Y - \\hat \\ell_0(X), \\\\ \\tilde D= D - \\hat r_0(X), \\\\ \\tilde Z = Z- \\hat m_0(X).\n", + "$$\n", + "The estimates should be of a cross-validated form, as detailed in the algorithm below.\n", + "\n", + "Estimate $\\theta_0$ by the the intstrumental\n", + "variable regression of $\\tilde Y$ on $\\tilde D$ using $\\tilde Z$ as an instrument.\n", + "Use the conventional inference for the IV regression estimator, ignoring\n", + "the estimation error in these residuals.\n", + "\n", + "The reason we work with this residualized form is that it eliminates the bias\n", + "arising when solving the prediction problem in stage 1. The role of cross-validation\n", + "is to avoid another source of bias due to potential overfitting.\n", + "\n", + "The estimator is adaptive,\n", + "in the sense that the first stage estimation errors do not affect the second\n", + "stage errors.\n" + ] }, - "tags": [] - }, - "source": [ - "\n", - "# Partially Linear IV Model\n", - "\n", - "We consider the partially linear structural equation model:\n", - "\\begin{eqnarray}\n", - " & Y - D\\theta_0 = g_0(X) + \\zeta, & E[\\zeta \\mid Z,X]= 0,\\\\\n", - " & Z = m_0(X) + V, & E[V \\mid X] = 0.\n", - "\\end{eqnarray}\n", - "\n", - "\n", - "Note that this model is not a regression model unless $Z=D$. The model is a canonical\n", - "model in causal inference, going back to P. Wright's work on IV methods for estimating demand/supply equations, with the modern difference being that $g_0$ and $m_0$ are nonlinear, potentially complicated functions of high-dimensional $X$. \n", - "\n", - "\n", - "The idea of this model is that there is a structural or causal relation between $Y$ and $D$, captured by $\\theta_0$, and $g_0(X) + \\zeta$ is the stochastic error, partly explained by covariates $X$. $V$ and $\\zeta$ are stochastic errors that are not explained by $X$. Since $Y$ and $D$ are jointly determined, we need an external factor, commonly referred to as an instrument, $Z$ to create exogenous variation\n", - "in $D$. Note that $Z$ should affect $D$. The $X$ here serve again as confounding factors, so we can think of variation in $Z$ as being exogenous only conditional on $X$.\n", - "\n", - "\n", - "The causal DAG this model corresponds to is given by:\n", - "$$\n", - "Z \\to D, X \\to (Y, Z, D), L \\to (Y,D),\n", - "$$\n", - "where $L$ is the latent confounder affecting both $Y$ and $D$, but not $Z$.\n", - "\n", - "\n", - "\n", - "---\n", - "\n", - "# Example\n", - "\n", - "A simple contextual example is from biostatistics, where $Y$ is a health outcome and $D$ is indicator of smoking. Thus, $\\theta_0$ is captures the effect of smoking on health. Health outcome $Y$ and smoking behavior $D$ are treated as being jointly determined. $X$ represents patient characteristics, and $Z$ could be a doctor's advice not to smoke (or another behavioral treatment) that may affect the outcome $Y$ only through shifting the behavior $D$, conditional on characteristics $X$. \n", - "\n", - "----\n", - "\n", - "\n", - "\n", - "# PLIVM in Residualized Form\n", - "\n", - "The PLIV model above can be rewritten in the following residualized form:\n", - "$$\n", - " \\tilde Y = \\tilde D \\theta_0 + \\zeta, \\quad E[\\zeta \\mid V,X]= 0,\n", - "$$\n", - "where\n", - "$$\n", - " \\tilde Y = (Y- \\ell_0(X)), \\quad \\ell_0(X) = E[Y \\mid X] \\\\\n", - " \\tilde D = (D - r_0(X)), \\quad r_0(X) = E[D \\mid X] \\\\\n", - " \\tilde Z = (Z- m_0(X)), \\quad m_0(X) = E[Z \\mid X].\n", - "$$\n", - " The \"tilde\" variables (e.g. $\\tilde Y$) above represent original variables after taking out or \"partialling out\"\n", - " the effect of $X$. Note that $\\theta_0$ is identified from this equation if $V$\n", - " and $U$ have non-zero correlation, which automatically means that $U$ and $V$\n", - " must have non-zero variation.\n", - "\n", - " \n", - "\n", - "-----\n", - "\n", - "# DML for PLIV Model\n", - "\n", - "Given identification, DML proceeds as follows\n", - "\n", - "Compute the estimates $\\hat \\ell_0$, $\\hat r_0$, and $\\hat m_0$ , which amounts\n", - "to solving the three problems of predicting $Y$, $D$, and $Z$ using\n", - "$X$, using any generic ML method, giving us estimated residuals\n", - "$$\n", - "\\tilde Y = Y - \\hat \\ell_0(X), \\\\ \\tilde D= D - \\hat r_0(X), \\\\ \\tilde Z = Z- \\hat m_0(X).\n", - "$$\n", - "The estimates should be of a cross-validated form, as detailed in the algorithm below.\n", - "\n", - "Estimate $\\theta_0$ by the the intstrumental\n", - "variable regression of $\\tilde Y$ on $\\tilde D$ using $\\tilde Z$ as an instrument.\n", - "Use the conventional inference for the IV regression estimator, ignoring\n", - "the estimation error in these residuals.\n", - "\n", - "The reason we work with this residualized form is that it eliminates the bias\n", - "arising when solving the prediction problem in stage 1. The role of cross-validation\n", - "is to avoid another source of bias due to potential overfitting.\n", - "\n", - "The estimator is adaptive,\n", - "in the sense that the first stage estimation errors do not affect the second\n", - "stage errors.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "_kg_hide-output": true, - "id": "yGW6JhG5zcL5", - "papermill": { - "duration": 34.670095, - "end_time": "2021-04-23T10:42:08.603197", - "exception": false, - "start_time": "2021-04-23T10:41:33.933102", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_kg_hide-output": true, + "id": "yGW6JhG5zcL5", + "papermill": { + "duration": 34.670095, + "end_time": "2021-04-23T10:42:08.603197", + "exception": false, + "start_time": "2021-04-23T10:41:33.933102", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Import relevant packages\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import cross_val_predict, KFold\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "import patsy\n", + "import warnings\n", + "from sklearn.base import BaseEstimator\n", + "import statsmodels.api as sm\n", + "from statsmodels.tools import add_constant\n", + "warnings.simplefilter('ignore')\n", + "np.random.seed(1234)" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Import relevant packages\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import cross_val_predict, KFold\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "import patsy\n", - "import warnings\n", - "from sklearn.base import BaseEstimator\n", - "import statsmodels.api as sm\n", - "from statsmodels.tools import add_constant\n", - "warnings.simplefilter('ignore')\n", - "np.random.seed(1234)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "j2WVUbBDzcL-", - "papermill": { - "duration": 1.846109, - "end_time": "2021-04-23T10:42:10.458406", - "exception": false, - "start_time": "2021-04-23T10:42:08.612297", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j2WVUbBDzcL-", + "papermill": { + "duration": 1.846109, + "end_time": "2021-04-23T10:42:10.458406", + "exception": false, + "start_time": "2021-04-23T10:42:08.612297", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def dml(X, Z, D, y, modely, modeld, modelz, *, nfolds, classifier=False):\n", + " '''\n", + " DML for the Partially Linear Model setting with cross-fitting\n", + "\n", + " Input\n", + " -----\n", + " X: the controls\n", + " Z: the instrument\n", + " D: the treatment\n", + " y: the outcome\n", + " modely: the ML model for predicting the outcome y\n", + " modeld: the ML model for predicting the treatment D\n", + " modelz: the ML model for predicting the instrument Z\n", + " nfolds: the number of folds in cross-fitting\n", + " classifier: bool, whether the modeld is a classifier or a regressor\n", + "\n", + " Output\n", + " ------\n", + " point: the point estimate of the treatment effect of D on y\n", + " stderr: the standard error of the treatment effect\n", + " yhat: the cross-fitted predictions for the outcome y\n", + " Dhat: the cross-fitted predictions for the treatment D\n", + " Zhat: the cross-fitted predictions for the instrument Z\n", + " resy: the outcome residuals\n", + " resD: the treatment residuals\n", + " resZ: the instrument residuals\n", + " epsilon: the final residual-on-residual OLS regression residual\n", + " '''\n", + " cv = KFold(n_splits=nfolds, shuffle=True, random_state=123) # shuffled k-folds\n", + " yhat = cross_val_predict(modely, X, y, cv=cv, n_jobs=-1) # out-of-fold predictions for y\n", + " # out-of-fold predictions for D\n", + " # use predict or predict_proba dependent on classifier or regressor for D\n", + " if classifier:\n", + " Dhat = cross_val_predict(modeld, X, D, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", + " Zhat = cross_val_predict(modelz, X, Z, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", + " else:\n", + " Dhat = cross_val_predict(modeld, X, D, cv=cv, n_jobs=-1)\n", + " Zhat = cross_val_predict(modelz, X, Z, cv=cv, n_jobs=-1)\n", + " # calculate outcome and treatment residuals\n", + " resy = y - yhat\n", + " resD = D - Dhat\n", + " resZ = Z - Zhat\n", + " # rename for downstream tasks\n", + " resy = resy.rename(\"resy\")\n", + " resD = resD.rename(\"resD\")\n", + " resZ = resZ.rename(\"resZ\")\n", + "\n", + " # final stage ols based point estimate and standard error\n", + " point = np.mean(resy * resZ) / np.mean(resD * resZ)\n", + " epsilon = resy - point * resD\n", + " var = np.mean(epsilon**2 * resZ**2) / np.mean(resD * resZ)**2\n", + " stderr = np.sqrt(var / X.shape[0])\n", + " return point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "def dml(X, Z, D, y, modely, modeld, modelz, *, nfolds, classifier=False):\n", - " '''\n", - " DML for the Partially Linear Model setting with cross-fitting\n", - "\n", - " Input\n", - " -----\n", - " X: the controls\n", - " Z: the instrument\n", - " D: the treatment\n", - " y: the outcome\n", - " modely: the ML model for predicting the outcome y\n", - " modeld: the ML model for predicting the treatment D\n", - " modelz: the ML model for predicting the instrument Z\n", - " nfolds: the number of folds in cross-fitting\n", - " classifier: bool, whether the modeld is a classifier or a regressor\n", - "\n", - " Output\n", - " ------\n", - " point: the point estimate of the treatment effect of D on y\n", - " stderr: the standard error of the treatment effect\n", - " yhat: the cross-fitted predictions for the outcome y\n", - " Dhat: the cross-fitted predictions for the treatment D\n", - " Zhat: the cross-fitted predictions for the instrument Z\n", - " resy: the outcome residuals\n", - " resD: the treatment residuals\n", - " resZ: the instrument residuals\n", - " epsilon: the final residual-on-residual OLS regression residual\n", - " '''\n", - " cv = KFold(n_splits=nfolds, shuffle=True, random_state=123) # shuffled k-folds\n", - " yhat = cross_val_predict(modely, X, y, cv=cv, n_jobs=-1) # out-of-fold predictions for y\n", - " # out-of-fold predictions for D\n", - " # use predict or predict_proba dependent on classifier or regressor for D\n", - " if classifier:\n", - " Dhat = cross_val_predict(modeld, X, D, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", - " Zhat = cross_val_predict(modelz, X, Z, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", - " else:\n", - " Dhat = cross_val_predict(modeld, X, D, cv=cv, n_jobs=-1)\n", - " Zhat = cross_val_predict(modelz, X, Z, cv=cv, n_jobs=-1)\n", - " # calculate outcome and treatment residuals\n", - " resy = y - yhat\n", - " resD = D - Dhat\n", - " resZ = Z - Zhat\n", - " # rename for downstream tasks\n", - " resy = resy.rename(\"resy\")\n", - " resD = resD.rename(\"resD\")\n", - " resZ = resZ.rename(\"resZ\")\n", - "\n", - " # final stage ols based point estimate and standard error\n", - " point = np.mean(resy * resZ) / np.mean(resD * resZ)\n", - " epsilon = resy - point * resD\n", - " var = np.mean(epsilon**2 * resZ**2) / np.mean(resD * resZ)**2\n", - " stderr = np.sqrt(var / X.shape[0])\n", - " return point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "F6U6fnzne-77" - }, - "outputs": [], - "source": [ - "def summary(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon, X, D, Z, y, *, name):\n", - " '''\n", - " Convenience summary function that takes the results of the DML IV function\n", - " and summarizes several estimation quantities and performance metrics.\n", - " '''\n", - " return pd.DataFrame({'estimate': point, # point estimate\n", - " 'stderr': stderr, # standard error\n", - " 'lower': point - 1.96 * stderr, # lower end of 95% confidence interval\n", - " 'upper': point + 1.96 * stderr, # upper end of 95% confidence interval\n", - " 'rmse y': np.sqrt(np.mean(resy**2)), # RMSE of model that predicts outcome y\n", - " 'rmse Z': np.sqrt(np.mean(resZ**2)), # RMSE of model that predicts instrument Z\n", - " 'rmse D': np.sqrt(np.mean(resD**2)) # RMSE of model that predicts treatment D\n", - " }, index=[name])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "x1g3XjsIzcL_", - "papermill": { - "duration": 0.011698, - "end_time": "2021-04-23T10:42:10.482689", - "exception": false, - "start_time": "2021-04-23T10:42:10.470991", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F6U6fnzne-77" + }, + "outputs": [], + "source": [ + "def summary(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon, X, D, Z, y, *, name):\n", + " '''\n", + " Convenience summary function that takes the results of the DML IV function\n", + " and summarizes several estimation quantities and performance metrics.\n", + " '''\n", + " return pd.DataFrame({'estimate': point, # point estimate\n", + " 'stderr': stderr, # standard error\n", + " 'lower': point - 1.96 * stderr, # lower end of 95% confidence interval\n", + " 'upper': point + 1.96 * stderr, # upper end of 95% confidence interval\n", + " 'rmse y': np.sqrt(np.mean(resy**2)), # RMSE of model that predicts outcome y\n", + " 'rmse Z': np.sqrt(np.mean(resZ**2)), # RMSE of model that predicts instrument Z\n", + " 'rmse D': np.sqrt(np.mean(resD**2)) # RMSE of model that predicts treatment D\n", + " }, index=[name])" + ] }, - "tags": [] - }, - "source": [ - "-----\n", - "\n", - "\n", - "# Emprical Example: Acemoglu, Johnson, Robinson (AER).\n", - "\n", - "* Y is log GDP;\n", - "* D is a measure of Protection from Expropriation, a proxy for\n", - "quality of insitutions;\n", - "* Z is the log of Settler's mortality;\n", - "* W are geographical variables (latitude, latitude squared, continent dummies as well as interactions)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0Pc6OCp24rji" - }, - "outputs": [], - "source": [ - "file = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/AJR.csv\"\n", - "data = pd.read_csv(file)\n", - "data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3rGw2hAqSx2Y" - }, - "source": [ - "Run the following command to install hdmpy for rigorous lasso:\n", - "\n", - "``` !pip install multiprocess ```\n", - "\n", - "\n", - "```!git clone https://github.com/maxhuppertz/hdmpy.git ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "O5Rknnz2Rh9O" - }, - "outputs": [], - "source": [ - "!pip install multiprocess\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Kb9N0c5uS47n" - }, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.insert(1, './hdmpy')\n", - "\n", - "import hdmpy\n", - "\n", - "\n", - "class RLasso(BaseEstimator):\n", - "\n", - " def __init__(self, *, post=True):\n", - " self.post = post\n", - "\n", - " def fit(self, X, y):\n", - " self.rlasso_ = hdmpy.rlasso(X, y, post=self.post)\n", - " return self\n", - "\n", - " def predict(self, X):\n", - " return np.array(X) @ np.array(self.rlasso_.est['beta']).flatten() + np.array(self.rlasso_.est['intercept'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nO6a6tJWS-FO" - }, - "outputs": [], - "source": [ - "y = data['GDP']\n", - "d = data['Exprop']\n", - "z = data['logMort']\n", - "\n", - "controls = data.drop(['GDP', 'Exprop', 'logMort'], axis=1)\n", - "x = patsy.dmatrix('-1 + (Latitude + Latitude2 + Africa + Asia + Namer + Samer)**2',\n", - " data=controls, return_type='dataframe')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kpPe250uelE7" - }, - "outputs": [], - "source": [ - "# DML with Post-Lasso:\n", - "modely = make_pipeline(StandardScaler(), RLasso(post=True))\n", - "modeld = make_pipeline(StandardScaler(), RLasso(post=True))\n", - "modelz = make_pipeline(StandardScaler(), RLasso(post=True))\n", - "\n", - "# Run DML model with nfolds folds of cross-fitting\n", - "res_rlasso = dml(x, z, d, y, modely, modeld, modelz, nfolds=5)\n", - "table_rlasso = summary(*res_rlasso, x, d, z, y, name='RLasso')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "pdKuGFpXglp8" - }, - "outputs": [], - "source": [ - "# DML with Random Forests. RFs don't require scaling but we do it for consistency\n", - "modely = make_pipeline(StandardScaler(),\n", - " RandomForestRegressor(n_estimators=100, min_samples_leaf=5, random_state=1234))\n", - "modeld = make_pipeline(StandardScaler(),\n", - " RandomForestRegressor(n_estimators=100, min_samples_leaf=5, random_state=1234))\n", - "modelz = make_pipeline(StandardScaler(),\n", - " RandomForestRegressor(n_estimators=100, min_samples_leaf=5, random_state=1234))\n", - "\n", - "# Run DML model with nfolds folds of cross-fitting (computationally intensive)\n", - "res_RF = dml(x, z, d, y, modely, modeld, modelz, nfolds=5)\n", - "table_RF = summary(*res_RF, x, d, z, y, name='RF')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ijo8p0Moig_F" - }, - "outputs": [], - "source": [ - "results = pd.concat([table_rlasso, table_RF], axis=0)\n", - "results" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r7l6cFi2zcMA", - "papermill": { - "duration": 0.013703, - "end_time": "2021-04-23T10:42:16.269251", - "exception": false, - "start_time": "2021-04-23T10:42:16.255548", - "status": "completed" + { + "cell_type": "markdown", + "metadata": { + "id": "x1g3XjsIzcL_", + "papermill": { + "duration": 0.011698, + "end_time": "2021-04-23T10:42:10.482689", + "exception": false, + "start_time": "2021-04-23T10:42:10.470991", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "-----\n", + "\n", + "\n", + "# Emprical Example: Acemoglu, Johnson, Robinson (AER).\n", + "\n", + "* Y is log GDP;\n", + "* D is a measure of Protection from Expropriation, a proxy for\n", + "quality of insitutions;\n", + "* Z is the log of Settler's mortality;\n", + "* W are geographical variables (latitude, latitude squared, continent dummies as well as interactions)\n", + "\n" + ] }, - "tags": [] - }, - "source": [ - "# Weak Instruments?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "rsUnPDfpzcMB", - "papermill": { - "duration": 16.107321, - "end_time": "2021-04-23T10:42:32.390552", - "exception": false, - "start_time": "2021-04-23T10:42:16.283231", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0Pc6OCp24rji" + }, + "outputs": [], + "source": [ + "file = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/AJR.csv\"\n", + "data = pd.read_csv(file)\n", + "data.shape" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Summary for RLasso\n", - "resy_rlasso, resD_rlasso, resZ_rlasso = res_rlasso[5], res_rlasso[6], res_rlasso[7]\n", - "# Using HC3 robust standard errors\n", - "regDZ_rlasso = sm.OLS(resD_rlasso, add_constant(resZ_rlasso)).fit(cov_type='HC3', use_t=True)\n", - "print(regDZ_rlasso.summary())\n", - "\n", - "# Summary for RF\n", - "resy_RF, resD_RF, resZ_RF = res_RF[5], res_RF[6], res_RF[7]\n", - "# Using HC3 robust standard errors\n", - "regDZ_RF = sm.OLS(resD_RF, add_constant(resZ_RF)).fit(cov_type='HC3', use_t=True)\n", - "print(regDZ_RF.summary())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L3fDDOmfzcMD", - "papermill": { - "duration": 0.015919, - "end_time": "2021-04-23T10:42:32.423865", - "exception": false, - "start_time": "2021-04-23T10:42:32.407946", - "status": "completed" + { + "cell_type": "markdown", + "metadata": { + "id": "3rGw2hAqSx2Y" + }, + "source": [ + "Run the following command to install hdmpy for rigorous lasso:\n", + "\n", + "``` !pip install multiprocess ```\n", + "\n", + "\n", + "```!git clone https://github.com/maxhuppertz/hdmpy.git ```" + ] }, - "tags": [] - }, - "source": [ - "## Anderson-Rubin Idea for Inference with Weak Instruments\n", - "\n", - "As shown above, we may have weak instruments because the t-stat in the regression $\\tilde D \\sim \\tilde Z$ is small relative to standard rules of thumb -- for example Stock and Yogo (2005) suggest accounting for weak instruments if the first stage F-statistic is less than 10 (and more recent work suggests even larger cutoffs).\n", - "\n", - "\n", - " Here, we consider one specific approach (from Anderson and Rubin (1949)) to doing valid inference under weak identification based upon the statistic:\n", - "$$\n", - "C(\\theta) = \\frac{ |E_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z]|^2}{ \\mathbb{V}_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z ]/n}.\n", - "$$\n", - "The empirical variance $\\mathbb{V}_n$ is defined as:\n", - "\\begin{align*}\n", - "\\mathbb{V}_{n}[ g(W_i)] &:= \\mathbb{E}_{n}g(W_i)g(W_i)' - \\mathbb{E}_{n}[ g(W_i)]\\mathbb{E}_{n}[ g(W_i)]'.\n", - "\\end{align*}\n", - "\n", - "If $\\theta_0 = \\theta$, then $C(\\theta) \\overset{a}{\\sim} N(0,1)^2 = \\chi^2(1)$. Therefore, we can reject the hypothesis $\\theta_0 = \\theta$ at level $a$ (for example $a = .05$ for a 5\\% level test) if $C(\\theta)> c(1-a)$ where $c(1-a)$ is the $(1-a)$- quantile of a $\\chi^2(1)$ variable. The probability of falsely rejecting the true hypothesis is approximately $a \\times 100\\%$. \n", - "To construct a $(1-a) \\times 100$\\% confidence region for $\\theta$, we can then simply invert the test by collecting all parameter values that are not rejected at the $a$ level:\n", - "$$\n", - "CR(\\theta) = \\{ \\theta \\in \\Theta: C(\\theta) \\leq c(1-a)\\}.\n", - "$$\n", - "\n", - "\n", - "In more complex settings with many controls or controls that enter with unknown functional form, we can simply replace the residuals $\\tilde Y$, $\\tilde D$, and $\\tilde Z$ by machine\n", - "learned cross-fitted residuals $\\check Y$, $\\check D$, and $ \\check Z$. Thanks to the orthogonality of the IV moment condition underlying the formulation outlined above, we can formally assert that the properties of $C(\\theta)$ and the subsequent testing procedure and confidence region for $\\theta$ continue to hold when using cross-fitted residuals. We will further be able to apply the general procedure to cases where $D$\n", - "is a vector, with a suitable adjustment of the statistic $C(\\theta)$.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PG2tPuFzzcMD", - "papermill": { - "duration": 0.015943, - "end_time": "2021-04-23T10:42:32.455719", - "exception": false, - "start_time": "2021-04-23T10:42:32.439776", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "O5Rknnz2Rh9O" + }, + "outputs": [], + "source": [ + "!pip install multiprocess\n", + "!git clone https://github.com/maxhuppertz/hdmpy.git" + ] }, - "tags": [] - }, - "source": [ - "So let's carry out DML inference combined with Anderson-Rubin Idea" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "6weyaIdJ3pWs" - }, - "outputs": [], - "source": [ - "from scipy.stats import chi2\n", - "\n", - "\n", - "def DML_AR_PLIV(rY, rD, rZ, grid, alpha=0.05):\n", - " n = len(rY)\n", - " Cstat = np.zeros(len(grid))\n", - "\n", - " for i in range(len(grid)):\n", - " term1 = (rY - grid[i] * rD) * rZ\n", - " Cstat[i] = n * (np.mean(term1))**2 / np.var(term1)\n", - "\n", - " Cstat = np.nan_to_num(Cstat) # Replace any NaNs with zeros\n", - "\n", - " lower_bound = min(grid[Cstat < chi2.ppf(1 - alpha, 1)])\n", - " upper_bound = max(grid[Cstat < chi2.ppf(1 - alpha, 1)])\n", - "\n", - " plt.plot(grid, Cstat, linestyle='-', color='black')\n", - " plt.axhline(y=chi2.ppf(1 - alpha, 1), linestyle='--', color='blue')\n", - " plt.axvline(x=lower_bound, linestyle='--', color='red')\n", - " plt.axvline(x=upper_bound, linestyle='--', color='red')\n", - " plt.xlabel('Effect of institutions')\n", - " plt.ylabel('Statistic')\n", - " plt.title('')\n", - " plt.show()\n", - "\n", - " return {'UB': upper_bound, 'LB': lower_bound}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "k9bB2O13zcME", - "papermill": { - "duration": 0.478528, - "end_time": "2021-04-23T10:42:33.002469", - "exception": false, - "start_time": "2021-04-23T10:42:32.523941", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Kb9N0c5uS47n" + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(1, './hdmpy')\n", + "\n", + "import hdmpy\n", + "\n", + "\n", + "class RLasso(BaseEstimator):\n", + "\n", + " def __init__(self, *, post=True):\n", + " self.post = post\n", + "\n", + " def fit(self, X, y):\n", + " self.rlasso_ = hdmpy.rlasso(X, y, post=self.post)\n", + " return self\n", + "\n", + " def predict(self, X):\n", + " return np.array(X) @ np.array(self.rlasso_.est['beta']).flatten() + np.array(self.rlasso_.est['intercept'])" + ] }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Using RLasso\n", - "dml_ar_rlasso = DML_AR_PLIV(rY=resy_rlasso, rD=resD_rlasso, rZ=resZ_rlasso, grid=np.arange(-2, 2, 0.01))\n", - "print(\"RLasso UB and LB: \", dml_ar_rlasso)\n", - "# Using RF\n", - "dml_ar_RF = DML_AR_PLIV(rY=resy_RF, rD=resD_RF, rZ=resZ_RF, grid=np.arange(-2, 2, 0.01))\n", - "print(\"RF UB and LB: \", dml_ar_RF)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nO6a6tJWS-FO" + }, + "outputs": [], + "source": [ + "y = data['GDP']\n", + "d = data['Exprop']\n", + "z = data['logMort']\n", + "\n", + "controls = data.drop(['GDP', 'Exprop', 'logMort'], axis=1)\n", + "x = patsy.dmatrix('-1 + (Latitude + Latitude2 + Africa + Asia + Namer + Samer)**2',\n", + " data=controls, return_type='dataframe')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kpPe250uelE7" + }, + "outputs": [], + "source": [ + "# DML with Post-Lasso:\n", + "modely = make_pipeline(StandardScaler(), RLasso(post=True))\n", + "modeld = make_pipeline(StandardScaler(), RLasso(post=True))\n", + "modelz = make_pipeline(StandardScaler(), RLasso(post=True))\n", + "\n", + "# Run DML model with nfolds folds of cross-fitting\n", + "res_rlasso = dml(x, z, d, y, modely, modeld, modelz, nfolds=5)\n", + "table_rlasso = summary(*res_rlasso, x, d, z, y, name='RLasso')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pdKuGFpXglp8" + }, + "outputs": [], + "source": [ + "# DML with Random Forests. RFs don't require scaling but we do it for consistency\n", + "modely = make_pipeline(StandardScaler(),\n", + " RandomForestRegressor(n_estimators=100, min_samples_leaf=5, random_state=1234))\n", + "modeld = make_pipeline(StandardScaler(),\n", + " RandomForestRegressor(n_estimators=100, min_samples_leaf=5, random_state=1234))\n", + "modelz = make_pipeline(StandardScaler(),\n", + " RandomForestRegressor(n_estimators=100, min_samples_leaf=5, random_state=1234))\n", + "\n", + "# Run DML model with nfolds folds of cross-fitting (computationally intensive)\n", + "res_RF = dml(x, z, d, y, modely, modeld, modelz, nfolds=5)\n", + "table_RF = summary(*res_RF, x, d, z, y, name='RF')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ijo8p0Moig_F" + }, + "outputs": [], + "source": [ + "results = pd.concat([table_rlasso, table_RF], axis=0)\n", + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r7l6cFi2zcMA", + "papermill": { + "duration": 0.013703, + "end_time": "2021-04-23T10:42:16.269251", + "exception": false, + "start_time": "2021-04-23T10:42:16.255548", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Weak Instruments?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rsUnPDfpzcMB", + "papermill": { + "duration": 16.107321, + "end_time": "2021-04-23T10:42:32.390552", + "exception": false, + "start_time": "2021-04-23T10:42:16.283231", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Summary for RLasso\n", + "resy_rlasso, resD_rlasso, resZ_rlasso = res_rlasso[5], res_rlasso[6], res_rlasso[7]\n", + "# Using HC3 robust standard errors\n", + "regDZ_rlasso = sm.OLS(resD_rlasso, add_constant(resZ_rlasso)).fit(cov_type='HC3', use_t=True)\n", + "print(regDZ_rlasso.summary())\n", + "\n", + "# Summary for RF\n", + "resy_RF, resD_RF, resZ_RF = res_RF[5], res_RF[6], res_RF[7]\n", + "# Using HC3 robust standard errors\n", + "regDZ_RF = sm.OLS(resD_RF, add_constant(resZ_RF)).fit(cov_type='HC3', use_t=True)\n", + "print(regDZ_RF.summary())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L3fDDOmfzcMD", + "papermill": { + "duration": 0.015919, + "end_time": "2021-04-23T10:42:32.423865", + "exception": false, + "start_time": "2021-04-23T10:42:32.407946", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Anderson-Rubin Idea for Inference with Weak Instruments\n", + "\n", + "As shown above, we may have weak instruments because the t-stat in the regression $\\tilde D \\sim \\tilde Z$ is small relative to standard rules of thumb -- for example Stock and Yogo (2005) suggest accounting for weak instruments if the first stage F-statistic is less than 10 (and more recent work suggests even larger cutoffs).\n", + "\n", + "\n", + " Here, we consider one specific approach (from Anderson and Rubin (1949)) to doing valid inference under weak identification based upon the statistic:\n", + "$$\n", + "C(\\theta) = \\frac{ |E_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z]|^2}{ \\mathbb{V}_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z ]/n}.\n", + "$$\n", + "The empirical variance $\\mathbb{V}_n$ is defined as:\n", + "\\begin{align*}\n", + "\\mathbb{V}_{n}[ g(W_i)] &:= \\mathbb{E}_{n}g(W_i)g(W_i)' - \\mathbb{E}_{n}[ g(W_i)]\\mathbb{E}_{n}[ g(W_i)]'.\n", + "\\end{align*}\n", + "\n", + "If $\\theta_0 = \\theta$, then $C(\\theta) \\overset{a}{\\sim} N(0,1)^2 = \\chi^2(1)$. Therefore, we can reject the hypothesis $\\theta_0 = \\theta$ at level $a$ (for example $a = .05$ for a 5\\% level test) if $C(\\theta)> c(1-a)$ where $c(1-a)$ is the $(1-a)$- quantile of a $\\chi^2(1)$ variable. The probability of falsely rejecting the true hypothesis is approximately $a \\times 100\\%$. \n", + "To construct a $(1-a) \\times 100$\\% confidence region for $\\theta$, we can then simply invert the test by collecting all parameter values that are not rejected at the $a$ level:\n", + "$$\n", + "CR(\\theta) = \\{ \\theta \\in \\Theta: C(\\theta) \\leq c(1-a)\\}.\n", + "$$\n", + "\n", + "\n", + "In more complex settings with many controls or controls that enter with unknown functional form, we can simply replace the residuals $\\tilde Y$, $\\tilde D$, and $\\tilde Z$ by machine\n", + "learned cross-fitted residuals $\\check Y$, $\\check D$, and $ \\check Z$. Thanks to the orthogonality of the IV moment condition underlying the formulation outlined above, we can formally assert that the properties of $C(\\theta)$ and the subsequent testing procedure and confidence region for $\\theta$ continue to hold when using cross-fitted residuals. We will further be able to apply the general procedure to cases where $D$\n", + "is a vector, with a suitable adjustment of the statistic $C(\\theta)$.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PG2tPuFzzcMD", + "papermill": { + "duration": 0.015943, + "end_time": "2021-04-23T10:42:32.455719", + "exception": false, + "start_time": "2021-04-23T10:42:32.439776", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "So let's carry out DML inference combined with Anderson-Rubin Idea" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6weyaIdJ3pWs" + }, + "outputs": [], + "source": [ + "from scipy.stats import chi2\n", + "\n", + "\n", + "def DML_AR_PLIV(rY, rD, rZ, grid, alpha=0.05):\n", + " n = len(rY)\n", + " Cstat = np.zeros(len(grid))\n", + "\n", + " for i in range(len(grid)):\n", + " term1 = (rY - grid[i] * rD) * rZ\n", + " Cstat[i] = n * (np.mean(term1))**2 / np.var(term1)\n", + "\n", + " Cstat = np.nan_to_num(Cstat) # Replace any NaNs with zeros\n", + "\n", + " lower_bound = min(grid[Cstat < chi2.ppf(1 - alpha, 1)])\n", + " upper_bound = max(grid[Cstat < chi2.ppf(1 - alpha, 1)])\n", + "\n", + " plt.plot(grid, Cstat, linestyle='-', color='black')\n", + " plt.axhline(y=chi2.ppf(1 - alpha, 1), linestyle='--', color='blue')\n", + " plt.axvline(x=lower_bound, linestyle='--', color='red')\n", + " plt.axvline(x=upper_bound, linestyle='--', color='red')\n", + " plt.xlabel('Effect of institutions')\n", + " plt.ylabel('Statistic')\n", + " plt.title('')\n", + " plt.show()\n", + "\n", + " return {'UB': upper_bound, 'LB': lower_bound}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "k9bB2O13zcME", + "papermill": { + "duration": 0.478528, + "end_time": "2021-04-23T10:42:33.002469", + "exception": false, + "start_time": "2021-04-23T10:42:32.523941", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Using RLasso\n", + "dml_ar_rlasso = DML_AR_PLIV(rY=resy_rlasso, rD=resD_rlasso, rZ=resZ_rlasso, grid=np.linspace(-2, 2, 401))\n", + "print(\"RLasso UB and LB: \", dml_ar_rlasso)\n", + "# Using RF\n", + "dml_ar_RF = DML_AR_PLIV(rY=resy_RF, rD=resD_RF, rZ=resZ_RF, grid=np.linspace(-2, 2, 401))\n", + "print(\"RF UB and LB: \", dml_ar_RF)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "papermill": { + "duration": 62.79319, + "end_time": "2021-04-23T10:42:34.038582", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2021-04-23T10:41:31.245392", + "version": "2.1.3" + } }, - "papermill": { - "duration": 62.79319, - "end_time": "2021-04-23T10:42:34.038582", - "environment_variables": {}, - "exception": null, - "input_path": "__notebook__.ipynb", - "output_path": "__notebook__.ipynb", - "parameters": {}, - "start_time": "2021-04-23T10:41:31.245392", - "version": "2.1.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/AC2/r-debiased-ml-for-partially-linear-iv-model.irnb b/AC2/r-debiased-ml-for-partially-linear-iv-model.irnb index 10f68a89..63e322ff 100644 --- a/AC2/r-debiased-ml-for-partially-linear-iv-model.irnb +++ b/AC2/r-debiased-ml-for-partially-linear-iv-model.irnb @@ -1,529 +1,530 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "qjaFmAyEzcLz", - "papermill": { - "duration": 0.008106, - "end_time": "2021-04-23T10:41:33.911944", - "exception": false, - "start_time": "2021-04-23T10:41:33.903838", - "status": "completed" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "qjaFmAyEzcLz", + "papermill": { + "duration": 0.008106, + "end_time": "2021-04-23T10:41:33.911944", + "exception": false, + "start_time": "2021-04-23T10:41:33.903838", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Double/Debiased ML for Partially Linear IV Model\n", + "\n", + "This is a simple implementation of Debiased Machine Learning for the Partially Linear\n", + "IV Regression Model, which provides an application of DML IV inference.\n", + "\n", + "\n", + "Reference:\n", + "\n", + "- https://arxiv.org/abs/1608.00060\n", + "- https://www.amazon.com/Business-Data-Science-Combining-Accelerate/dp/1260452778\n", + "\n", + "The code is based on the book.\n" + ] }, - "tags": [] - }, - "source": [ - "# Double/Debiased ML for Partially Linear IV Model\n", - "\n", - "This is a simple implementation of Debiased Machine Learning for the Partially Linear\n", - "IV Regression Model, which provides an application of DML IV inference.\n", - "\n", - "\n", - "Reference:\n", - "\n", - "- https://arxiv.org/abs/1608.00060\n", - "- https://www.amazon.com/Business-Data-Science-Combining-Accelerate/dp/1260452778\n", - "\n", - "The code is based on the book.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G3YNyr-ezcL4", - "papermill": { - "duration": 0.006963, - "end_time": "2021-04-23T10:41:33.926085", - "exception": false, - "start_time": "2021-04-23T10:41:33.919122", - "status": "completed" + { + "cell_type": "markdown", + "metadata": { + "id": "G3YNyr-ezcL4", + "papermill": { + "duration": 0.006963, + "end_time": "2021-04-23T10:41:33.926085", + "exception": false, + "start_time": "2021-04-23T10:41:33.919122", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "\n", + "# Partially Linear IV Model\n", + "\n", + "We consider the partially linear structural equation model:\n", + "\\begin{align}\n", + " Y :=~& D\\theta_0 + g_0(X) + \\zeta, & E[\\zeta \\mid Z,X]= 0,\\\\\n", + " Z :=~& m_0(X) + V, & E[V \\mid X] = 0.\n", + "\\end{align}\n", + "\n", + "\n", + "Note that this model is not a regression model unless $Z=D$. The model is a canonical\n", + "model in causal inference, going back to P. Wright's work on IV methods for estimating demand/supply equations, with the modern difference being that $g_0$ and $m_0$ are nonlinear, potentially complicated functions of high-dimensional $X$. \n", + "\n", + "\n", + "The idea of this model is that there is a structural or causal relation between $Y$ and $D$, captured by $\\theta_0$, and $g_0(X) + \\zeta$ is the stochastic error, partly explained by covariates $X$. $V$ and $\\zeta$ are stochastic errors that are not explained by $X$. Since $Y$ and $D$ are jointly determined, we need an external factor, commonly referred to as an instrument, $Z$ to create exogenous variation\n", + "in $D$. Note that $Z$ should affect $D$. The $X$ here serve again as confounding factors, so we can think of variation in $Z$ as being exogenous only conditional on $X$.\n", + "\n", + "\n", + "The causal DAG this model corresponds to is given by:\n", + "$$\n", + "Z \\to D, X \\to (Y, Z, D), L \\to (Y,D),\n", + "$$\n", + "where $L$ is the latent confounder affecting both $Y$ and $D$, but not $Z$.\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "# Example\n", + "\n", + "A simple contextual example is from biostatistics, where $Y$ is a health outcome and $D$ is indicator of smoking. Thus, $\\theta_0$ is captures the effect of smoking on health. Health outcome $Y$ and smoking behavior $D$ are treated as being jointly determined. $X$ represents patient characteristics, and $Z$ could be a doctor's advice not to smoke (or another behavioral treatment) that may affect the outcome $Y$ only through shifting the behavior $D$, conditional on characteristics $X$. \n", + "\n", + "----\n", + "\n", + "\n", + "\n", + "# PLIVM in Residualized Form\n", + "\n", + "The PLIV model above can be rewritten in the following residualized form:\n", + "$$\n", + " \\tilde Y = \\tilde D \\theta_0 + \\zeta, \\quad E[\\zeta \\mid V,X]= 0,\n", + "$$\n", + "where\n", + "$$\n", + " \\tilde Y = (Y- \\ell_0(X)), \\quad \\ell_0(X) = E[Y \\mid X] \\\\\n", + " \\tilde D = (D - r_0(X)), \\quad r_0(X) = E[D \\mid X] \\\\\n", + " \\tilde Z = (Z- m_0(X)), \\quad m_0(X) = E[Z \\mid X].\n", + "$$\n", + " The \"tilde\" variables (e.g. $\\tilde Y$) above represent original variables after taking out or \"partialling out\"\n", + " the effect of $X$. Note that $\\theta_0$ is identified from this equation if $V$\n", + " and $U$ have non-zero correlation, which automatically means that $U$ and $V$\n", + " must have non-zero variation.\n", + "\n", + " \n", + "\n", + "-----\n", + "\n", + "# DML for PLIV Model\n", + "\n", + "Given identification, DML proceeds as follows\n", + "\n", + "Compute the estimates $\\hat \\ell_0$, $\\hat r_0$, and $\\hat m_0$ , which amounts\n", + "to solving the three problems of predicting $Y$, $D$, and $Z$ using\n", + "$X$, using any generic ML method, giving us estimated residuals\n", + "$$\n", + "\\tilde Y = Y - \\hat \\ell_0(X), \\\\ \\tilde D= D - \\hat r_0(X), \\\\ \\tilde Z = Z- \\hat m_0(X).\n", + "$$\n", + "The estimates should be of a cross-validated form, as detailed in the algorithm below.\n", + "\n", + "Estimate $\\theta_0$ by the the intstrumental\n", + "variable regression of $\\tilde Y$ on $\\tilde D$ using $\\tilde Z$ as an instrument.\n", + "Use the conventional inference for the IV regression estimator, ignoring\n", + "the estimation error in these residuals.\n", + "\n", + "The reason we work with this residualized form is that it eliminates the bias\n", + "arising when solving the prediction problem in stage 1. The role of cross-validation\n", + "is to avoid another source of bias due to potential overfitting.\n", + "\n", + "The estimator is adaptive,\n", + "in the sense that the first stage estimation errors do not affect the second\n", + "stage errors.\n" + ] }, - "tags": [] - }, - "source": [ - "\n", - "# Partially Linear IV Model\n", - "\n", - "We consider the partially linear structural equation model:\n", - "\\begin{align}\n", - " Y :=~& D\\theta_0 + g_0(X) + \\zeta, & E[\\zeta \\mid Z,X]= 0,\\\\\n", - " Z :=~& m_0(X) + V, & E[V \\mid X] = 0.\n", - "\\end{align}\n", - "\n", - "\n", - "Note that this model is not a regression model unless $Z=D$. The model is a canonical\n", - "model in causal inference, going back to P. Wright's work on IV methods for estimating demand/supply equations, with the modern difference being that $g_0$ and $m_0$ are nonlinear, potentially complicated functions of high-dimensional $X$. \n", - "\n", - "\n", - "The idea of this model is that there is a structural or causal relation between $Y$ and $D$, captured by $\\theta_0$, and $g_0(X) + \\zeta$ is the stochastic error, partly explained by covariates $X$. $V$ and $\\zeta$ are stochastic errors that are not explained by $X$. Since $Y$ and $D$ are jointly determined, we need an external factor, commonly referred to as an instrument, $Z$ to create exogenous variation\n", - "in $D$. Note that $Z$ should affect $D$. The $X$ here serve again as confounding factors, so we can think of variation in $Z$ as being exogenous only conditional on $X$.\n", - "\n", - "\n", - "The causal DAG this model corresponds to is given by:\n", - "$$\n", - "Z \\to D, X \\to (Y, Z, D), L \\to (Y,D),\n", - "$$\n", - "where $L$ is the latent confounder affecting both $Y$ and $D$, but not $Z$.\n", - "\n", - "\n", - "\n", - "---\n", - "\n", - "# Example\n", - "\n", - "A simple contextual example is from biostatistics, where $Y$ is a health outcome and $D$ is indicator of smoking. Thus, $\\theta_0$ is captures the effect of smoking on health. Health outcome $Y$ and smoking behavior $D$ are treated as being jointly determined. $X$ represents patient characteristics, and $Z$ could be a doctor's advice not to smoke (or another behavioral treatment) that may affect the outcome $Y$ only through shifting the behavior $D$, conditional on characteristics $X$. \n", - "\n", - "----\n", - "\n", - "\n", - "\n", - "# PLIVM in Residualized Form\n", - "\n", - "The PLIV model above can be rewritten in the following residualized form:\n", - "$$\n", - " \\tilde Y = \\tilde D \\theta_0 + \\zeta, \\quad E[\\zeta \\mid V,X]= 0,\n", - "$$\n", - "where\n", - "$$\n", - " \\tilde Y = (Y- \\ell_0(X)), \\quad \\ell_0(X) = E[Y \\mid X] \\\\\n", - " \\tilde D = (D - r_0(X)), \\quad r_0(X) = E[D \\mid X] \\\\\n", - " \\tilde Z = (Z- m_0(X)), \\quad m_0(X) = E[Z \\mid X].\n", - "$$\n", - " The \"tilde\" variables (e.g. $\\tilde Y$) above represent original variables after taking out or \"partialling out\"\n", - " the effect of $X$. Note that $\\theta_0$ is identified from this equation if $V$\n", - " and $U$ have non-zero correlation, which automatically means that $U$ and $V$\n", - " must have non-zero variation.\n", - "\n", - " \n", - "\n", - "-----\n", - "\n", - "# DML for PLIV Model\n", - "\n", - "Given identification, DML proceeds as follows\n", - "\n", - "Compute the estimates $\\hat \\ell_0$, $\\hat r_0$, and $\\hat m_0$ , which amounts\n", - "to solving the three problems of predicting $Y$, $D$, and $Z$ using\n", - "$X$, using any generic ML method, giving us estimated residuals\n", - "$$\n", - "\\tilde Y = Y - \\hat \\ell_0(X), \\\\ \\tilde D= D - \\hat r_0(X), \\\\ \\tilde Z = Z- \\hat m_0(X).\n", - "$$\n", - "The estimates should be of a cross-validated form, as detailed in the algorithm below.\n", - "\n", - "Estimate $\\theta_0$ by the the intstrumental\n", - "variable regression of $\\tilde Y$ on $\\tilde D$ using $\\tilde Z$ as an instrument.\n", - "Use the conventional inference for the IV regression estimator, ignoring\n", - "the estimation error in these residuals.\n", - "\n", - "The reason we work with this residualized form is that it eliminates the bias\n", - "arising when solving the prediction problem in stage 1. The role of cross-validation\n", - "is to avoid another source of bias due to potential overfitting.\n", - "\n", - "The estimator is adaptive,\n", - "in the sense that the first stage estimation errors do not affect the second\n", - "stage errors.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "_kg_hide-output": true, - "id": "yGW6JhG5zcL5", - "papermill": { - "duration": 34.670095, - "end_time": "2021-04-23T10:42:08.603197", - "exception": false, - "start_time": "2021-04-23T10:41:33.933102", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_kg_hide-output": true, + "id": "yGW6JhG5zcL5", + "papermill": { + "duration": 34.670095, + "end_time": "2021-04-23T10:42:08.603197", + "exception": false, + "start_time": "2021-04-23T10:41:33.933102", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "install.packages(\"hdm\")\n", + "install.packages(\"AER\")\n", + "install.packages(\"randomForest\")\n", + "install.packages(\"lfe\")\n", + "install.packages(\"glmnet\")" + ] }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "install.packages(\"hdm\")\n", - "install.packages(\"AER\")\n", - "install.packages(\"randomForest\")\n", - "install.packages(\"lfe\")\n", - "install.packages(\"glmnet\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "library(hdm)\n", - "library(AER) # applied econometrics library\n", - "library(randomForest) # random Forest library\n", - "library(lfe) # high-dimensional econometrics library\n", - "library(glmnet) # glm net\n", - "\n", - "set.seed(1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "j2WVUbBDzcL-", - "papermill": { - "duration": 1.846109, - "end_time": "2021-04-23T10:42:10.458406", - "exception": false, - "start_time": "2021-04-23T10:42:08.612297", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + }, + "id": "kKZcvjhZSsJe" + }, + "outputs": [], + "source": [ + "library(hdm)\n", + "library(AER) # applied econometrics library\n", + "library(randomForest) # random Forest library\n", + "library(lfe) # high-dimensional econometrics library\n", + "library(glmnet) # glm net\n", + "\n", + "set.seed(1)" + ] }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML for PLIVM\n", - "\n", - "dml2_for_plivm <- function(x, d, z, y, dreg, yreg, zreg, nfold = 5) {\n", - " # this implements DML2 algorithm, where there moments are estimated via DML, before constructing\n", - " # the pooled estimate of theta randomly split data into folds\n", - " nobs <- nrow(x)\n", - " foldid <- rep.int(1:nfold, times = ceiling(nobs / nfold))[sample.int(nobs)]\n", - " I <- split(1:nobs, foldid)\n", - " # create residualized objects to fill\n", - " ytil <- dtil <- ztil <- rep(NA, nobs)\n", - " # obtain cross-fitted residuals\n", - " cat(\"fold: \")\n", - " for (b in seq_along(I)) {\n", - " dfit <- dreg(x[-I[[b]], ], d[-I[[b]]]) # take a fold out\n", - " zfit <- zreg(x[-I[[b]], ], z[-I[[b]]]) # take a fold out\n", - " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]]) # take a folot out\n", - " dhat <- predict(dfit, x[I[[b]], ], type = \"response\") # predict the fold out\n", - " zhat <- predict(zfit, x[I[[b]], ], type = \"response\") # predict the fold out\n", - " yhat <- predict(yfit, x[I[[b]], ], type = \"response\") # predict the fold out\n", - " dtil[I[[b]]] <- (d[I[[b]]] - dhat) # record residual\n", - " ztil[I[[b]]] <- (z[I[[b]]] - zhat) # record residual\n", - " ytil[I[[b]]] <- (y[I[[b]]] - yhat) # record residial\n", - " cat(b, \" \")\n", - " }\n", - " ivfit <- tsls(y = ytil, d = dtil, x = NULL, z = ztil, intercept = FALSE)\n", - " coef_est <- ivfit$coef # extract coefficient\n", - " se <- ivfit$se # record standard error\n", - " cat(sprintf(\"\\ncoef (se) = %g (%g)\\n\", coef_est, se))\n", - " return(list(coef_est = coef_est, se = se, dtil = dtil, ytil = ytil, ztil = ztil))\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "x1g3XjsIzcL_", - "papermill": { - "duration": 0.011698, - "end_time": "2021-04-23T10:42:10.482689", - "exception": false, - "start_time": "2021-04-23T10:42:10.470991", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j2WVUbBDzcL-", + "papermill": { + "duration": 1.846109, + "end_time": "2021-04-23T10:42:10.458406", + "exception": false, + "start_time": "2021-04-23T10:42:08.612297", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML for PLIVM\n", + "\n", + "dml2_for_plivm <- function(x, d, z, y, dreg, yreg, zreg, nfold = 5) {\n", + " # this implements DML2 algorithm, where there moments are estimated via DML, before constructing\n", + " # the pooled estimate of theta randomly split data into folds\n", + " nobs <- nrow(x)\n", + " foldid <- rep.int(1:nfold, times = ceiling(nobs / nfold))[sample.int(nobs)]\n", + " I <- split(1:nobs, foldid)\n", + " # create residualized objects to fill\n", + " ytil <- dtil <- ztil <- rep(NA, nobs)\n", + " # obtain cross-fitted residuals\n", + " cat(\"fold: \")\n", + " for (b in seq_along(I)) {\n", + " dfit <- dreg(x[-I[[b]], ], d[-I[[b]]]) # take a fold out\n", + " zfit <- zreg(x[-I[[b]], ], z[-I[[b]]]) # take a fold out\n", + " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]]) # take a folot out\n", + " dhat <- predict(dfit, x[I[[b]], ], type = \"response\") # predict the fold out\n", + " zhat <- predict(zfit, x[I[[b]], ], type = \"response\") # predict the fold out\n", + " yhat <- predict(yfit, x[I[[b]], ], type = \"response\") # predict the fold out\n", + " dtil[I[[b]]] <- (d[I[[b]]] - dhat) # record residual\n", + " ztil[I[[b]]] <- (z[I[[b]]] - zhat) # record residual\n", + " ytil[I[[b]]] <- (y[I[[b]]] - yhat) # record residial\n", + " cat(b, \" \")\n", + " }\n", + " ivfit <- tsls(y = ytil, d = dtil, x = NULL, z = ztil, intercept = FALSE, homoscedastic = FALSE)\n", + " coef_est <- ivfit$coef # extract coefficient\n", + " se <- ivfit$se # record standard error\n", + " cat(sprintf(\"\\ncoef (se) = %g (%g)\\n\", coef_est, se))\n", + " return(list(coef_est = coef_est, se = se, dtil = dtil, ytil = ytil, ztil = ztil))\n", + "}" + ] }, - "tags": [] - }, - "source": [ - "-----\n", - "\n", - "# Emprical Example: Acemoglu, Johnson, Robinson (AER).\n", - "\n", - "\n", - "* Y is log GDP;\n", - "* D is a measure of Protection from Expropriation, a proxy for\n", - "quality of insitutions;\n", - "* Z is the log of Settler's mortality;\n", - "* W are geographical variables (latitude, latitude squared, continent dummies as well as interactions)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0Pc6OCp24rji", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "file <- \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/AJR.csv\"\n", - "AJR <- read.csv(file)\n", - "dim(AJR)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Z9uynanA9H8m", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "y <- AJR$GDP\n", - "d <- AJR$Exprop\n", - "z <- AJR$logMort\n", - "xraw <- model.matrix(~ Latitude + Africa + Asia + Namer + Samer, data = AJR)\n", - "x <- model.matrix(~ -1 + (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2, data = AJR)\n", - "dim(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LwVXRiAqzcL_", - "papermill": { - "duration": 5.743953, - "end_time": "2021-04-23T10:42:16.240693", - "exception": false, - "start_time": "2021-04-23T10:42:10.496740", - "status": "completed" + { + "cell_type": "markdown", + "metadata": { + "id": "x1g3XjsIzcL_", + "papermill": { + "duration": 0.011698, + "end_time": "2021-04-23T10:42:10.482689", + "exception": false, + "start_time": "2021-04-23T10:42:10.470991", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "-----\n", + "\n", + "# Emprical Example: Acemoglu, Johnson, Robinson (AER).\n", + "\n", + "\n", + "* Y is log GDP;\n", + "* D is a measure of Protection from Expropriation, a proxy for\n", + "quality of insitutions;\n", + "* Z is the log of Settler's mortality;\n", + "* W are geographical variables (latitude, latitude squared, continent dummies as well as interactions)\n", + "\n" + ] }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "set.seed(1)\n", - "# DML with PostLasso\n", - "cat(sprintf(\"\\n DML with Post-Lasso \\n\"))\n", - "\n", - "dreg <- function(x, d) {\n", - " hdm::rlasso(x, d)\n", - "} # ML method=lasso\n", - "yreg <- function(x, y) {\n", - " hdm::rlasso(x, y)\n", - "} # ML method=lasso\n", - "zreg <- function(x, z) {\n", - " hdm::rlasso(x, z)\n", - "} # ML method=lasso\n", - "\n", - "dml2_lasso <- dml2_for_plivm(x, d, z, y, dreg, yreg, zreg, nfold = 5)\n", - "\n", - "\n", - "# DML with Random Forest\n", - "cat(sprintf(\"\\n DML with Random Forest \\n\"))\n", - "\n", - "dreg <- function(x, d) {\n", - " randomForest(x, d)\n", - "} # ML method=Forest\n", - "yreg <- function(x, y) {\n", - " randomForest(x, y)\n", - "} # ML method=Forest\n", - "zreg <- function(x, z) {\n", - " randomForest(x, z)\n", - "} # ML method=Forest\n", - "\n", - "dml2_rf <- dml2_for_plivm(xraw, d, z, y, dreg, yreg, zreg, nfold = 5)\n", - "\n", - "# Compare Forest vs Lasso\n", - "comp_tab <- matrix(NA, 3, 2)\n", - "comp_tab[1, ] <- c(sqrt(mean((dml2_rf$ytil)^2)), sqrt(mean((dml2_lasso$ytil)^2)))\n", - "comp_tab[2, ] <- c(sqrt(mean((dml2_rf$dtil)^2)), sqrt(mean((dml2_lasso$dtil)^2)))\n", - "comp_tab[3, ] <- c(sqrt(mean((dml2_rf$ztil)^2)), sqrt(mean((dml2_lasso$ztil)^2)))\n", - "rownames(comp_tab) <- c(\"RMSE for Y:\", \"RMSE for D:\", \"RMSE for Z:\")\n", - "colnames(comp_tab) <- c(\"RF\", \"LASSO\")\n", - "print(comp_tab, digits = 3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r7l6cFi2zcMA", - "papermill": { - "duration": 0.013703, - "end_time": "2021-04-23T10:42:16.269251", - "exception": false, - "start_time": "2021-04-23T10:42:16.255548", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0Pc6OCp24rji", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "file <- \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/AJR.csv\"\n", + "AJR <- read.csv(file)\n", + "dim(AJR)" + ] }, - "tags": [] - }, - "source": [ - "# Weak Instruments?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "rsUnPDfpzcMB", - "papermill": { - "duration": 16.107321, - "end_time": "2021-04-23T10:42:32.390552", - "exception": false, - "start_time": "2021-04-23T10:42:16.283231", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Z9uynanA9H8m", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "y <- AJR$GDP\n", + "d <- AJR$Exprop\n", + "z <- AJR$logMort\n", + "xraw <- model.matrix(~ Latitude + Africa + Asia + Namer + Samer, data = AJR)\n", + "x <- model.matrix(~ -1 + (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2, data = AJR)\n", + "dim(x)" + ] }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# using lfe package\n", - "summary(felm(dml2_lasso$dtil ~ dml2_lasso$ztil), robust = TRUE)\n", - "summary(felm(dml2_rf$dtil ~ dml2_rf$ztil), robust = TRUE)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L3fDDOmfzcMD", - "papermill": { - "duration": 0.015919, - "end_time": "2021-04-23T10:42:32.423865", - "exception": false, - "start_time": "2021-04-23T10:42:32.407946", - "status": "completed" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LwVXRiAqzcL_", + "papermill": { + "duration": 5.743953, + "end_time": "2021-04-23T10:42:16.240693", + "exception": false, + "start_time": "2021-04-23T10:42:10.496740", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "set.seed(1)\n", + "# DML with PostLasso\n", + "cat(sprintf(\"\\n DML with Post-Lasso \\n\"))\n", + "\n", + "dreg <- function(x, d) {\n", + " hdm::rlasso(x, d)\n", + "} # ML method=lasso\n", + "yreg <- function(x, y) {\n", + " hdm::rlasso(x, y)\n", + "} # ML method=lasso\n", + "zreg <- function(x, z) {\n", + " hdm::rlasso(x, z)\n", + "} # ML method=lasso\n", + "\n", + "dml2_lasso <- dml2_for_plivm(x, d, z, y, dreg, yreg, zreg, nfold = 5)\n", + "\n", + "\n", + "# DML with Random Forest\n", + "cat(sprintf(\"\\n DML with Random Forest \\n\"))\n", + "\n", + "dreg <- function(x, d) {\n", + " randomForest(x, d)\n", + "} # ML method=Forest\n", + "yreg <- function(x, y) {\n", + " randomForest(x, y)\n", + "} # ML method=Forest\n", + "zreg <- function(x, z) {\n", + " randomForest(x, z)\n", + "} # ML method=Forest\n", + "\n", + "dml2_rf <- dml2_for_plivm(xraw, d, z, y, dreg, yreg, zreg, nfold = 5)\n", + "\n", + "# Compare Forest vs Lasso\n", + "comp_tab <- matrix(NA, 3, 2)\n", + "comp_tab[1, ] <- c(sqrt(mean((dml2_rf$ytil)^2)), sqrt(mean((dml2_lasso$ytil)^2)))\n", + "comp_tab[2, ] <- c(sqrt(mean((dml2_rf$dtil)^2)), sqrt(mean((dml2_lasso$dtil)^2)))\n", + "comp_tab[3, ] <- c(sqrt(mean((dml2_rf$ztil)^2)), sqrt(mean((dml2_lasso$ztil)^2)))\n", + "rownames(comp_tab) <- c(\"RMSE for Y:\", \"RMSE for D:\", \"RMSE for Z:\")\n", + "colnames(comp_tab) <- c(\"RF\", \"LASSO\")\n", + "print(comp_tab, digits = 3)" + ] }, - "tags": [] - }, - "source": [ - "## Anderson-Rubin Idea for Inference with Weak Instruments\n", - "\n", - "As shown above, we may have weak instruments because the t-stat in the regression $\\tilde D \\sim \\tilde Z$ is small relative to standard rules of thumb -- for example Stock and Yogo (2005) suggest accounting for weak instruments if the first stage F-statistic is less than 10 (and more recent work suggests even larger cutoffs).\n", - "\n", - "\n", - " Here, we consider one specific approach (from Anderson and Rubin (1949)) to doing valid inference under weak identification based upon the statistic:\n", - "$$\n", - "C(\\theta) = \\frac{ |E_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z]|^2}{ \\mathbb{V}_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z ]/n}.\n", - "$$\n", - "The empirical variance $\\mathbb{V}_n$ is defined as:\n", - "\\begin{align*}\n", - "\\mathbb{V}_{n}[ g(W_i)] &:= \\mathbb{E}_{n}g(W_i)g(W_i)' - \\mathbb{E}_{n}[ g(W_i)]\\mathbb{E}_{n}[ g(W_i)]'.\n", - "\\end{align*}\n", - "\n", - "If $\\theta_0 = \\theta$, then $C(\\theta) \\overset{a}{\\sim} N(0,1)^2 = \\chi^2(1)$. Therefore, we can reject the hypothesis $\\theta_0 = \\theta$ at level $a$ (for example $a = .05$ for a 5\\% level test) if $C(\\theta)> c(1-a)$ where $c(1-a)$ is the $(1-a)$- quantile of a $\\chi^2(1)$ variable. The probability of falsely rejecting the true hypothesis is approximately $a \\times 100\\%$. \n", - "To construct a $(1-a) \\times 100$\\% confidence region for $\\theta$, we can then simply invert the test by collecting all parameter values that are not rejected at the $a$ level:\n", - "$$\n", - "CR(\\theta) = \\{ \\theta \\in \\Theta: C(\\theta) \\leq c(1-a)\\}.\n", - "$$\n", - "\n", - "\n", - "In more complex settings with many controls or controls that enter with unknown functional form, we can simply replace the residuals $\\tilde Y$, $\\tilde D$, and $\\tilde Z$ by machine\n", - "learned cross-fitted residuals $\\check Y$, $\\check D$, and $ \\check Z$. Thanks to the orthogonality of the IV moment condition underlying the formulation outlined above, we can formally assert that the properties of $C(\\theta)$ and the subsequent testing procedure and confidence region for $\\theta$ continue to hold when using cross-fitted residuals. We will further be able to apply the general procedure to cases where $D$\n", - "is a vector, with a suitable adjustment of the statistic $C(\\theta)$.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "zi5Gkqw2zcMD", - "papermill": { - "duration": 0.036055, - "end_time": "2021-04-23T10:42:32.507790", - "exception": false, - "start_time": "2021-04-23T10:42:32.471735", - "status": "completed" + { + "cell_type": "markdown", + "metadata": { + "id": "r7l6cFi2zcMA", + "papermill": { + "duration": 0.013703, + "end_time": "2021-04-23T10:42:16.269251", + "exception": false, + "start_time": "2021-04-23T10:42:16.255548", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Weak Instruments?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rsUnPDfpzcMB", + "papermill": { + "duration": 16.107321, + "end_time": "2021-04-23T10:42:32.390552", + "exception": false, + "start_time": "2021-04-23T10:42:16.283231", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# using lfe package\n", + "summary(felm(dml2_lasso$dtil ~ dml2_lasso$ztil), robust = TRUE)\n", + "summary(felm(dml2_rf$dtil ~ dml2_rf$ztil), robust = TRUE)" + ] }, - "tags": [], - "vscode": { - "languageId": "r" + { + "cell_type": "markdown", + "metadata": { + "id": "L3fDDOmfzcMD", + "papermill": { + "duration": 0.015919, + "end_time": "2021-04-23T10:42:32.423865", + "exception": false, + "start_time": "2021-04-23T10:42:32.407946", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Anderson-Rubin Idea for Inference with Weak Instruments\n", + "\n", + "As shown above, we may have weak instruments because the t-stat in the regression $\\tilde D \\sim \\tilde Z$ is small relative to standard rules of thumb -- for example Stock and Yogo (2005) suggest accounting for weak instruments if the first stage F-statistic is less than 10 (and more recent work suggests even larger cutoffs).\n", + "\n", + "\n", + " Here, we consider one specific approach (from Anderson and Rubin (1949)) to doing valid inference under weak identification based upon the statistic:\n", + "$$\n", + "C(\\theta) = \\frac{ |E_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z]|^2}{ \\mathbb{V}_n [(\\tilde Y - \\theta \\tilde D) \\tilde Z ]/n}.\n", + "$$\n", + "The empirical variance $\\mathbb{V}_n$ is defined as:\n", + "\\begin{align*}\n", + "\\mathbb{V}_{n}[ g(W_i)] &:= \\mathbb{E}_{n}g(W_i)g(W_i)' - \\mathbb{E}_{n}[ g(W_i)]\\mathbb{E}_{n}[ g(W_i)]'.\n", + "\\end{align*}\n", + "\n", + "If $\\theta_0 = \\theta$, then $C(\\theta) \\overset{a}{\\sim} N(0,1)^2 = \\chi^2(1)$. Therefore, we can reject the hypothesis $\\theta_0 = \\theta$ at level $a$ (for example $a = .05$ for a 5\\% level test) if $C(\\theta)> c(1-a)$ where $c(1-a)$ is the $(1-a)$- quantile of a $\\chi^2(1)$ variable. The probability of falsely rejecting the true hypothesis is approximately $a \\times 100\\%$. \n", + "To construct a $(1-a) \\times 100$\\% confidence region for $\\theta$, we can then simply invert the test by collecting all parameter values that are not rejected at the $a$ level:\n", + "$$\n", + "CR(\\theta) = \\{ \\theta \\in \\Theta: C(\\theta) \\leq c(1-a)\\}.\n", + "$$\n", + "\n", + "\n", + "In more complex settings with many controls or controls that enter with unknown functional form, we can simply replace the residuals $\\tilde Y$, $\\tilde D$, and $\\tilde Z$ by machine\n", + "learned cross-fitted residuals $\\check Y$, $\\check D$, and $ \\check Z$. Thanks to the orthogonality of the IV moment condition underlying the formulation outlined above, we can formally assert that the properties of $C(\\theta)$ and the subsequent testing procedure and confidence region for $\\theta$ continue to hold when using cross-fitted residuals. We will further be able to apply the general procedure to cases where $D$\n", + "is a vector, with a suitable adjustment of the statistic $C(\\theta)$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zi5Gkqw2zcMD", + "papermill": { + "duration": 0.036055, + "end_time": "2021-04-23T10:42:32.507790", + "exception": false, + "start_time": "2021-04-23T10:42:32.471735", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML-AR (DML with Anderson-Rubin)\n", + "\n", + "dml_ar_pliv <- function(rY, rD, rZ, grid, alpha = .05) {\n", + " n <- length(rY)\n", + " Cstat <- rep(0, length(grid))\n", + " for (i in seq_along(grid)) {\n", + " Cstat[i] <- n * (mean((rY - grid[i] * rD) * rZ))^2 / var((rY - grid[i] * rD) * rZ)\n", + " }\n", + " LB <- min(grid[Cstat < qchisq(1 - alpha, 1)])\n", + " UB <- max(grid[Cstat < qchisq(1 - alpha, 1)])\n", + " plot(range(grid), range(c(Cstat)), type = \"n\", xlab = \"Effect of institutions\", ylab = \"Statistic\", main = \" \")\n", + " lines(grid, Cstat, lty = 1, col = 1)\n", + " abline(h = qchisq(1 - alpha, 1), lty = 3, col = 4)\n", + " abline(v = LB, lty = 3, col = 2)\n", + " abline(v = UB, lty = 3, col = 2)\n", + " return(list(UB = UB, LB = LB))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "k9bB2O13zcME", + "papermill": { + "duration": 0.478528, + "end_time": "2021-04-23T10:42:33.002469", + "exception": false, + "start_time": "2021-04-23T10:42:32.523941", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "dml_ar_pliv(\n", + " rY = dml2_lasso$ytil, rD = dml2_lasso$dtil, rZ = dml2_lasso$ztil,\n", + " grid = seq(-2, 2, by = .01)\n", + ")\n", + "\n", + "dml_ar_pliv(\n", + " rY = dml2_rf$ytil, rD = dml2_rf$dtil, rZ = dml2_rf$ztil,\n", + " grid = seq(-2, 2, by = .01)\n", + ")" + ] } - }, - "outputs": [], - "source": [ - "# DML-AR (DML with Anderson-Rubin)\n", - "\n", - "dml_ar_pliv <- function(rY, rD, rZ, grid, alpha = .05) {\n", - " n <- length(rY)\n", - " Cstat <- rep(0, length(grid))\n", - " for (i in seq_along(grid)) {\n", - " Cstat[i] <- n * (mean((rY - grid[i] * rD) * rZ))^2 / var((rY - grid[i] * rD) * rZ)\n", - " }\n", - " LB <- min(grid[Cstat < qchisq(1 - alpha, 1)])\n", - " UB <- max(grid[Cstat < qchisq(1 - alpha, 1)])\n", - " plot(range(grid), range(c(Cstat)), type = \"n\", xlab = \"Effect of institutions\", ylab = \"Statistic\", main = \" \")\n", - " lines(grid, Cstat, lty = 1, col = 1)\n", - " abline(h = qchisq(1 - alpha, 1), lty = 3, col = 4)\n", - " abline(v = LB, lty = 3, col = 2)\n", - " abline(v = UB, lty = 3, col = 2)\n", - " return(list(UB = UB, LB = LB))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "k9bB2O13zcME", - "papermill": { - "duration": 0.478528, - "end_time": "2021-04-23T10:42:33.002469", - "exception": false, - "start_time": "2021-04-23T10:42:32.523941", - "status": "completed" + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" }, - "tags": [], - "vscode": { - "languageId": "r" + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.3" + }, + "papermill": { + "duration": 62.79319, + "end_time": "2021-04-23T10:42:34.038582", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2021-04-23T10:41:31.245392", + "version": "2.1.3" } - }, - "outputs": [], - "source": [ - "dml_ar_pliv(\n", - " rY = dml2_lasso$ytil, rD = dml2_lasso$dtil, rZ = dml2_lasso$ztil,\n", - " grid = seq(-2, 2, by = .01)\n", - ")\n", - "\n", - "dml_ar_pliv(\n", - " rY = dml2_rf$ytil, rD = dml2_rf$dtil, rZ = dml2_rf$ztil,\n", - " grid = seq(-2, 2, by = .01)\n", - ")" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "3.6.3" }, - "papermill": { - "duration": 62.79319, - "end_time": "2021-04-23T10:42:34.038582", - "environment_variables": {}, - "exception": null, - "input_path": "__notebook__.ipynb", - "output_path": "__notebook__.ipynb", - "parameters": {}, - "start_time": "2021-04-23T10:41:31.245392", - "version": "2.1.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From b26815d66fff9e4bbbef51de674c5cadfb7cf8de Mon Sep 17 00:00:00 2001 From: chansen776 Date: Wed, 30 Sep 2026 14:46:54 -0500 Subject: [PATCH 19/32] AC-2 401k notebooks Corrections to 401k notebook code - some nuisance functions incorrectly input Update to run on current google colab --- AC2/python-dml-401k-IV.ipynb | 3636 ++++++++++++------------- AC2/r-dml-401k-IV.irnb | 4944 +++++++++++++++++----------------- 2 files changed, 4297 insertions(+), 4283 deletions(-) diff --git a/AC2/python-dml-401k-IV.ipynb b/AC2/python-dml-401k-IV.ipynb index 6c8f52f0..71d6c21e 100644 --- a/AC2/python-dml-401k-IV.ipynb +++ b/AC2/python-dml-401k-IV.ipynb @@ -1,1820 +1,1820 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "0", - "metadata": { - "id": "narrative-sailing", - "papermill": { - "duration": 0.040767, - "end_time": "2021-03-24T14:13:51.082088", - "exception": false, - "start_time": "2021-03-24T14:13:51.041321", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Inference on Predictive and Causal Effects in High-Dimensional Nonlinear Models" - ] - }, - { - "cell_type": "markdown", - "id": "1", - "metadata": { - "id": "ready-appearance", - "papermill": { - "duration": 0.036878, - "end_time": "2021-03-24T14:13:51.155414", - "exception": false, - "start_time": "2021-03-24T14:13:51.118536", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Impact of 401(k) on Financial Wealth\n", - "\n", - "We consider estimation of the effect of 401(k) participation\n", - "on accumulated assets. 401(k) plans are pension accounts sponsored by employers. The key problem in determining the effect of participation in 401(k) plans on accumulated assets is saver heterogeneity coupled with the fact that the decision to enroll in a 401(k) is non-random. It is generally recognized that some people have a higher preference for saving than others. It also seems likely that those individuals with high unobserved preference for saving would be most likely to choose to participate in tax-advantaged retirement savings plans and would tend to have otherwise high amounts of accumulated assets. The presence of unobserved savings preferences with these properties then implies that conventional estimates that do not account for saver heterogeneity and endogeneity of participation will be biased upward, tending to overstate the savings effects of 401(k) participation.\n", - "\n", - "One can argue that eligibility for enrolling in a 401(k) plan in this data can be taken as exogenous after conditioning on a few observables of which the most important for their argument is income. The basic idea is that, at least around the time 401(k)’s initially became available, people were unlikely to be basing their employment decisions on whether an employer offered a 401(k) but would instead focus on income and other aspects of the job." - ] - }, - { - "cell_type": "markdown", - "id": "2", - "metadata": { - "id": "divine-phoenix", - "papermill": { - "duration": 0.036497, - "end_time": "2021-03-24T14:13:51.229370", - "exception": false, - "start_time": "2021-03-24T14:13:51.192873", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Data\n", - "\n", - "The data set can be downloaded from the github repo\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3", - "metadata": { - "id": "teq-s8pb3a82" - }, - "outputs": [], - "source": [ - "!pip install wget" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4", - "metadata": { - "id": "75662e18" - }, - "outputs": [], - "source": [ - "# Import relevant packages\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import cross_val_predict, KFold\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.linear_model import LassoCV, LogisticRegressionCV\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", - "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\n", - "import scipy.stats\n", - "import warnings\n", - "from sklearn.base import BaseEstimator, clone\n", - "from IPython.display import Markdown\n", - "import wget\n", - "import seaborn as sns\n", - "warnings.simplefilter('ignore')\n", - "np.random.seed(1234)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5", - "metadata": { - "id": "6e66e77f" - }, - "outputs": [], - "source": [ - "file = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/401k.csv\"\n", - "data = pd.read_csv(file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6", - "metadata": { - "id": "65a2d086" - }, - "outputs": [], - "source": [ - "data.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7", - "metadata": { - "id": "00884061" - }, - "outputs": [], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8", - "metadata": { - "id": "1d3dc85c" - }, - "outputs": [], - "source": [ - "readme = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/401k.md\"\n", - "filename = wget.download(readme)\n", - "Markdown(open(filename, 'r').read())" - ] - }, - { - "cell_type": "markdown", - "id": "9", - "metadata": { - "id": "looking-invention", - "papermill": { - "duration": 0.037509, - "end_time": "2021-03-24T14:13:52.250583", - "exception": false, - "start_time": "2021-03-24T14:13:52.213074", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "The data consist of 9,915 observations at the household level drawn from the 1991 Survey of Income and Program Participation (SIPP). All the variables are referred to 1990. We use net financial assets (*net\\_tfa*) as the outcome variable, $Y$, in our analysis. The net financial assets are computed as the sum of IRA balances, 401(k) balances, checking accounts, saving bonds, other interest-earning accounts, other interest-earning assets, stocks, and mutual funds less non mortgage debts." - ] - }, - { - "cell_type": "markdown", - "id": "10", - "metadata": { - "id": "received-nutrition", - "papermill": { - "duration": 0.038843, - "end_time": "2021-03-24T14:13:52.327055", - "exception": false, - "start_time": "2021-03-24T14:13:52.288212", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Among the $9915$ individuals, $3682$ are eligible to participate in the program. The variable *e401* indicates eligibility and *p401* indicates participation, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11", - "metadata": { - "id": "0d26d552" - }, - "outputs": [], - "source": [ - "sns.countplot(data, x='e401')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "12", - "metadata": { - "id": "material-sending", - "papermill": { - "duration": 0.039406, - "end_time": "2021-03-24T14:13:53.435306", - "exception": false, - "start_time": "2021-03-24T14:13:53.395900", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Eligibility is highly associated with financial wealth:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13", - "metadata": { - "id": "a7741bb6" - }, - "outputs": [], - "source": [ - "sns.displot(data=data, x='net_tfa', kind='kde', col='e401', hue='e401', fill=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "14", - "metadata": { - "id": "awful-antigua", - "papermill": { - "duration": 0.040645, - "end_time": "2021-03-24T14:13:54.245822", - "exception": false, - "start_time": "2021-03-24T14:13:54.205177", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "The unconditional APE of e401 is about $19559$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15", - "metadata": { - "id": "ef070d79" - }, - "outputs": [], - "source": [ - "e1 = data[data['e401'] == 1]['net_tfa']\n", - "e0 = data[data['e401'] == 0]['net_tfa']\n", - "print(f'{np.mean(e1) - np.mean(e0):.0f}')" - ] - }, - { - "cell_type": "markdown", - "id": "16", - "metadata": { - "id": "cross-priority", - "papermill": { - "duration": 0.041187, - "end_time": "2021-03-24T14:13:54.437695", - "exception": false, - "start_time": "2021-03-24T14:13:54.396508", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Among the $3682$ individuals that are eligible, $2594$ decided to participate in the program. The unconditional APE of p401 is about $27372$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17", - "metadata": { - "id": "33cd014e" - }, - "outputs": [], - "source": [ - "e1 = data[data['p401'] == 1]['net_tfa']\n", - "e0 = data[data['p401'] == 0]['net_tfa']\n", - "print(f'{np.mean(e1) - np.mean(e0):.0f}')" - ] - }, - { - "cell_type": "markdown", - "id": "18", - "metadata": { - "id": "suitable-vulnerability", + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": { + "id": "0", + "papermill": { + "duration": 0.040767, + "end_time": "2021-03-24T14:13:51.082088", + "exception": false, + "start_time": "2021-03-24T14:13:51.041321", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Inference on Predictive and Causal Effects in High-Dimensional Nonlinear Models" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": { + "id": "1", + "papermill": { + "duration": 0.036878, + "end_time": "2021-03-24T14:13:51.155414", + "exception": false, + "start_time": "2021-03-24T14:13:51.118536", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Impact of 401(k) on Financial Wealth\n", + "\n", + "We consider estimation of the effect of 401(k) participation\n", + "on accumulated assets. 401(k) plans are pension accounts sponsored by employers. The key problem in determining the effect of participation in 401(k) plans on accumulated assets is saver heterogeneity coupled with the fact that the decision to enroll in a 401(k) is non-random. It is generally recognized that some people have a higher preference for saving than others. It also seems likely that those individuals with high unobserved preference for saving would be most likely to choose to participate in tax-advantaged retirement savings plans and would tend to have otherwise high amounts of accumulated assets. The presence of unobserved savings preferences with these properties then implies that conventional estimates that do not account for saver heterogeneity and endogeneity of participation will be biased upward, tending to overstate the savings effects of 401(k) participation.\n", + "\n", + "One can argue that eligibility for enrolling in a 401(k) plan in this data can be taken as exogenous after conditioning on a few observables of which the most important for their argument is income. The basic idea is that, at least around the time 401(k)’s initially became available, people were unlikely to be basing their employment decisions on whether an employer offered a 401(k) but would instead focus on income and other aspects of the job." + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": { + "id": "2", + "papermill": { + "duration": 0.036497, + "end_time": "2021-03-24T14:13:51.229370", + "exception": false, + "start_time": "2021-03-24T14:13:51.192873", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Data\n", + "\n", + "The data set can be downloaded from the github repo\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": { + "id": "3" + }, + "outputs": [], + "source": [ + "!pip install wget" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": { + "id": "4" + }, + "outputs": [], + "source": [ + "# Import relevant packages\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import cross_val_predict, KFold\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.linear_model import LassoCV, LogisticRegressionCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\n", + "import scipy.stats\n", + "import warnings\n", + "from sklearn.base import BaseEstimator, clone\n", + "from IPython.display import Markdown\n", + "import wget\n", + "import seaborn as sns\n", + "warnings.simplefilter('ignore')\n", + "np.random.seed(1234)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": { + "id": "5" + }, + "outputs": [], + "source": [ + "file = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/401k.csv\"\n", + "data = pd.read_csv(file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": { + "id": "6" + }, + "outputs": [], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": { + "id": "7" + }, + "outputs": [], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": { + "id": "8" + }, + "outputs": [], + "source": [ + "readme = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/401k.md\"\n", + "filename = wget.download(readme)\n", + "Markdown(open(filename, 'r').read())" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": { + "id": "9", + "papermill": { + "duration": 0.037509, + "end_time": "2021-03-24T14:13:52.250583", + "exception": false, + "start_time": "2021-03-24T14:13:52.213074", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The data consist of 9,915 observations at the household level drawn from the 1991 Survey of Income and Program Participation (SIPP). All the variables are referred to 1990. We use net financial assets (*net\\_tfa*) as the outcome variable, $Y$, in our analysis. The net financial assets are computed as the sum of IRA balances, 401(k) balances, checking accounts, saving bonds, other interest-earning accounts, other interest-earning assets, stocks, and mutual funds less non mortgage debts." + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": { + "id": "10", + "papermill": { + "duration": 0.038843, + "end_time": "2021-03-24T14:13:52.327055", + "exception": false, + "start_time": "2021-03-24T14:13:52.288212", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Among the $9915$ individuals, $3682$ are eligible to participate in the program. The variable *e401* indicates eligibility and *p401* indicates participation, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": { + "id": "11" + }, + "outputs": [], + "source": [ + "sns.countplot(data, x='e401')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": { + "id": "12", + "papermill": { + "duration": 0.039406, + "end_time": "2021-03-24T14:13:53.435306", + "exception": false, + "start_time": "2021-03-24T14:13:53.395900", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Eligibility is highly associated with financial wealth:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": { + "id": "13" + }, + "outputs": [], + "source": [ + "sns.displot(data=data, x='net_tfa', kind='kde', col='e401', hue='e401', fill=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": { + "id": "14", + "papermill": { + "duration": 0.040645, + "end_time": "2021-03-24T14:13:54.245822", + "exception": false, + "start_time": "2021-03-24T14:13:54.205177", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The unconditional APE of e401 is about $19559$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": { + "id": "15" + }, + "outputs": [], + "source": [ + "e1 = data[data['e401'] == 1]['net_tfa']\n", + "e0 = data[data['e401'] == 0]['net_tfa']\n", + "print(f'{np.mean(e1) - np.mean(e0):.0f}')" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": { + "id": "16", + "papermill": { + "duration": 0.041187, + "end_time": "2021-03-24T14:13:54.437695", + "exception": false, + "start_time": "2021-03-24T14:13:54.396508", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Among the $3682$ individuals that are eligible, $2594$ decided to participate in the program. The unconditional APE of p401 is about $27372$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": { + "id": "17" + }, + "outputs": [], + "source": [ + "e1 = data[data['p401'] == 1]['net_tfa']\n", + "e0 = data[data['p401'] == 0]['net_tfa']\n", + "print(f'{np.mean(e1) - np.mean(e0):.0f}')" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": { + "id": "18", + "papermill": { + "duration": 0.041678, + "end_time": "2021-03-24T14:13:54.640532", + "exception": false, + "start_time": "2021-03-24T14:13:54.598854", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As discussed, these estimates are biased since they do not account for saver heterogeneity and endogeneity of participation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": { + "id": "19" + }, + "outputs": [], + "source": [ + "y = data['net_tfa'].values\n", + "Z = data['e401'].values\n", + "D = data['p401'].values\n", + "X = data.drop(['e401', 'p401', 'a401', 'tw', 'tfa', 'net_tfa', 'tfa_he',\n", + " 'hval', 'hmort', 'hequity',\n", + " 'nifa', 'net_nifa', 'net_n401', 'ira',\n", + " 'dum91', 'icat', 'ecat', 'zhat',\n", + " 'i1', 'i2', 'i3', 'i4', 'i5', 'i6', 'i7',\n", + " 'a1', 'a2', 'a3', 'a4', 'a5'], axis=1)\n", + "X.columns" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": { + "id": "20" + }, + "source": [ + "### We define a transformer that constructs the engineered features for controls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": { + "id": "21" + }, + "outputs": [], + "source": [ + "!pip install formulaic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": { + "id": "22" + }, + "outputs": [], + "source": [ + "from sklearn.base import TransformerMixin\n", + "from formulaic import Formula\n", + "\n", + "\n", + "class FormulaTransformer(TransformerMixin, BaseEstimator):\n", + "\n", + " def __init__(self, formula, array=False):\n", + " self.formula = formula\n", + " self.array = array\n", + "\n", + " def fit(self, X, y=None):\n", + " return self\n", + "\n", + " def transform(self, X, y=None):\n", + " df = Formula(self.formula).get_model_matrix(X)\n", + " if self.array:\n", + " return df.values\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": { + "id": "23" + }, + "outputs": [], + "source": [ + "transformer = FormulaTransformer(\"0 + poly(age, degree=6, raw=True) + poly(inc, degree=8, raw=True) \"\n", + " \"+ poly(educ, degree=4, raw=True) + poly(fsize, degree=2, raw=True) \"\n", + " \"+ male + marr + twoearn + db + pira + hown\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": { + "id": "24" + }, + "outputs": [], + "source": [ + "transformer.fit_transform(X).head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": { + "id": "25" + }, + "outputs": [], + "source": [ + "transformer = FormulaTransformer(\"0 + poly(age, degree=6, raw=True) + poly(inc, degree=8, raw=True) \"\n", + " \"+ poly(educ, degree=4, raw=True) + poly(fsize, degree=2, raw=True) \"\n", + " \"+ male + marr + twoearn + db + pira + hown\", array=True)" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": { + "id": "26" + }, + "source": [ + "# Instrumental Variables: Effect of 401k Participation on Financial Assets" + ] + }, + { + "cell_type": "markdown", + "id": "27", + "metadata": { + "id": "27" + }, + "source": [ + "## Double ML IV under Partial Linearity" + ] + }, + { + "cell_type": "markdown", + "id": "28", + "metadata": { + "id": "28" + }, + "source": [ + "Now, we consider estimation of average treatment effects of participation in 401k, i.e. `p401`, with the binary instrument being eligibility in 401k, i.e. `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. We consider a partially linear structural equation model:\n", + "\\begin{eqnarray*}\n", + "Y & := & g_Y(\\epsilon_Y) D + f_Y(A, X, \\epsilon_Y), \\\\\n", + "D & := & f_D(Z, X, A, \\epsilon_D), \\\\\n", + "Z & := & f_Z(X, \\epsilon_Z),\\\\\n", + "A & : = & f_A(X, \\epsilon_A), \\\\\n", + "X & := & \\epsilon_X,\n", + "\\end{eqnarray*}\n", + "where $A$ is a vector of un-observed confounders.\n", + "\n", + "Under this structural equation model, the average treatment effect:\n", + "\\begin{align}\n", + "\\alpha = E[Y(1) - Y(0)]\n", + "\\end{align}\n", + "can be identified by the moment restriction:\n", + "\\begin{align}\n", + "E[(\\tilde{Y} - \\alpha \\tilde{D}) \\tilde{Z}] = 0\n", + "\\end{align}\n", + "where for any variable $V$, we denote with $\\tilde{V} = V - E[V|X]$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29", + "metadata": { + "id": "29" + }, + "outputs": [], + "source": [ + "modely = make_pipeline(transformer, StandardScaler(), LassoCV())\n", + "modeld = make_pipeline(transformer, StandardScaler(), LassoCV())\n", + "modelz = make_pipeline(transformer, StandardScaler(), LassoCV())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "metadata": { + "id": "30" + }, + "outputs": [], + "source": [ + "resy = y - modely.fit(X, y).predict(X)\n", + "resZ = Z - modelz.fit(X, Z).predict(X) # instrument is e401k (eligibility)\n", + "resD = D - modeld.fit(X, D).predict(X) # treatment is p401k (participation)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31", + "metadata": { + "id": "31" + }, + "outputs": [], + "source": [ + "np.mean(resy * resZ) / np.mean(resD * resZ)" + ] + }, + { + "cell_type": "markdown", + "id": "32", + "metadata": { + "id": "32" + }, + "source": [ + "Recall if we want to do inference, we need to either use the theoretically driven penalty parameter for Lasso or perform cross-fitting." + ] + }, + { + "cell_type": "markdown", + "id": "33", + "metadata": { + "id": "33" + }, + "source": [ + "### DML with Non-Linear ML Models and Cross-fitting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34", + "metadata": { + "id": "34" + }, + "outputs": [], + "source": [ + "def dml(X, Z, D, y, modely, modeld, modelz, *, nfolds, classifier=False):\n", + " '''\n", + " DML for the Partially Linear Model setting with cross-fitting\n", + "\n", + " Input\n", + " -----\n", + " X: the controls\n", + " Z: the instrument\n", + " D: the treatment\n", + " y: the outcome\n", + " modely: the ML model for predicting the outcome y\n", + " modeld: the ML model for predicting the treatment D\n", + " modelz: the ML model for predicting the instrument Z\n", + " nfolds: the number of folds in cross-fitting\n", + " classifier: bool, whether the modeld is a classifier or a regressor\n", + "\n", + " Output\n", + " ------\n", + " point: the point estimate of the treatment effect of D on y\n", + " stderr: the standard error of the treatment effect\n", + " yhat: the cross-fitted predictions for the outcome y\n", + " Dhat: the cross-fitted predictions for the treatment D\n", + " Zhat: the cross-fitted predictions for the instrument Z\n", + " resy: the outcome residuals\n", + " resD: the treatment residuals\n", + " resZ: the instrument residuals\n", + " epsilon: the final residual-on-residual OLS regression residual\n", + " '''\n", + " cv = KFold(n_splits=nfolds, shuffle=True, random_state=123) # shuffled k-folds\n", + " yhat = cross_val_predict(modely, X, y, cv=cv, n_jobs=-1) # out-of-fold predictions for y\n", + " # out-of-fold predictions for D\n", + " # use predict or predict_proba dependent on classifier or regressor for D\n", + " if classifier:\n", + " Dhat = cross_val_predict(modeld, X, D, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", + " Zhat = cross_val_predict(modelz, X, Z, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", + " else:\n", + " Dhat = cross_val_predict(modeld, X, D, cv=cv, n_jobs=-1)\n", + " Zhat = cross_val_predict(modelz, X, Z, cv=cv, n_jobs=-1)\n", + " # calculate outcome and treatment residuals\n", + " resy = y - yhat\n", + " resD = D - Dhat\n", + " resZ = Z - Zhat\n", + " # final stage ols based point estimate and standard error\n", + " point = np.mean(resy * resZ) / np.mean(resD * resZ)\n", + " epsilon = resy - point * resD\n", + " var = np.mean(epsilon**2 * resZ**2) / np.mean(resD * resZ)**2\n", + " stderr = np.sqrt(var / X.shape[0])\n", + " return point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35", + "metadata": { + "id": "35" + }, + "outputs": [], + "source": [ + "def summary(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon, X, Z, D, y, *, name):\n", + " '''\n", + " Convenience summary function that takes the results of the DML function\n", + " and summarizes several estimation quantities and performance metrics.\n", + " '''\n", + " return pd.DataFrame({'estimate': point, # point estimate\n", + " 'stderr': stderr, # standard error\n", + " 'lower': point - 1.96 * stderr, # lower end of 95% confidence interval\n", + " 'upper': point + 1.96 * stderr, # upper end of 95% confidence interval\n", + " 'rmse y': np.sqrt(np.mean(resy**2)), # RMSE of model that predicts outcome y\n", + " 'rmse D': np.sqrt(np.mean(resD**2)), # RMSE of model that predicts treatment D\n", + " 'rmse Z': np.sqrt(np.mean(resZ**2)), # RMSE of model that predicts treatment D\n", + " 'accuracy D': np.mean(np.abs(resD) < .5), # binary classification accuracy of model for D\n", + " 'accuracy Z': np.mean(np.abs(resZ) < .5), # binary classification accuracy of model for Z\n", + " }, index=[name])" + ] + }, + { + "cell_type": "markdown", + "id": "36", + "metadata": { + "id": "36" + }, + "source": [ + "#### Double Lasso with Cross-Fitting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37", + "metadata": { + "id": "37" + }, + "outputs": [], + "source": [ + "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", + "lassoy = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", + "lassod = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", + "lassoz = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", + "result = dml(X, Z, D, y, lassoy, lassod, lassoz, nfolds=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38", + "metadata": { + "id": "38" + }, + "outputs": [], + "source": [ + "table = summary(*result, X, Z, D, y, name='double lasso')\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "39", + "metadata": { + "id": "39" + }, + "source": [ + "#### Using a Penalized Logistic Regression for D" + ] + }, + { + "cell_type": "markdown", + "id": "40", + "metadata": { + "id": "40" + }, + "source": [ + "Note the default logistic regression uses an ℓ2 penalty. You can use the ℓ1 penalty as well, but computation will take longer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41", + "metadata": { + "id": "41" + }, + "outputs": [], + "source": [ + "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", + "lassoy = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", + "lgrd = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", + "lgrz = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", + "result = dml(X, Z, D, y, lassoy, lgrd, lgrz, nfolds=3, classifier=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42", + "metadata": { + "id": "42" + }, + "outputs": [], + "source": [ + "table = pd.concat([table, summary(*result, X, Z, D, y, name='lasso/logistic')])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "43", + "metadata": { + "id": "43" + }, + "source": [ + "### Random Forests" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44", + "metadata": { + "id": "44" + }, + "outputs": [], + "source": [ + "rfy = make_pipeline(transformer, RandomForestRegressor(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", + "rfd = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", + "rfz = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", + "result = dml(X, Z, D, y, rfy, rfd, rfz, nfolds=3, classifier=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45", + "metadata": { + "id": "45" + }, + "outputs": [], + "source": [ + "table = pd.concat([table, summary(*result, X, Z, D, y, name='random forest')])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "46", + "metadata": { + "id": "46" + }, + "source": [ + "### Decision Trees" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47", + "metadata": { + "id": "47" + }, + "outputs": [], + "source": [ + "dtry = make_pipeline(transformer, DecisionTreeRegressor(min_samples_leaf=10, ccp_alpha=.001))\n", + "dtrd = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", + "dtrz = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", + "result = dml(X, Z, D, y, dtry, dtrd, dtrz, nfolds=3, classifier=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48", + "metadata": { + "id": "48" + }, + "outputs": [], + "source": [ + "table = pd.concat([table, summary(*result, X, Z, D, y, name='decision tree')])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "49", + "metadata": { + "id": "49" + }, + "source": [ + "### Boosted Trees" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50", + "metadata": { + "id": "50" + }, + "outputs": [], + "source": [ + "gbfy = make_pipeline(transformer, GradientBoostingRegressor(max_depth=2, n_iter_no_change=5))\n", + "gbfd = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", + "gbfz = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", + "result = dml(X, Z, D, y, gbfy, gbfd, gbfz, nfolds=3, classifier=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51", + "metadata": { + "id": "51" + }, + "outputs": [], + "source": [ + "table = pd.concat([table, summary(*result, X, Z, D, y, name='boosted forest')])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "52", + "metadata": { + "id": "52" + }, + "source": [ + "## Semi-Crossfitting and AutoML" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53", + "metadata": { + "id": "53" + }, + "outputs": [], + "source": [ + "!pip install flaml" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54", + "metadata": { + "id": "54" + }, + "outputs": [], + "source": [ + "from flaml import AutoML\n", + "\n", + "flamly = make_pipeline(transformer, AutoML(time_budget=100, task='regression', early_stop=True,\n", + " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", + "flamld = make_pipeline(transformer, AutoML(time_budget=100, task='classification', early_stop=True,\n", + " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", + "flamlz = make_pipeline(transformer, AutoML(time_budget=100, task='classification', early_stop=True,\n", + " eval_method='cv', n_splits=3, metric='r2', verbose=0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55", + "metadata": { + "id": "55" + }, + "outputs": [], + "source": [ + "flamly.fit(X, y)\n", + "besty = make_pipeline(transformer, clone(flamly[-1].best_model_for_estimator(flamly[-1].best_estimator)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56", + "metadata": { + "id": "56" + }, + "outputs": [], + "source": [ + "flamld.fit(X, D)\n", + "bestd = make_pipeline(transformer, clone(flamld[-1].best_model_for_estimator(flamld[-1].best_estimator)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57", + "metadata": { + "id": "57" + }, + "outputs": [], + "source": [ + "flamlz.fit(X, Z)\n", + "bestz = make_pipeline(transformer, clone(flamlz[-1].best_model_for_estimator(flamlz[-1].best_estimator)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58", + "metadata": { + "id": "58" + }, + "outputs": [], + "source": [ + "result = dml(X, Z, D, y, besty, bestd, bestz, nfolds=3, classifier=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59", + "metadata": { + "id": "59" + }, + "outputs": [], + "source": [ + "table = pd.concat([table, summary(*result, X, Z, D, y, name='automl (semi-cfit)')])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "60", + "metadata": { + "id": "60" + }, + "source": [ + "## Inference Robust to Weak Identification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61", + "metadata": { + "id": "61" + }, + "outputs": [], + "source": [ + "def robust_inference(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon, X, Z, D, y, *, grid, alpha=0.05):\n", + " '''\n", + " Inference in the partially linear IV model that is robust to weak identification.\n", + " grid: grid of theta values to search over when trying to identify the confidence region\n", + " alpha: confidence level\n", + " '''\n", + " n = X.shape[0]\n", + " thr = scipy.stats.chi2.ppf(1 - alpha, df=1)\n", + " accept = []\n", + " for theta in grid:\n", + " moment = (resy - theta * resD) * resZ\n", + " test = n * np.mean(moment)**2 / np.var(moment)\n", + " if test <= thr:\n", + " accept.append(theta)\n", + " return accept" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62", + "metadata": { + "id": "62" + }, + "outputs": [], + "source": [ + "region = robust_inference(*result, X, Z, D, y, grid=np.linspace(0, 20000, 10000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63", + "metadata": { + "id": "63" + }, + "outputs": [], + "source": [ + "np.min(region), np.max(region)" + ] + }, + { + "cell_type": "markdown", + "id": "64", + "metadata": { + "id": "64" + }, + "source": [ + "We find that the robust inference confidence region is almost identical to the normal based inference. We are most probably in the strong instrument regime. We can check the t-statistic for the effect of the instrument on the treatment, to verify that" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65", + "metadata": { + "id": "65" + }, + "outputs": [], + "source": [ + "beta = np.mean(resZ * resD) / np.mean(resZ**2)\n", + "var_beta = np.mean((resD - beta * resZ)**2 * resZ**2) / np.mean(resZ**2)**2\n", + "se_beta = np.sqrt(var_beta / resD.shape[0])\n", + "print(np.abs(beta) / se_beta)" + ] + }, + { + "cell_type": "markdown", + "id": "66", + "metadata": { + "id": "66" + }, + "source": [ + "Since the $t$-statistic is very large, the normal based approximation and confidence intervals should be fine. We can also get this $t$-statistic by simply using the statsmodels package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67", + "metadata": { + "id": "67" + }, + "outputs": [], + "source": [ + "from statsmodels.api import OLS\n", + "OLS(endog=resD, exog=resZ, hasconst=False).fit(cov_type='HC0').summary()" + ] + }, + { + "cell_type": "markdown", + "id": "68", + "metadata": { + "id": "68" + }, + "source": [ + "# Interactive IV Model and LATE" + ] + }, + { + "cell_type": "markdown", + "id": "69", + "metadata": { + "id": "69" + }, + "source": [ + "Now, we consider estimation of local average treatment effects (LATE) of participation `p401`, with the binary instrument `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. Here the structural equation model is:\n", + "\\begin{eqnarray}\n", + "Y &:=& f_Y (D, X, A, \\epsilon_Y) \\\\\n", + "D &:= & f_D(Z, X, A, \\epsilon_D) \\in \\{0,1\\}, \\\\\n", + "Z &:= & f_Z(X,\\epsilon_Z) \\in \\{0,1\\}, \\\\\n", + "X &:=& \\epsilon_X, \\quad A = \\epsilon_A,\n", + "\\end{eqnarray}\n", + "where $\\epsilon$'s are all exogenous and independent,\n", + "and\n", + "$$\n", + "z \\mapsto f_D(z , A, X, \\epsilon_D) \\text{ is weakly increasing (weakly monotone)}.\n", + "$$\n", + "and $A$ is a vector of unobserved confounders. Note that in our setting monotonicity is satisfied, since participation is only feasible when it is eligible. Thus we have that $D=0$ whenever $Z=0$. Thus it can only be that $f_D(1, A, X, \\epsilon_D) \\geq 0 = f_D(0, A, X, \\epsilon_D)$.\n", + "\n", + "In this case, we can estimate the local average treatment effect (LATE):\n", + "$$\n", + "\\alpha = E[Y(1) - Y(0) | D(1) > D(0)]\n", + "$$\n", + "This can be identified using the Neyman orthogonal moment equation:\n", + "\\begin{align}\n", + "E\\left[g(1, X) - g(0, X) + H(Z) (Y - g(Z, X)) - \\alpha \\cdot (m(1, X) - m(0, X) + H(Z) (D - m(Z, X))\\right] = 0\n", + "\\end{align}\n", + "where\n", + "\\begin{align}\n", + "g(Z,X) =~& E[Y|Z,X],\\\\\n", + "m(Z,X) =~& E[D|Z,X],\\\\\n", + "H(Z) =~& \\frac{Z}{Pr(Z=1|X)} - \\frac{1 - Z}{1 - Pr(Z=1|X)}\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70", + "metadata": { + "id": "70" + }, + "outputs": [], + "source": [ + "def iiv(X, Z, D, y, modely0, modely1, modeld1, modeld0, modelz, *, trimming=0.01, nfolds):\n", + " '''\n", + " DML for the Interactive IV Model setting with cross-fitting\n", + "\n", + " Input\n", + " -----\n", + " X: the controls\n", + " D: the treatment\n", + " y: the outcome\n", + " modely0: the ML model for predicting the outcome y in the Z=0 population\n", + " modely1: the ML model for predicting the outcome y in the Z=1 population\n", + " modeld0: the ML model for predicting the treatment D in the Z=0 population\n", + " modeld1: the ML model for predicting the treatment D in the Z=1 population\n", + " modelz: the ML model for predicting the instrument Z\n", + " trimming: threshold below which to trim propensities\n", + " nfolds: the number of folds in cross-fitting\n", + "\n", + " Output\n", + " ------\n", + " point: the point estimate of the treatment effect of D on y\n", + " stderr: the standard error of the treatment effect\n", + " yhat: the cross-fitted predictions for the outcome y\n", + " Dhat: the cross-fitted predictions for the outcome D\n", + " resy: the outcome residuals\n", + " resD: the treatment residuals\n", + " drhat: the doubly robust quantity for each sample\n", + " '''\n", + " cv = KFold(n_splits=nfolds, shuffle=True, random_state=123)\n", + "\n", + " yhat0, yhat1 = np.zeros(y.shape), np.zeros(y.shape)\n", + " Dhat0, Dhat1 = np.zeros(D.shape), np.zeros(D.shape)\n", + "\n", + " # we will fit a model E[Y| D, X] by fitting a separate model for D==0\n", + " # and a separate model for D==1.\n", + " for train, test in cv.split(X, y):\n", + " # train an outcome model on training data that received Z=0 and predict outcome on all data in test set\n", + " yhat0[test] = clone(modely0).fit(X.iloc[train][Z[train] == 0], y[train][Z[train] == 0]).predict(X.iloc[test])\n", + " # train an outcome model on training data that received Z=1 and predict outcome on all data in test set\n", + " yhat1[test] = clone(modely1).fit(X.iloc[train][Z[train] == 1], y[train][Z[train] == 1]).predict(X.iloc[test])\n", + " # train a treatment model on training data that received Z=0 and predict treatment on all data in test set\n", + " if np.mean(D[train][Z[train] == 0]) > 0: # it could be that D=0, whenever Z=0 deterministically\n", + " modeld0_ = clone(modeld0).fit(X.iloc[train][Z[train] == 0], D[train][Z[train] == 0])\n", + " Dhat0[test] = modeld0_.predict_proba(X.iloc[test])[:, 1]\n", + " # train a treamtent model on training data that received Z=1 and predict treatment on all data in test set\n", + " if np.mean(D[train][Z[train] == 1]) < 1: # it could be that D=1, whenever Z=1 deterministically\n", + " modeld1_ = clone(modeld1).fit(X.iloc[train][Z[train] == 1], D[train][Z[train] == 1])\n", + " Dhat1[test] = modeld1_.predict_proba(X.iloc[test])[:, 1]\n", + " else:\n", + " Dhat1[test] = 1\n", + "\n", + " # prediction of treatment and outcome for observed instrument\n", + " yhat = yhat0 * (1 - Z) + yhat1 * Z\n", + " Dhat = Dhat0 * (1 - Z) + Dhat1 * Z\n", + " # propensity scores\n", + " Zhat = cross_val_predict(modelz, X, Z, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", + " Zhat = np.clip(Zhat, trimming, 1 - trimming)\n", + " # doubly robust quantity for every sample\n", + " HZ = Z / Zhat - (1 - Z) / (1 - Zhat)\n", + " drZ = yhat1 - yhat0 + (y - yhat) * HZ\n", + " drD = Dhat1 - Dhat0 + (D - Dhat) * HZ\n", + " point = np.mean(drZ) / np.mean(drD)\n", + " psi = drZ - point * drD\n", + " Jhat = np.mean(drD)\n", + " var = np.mean(psi**2) / Jhat**2\n", + " stderr = np.sqrt(var / X.shape[0])\n", + " return point, stderr, yhat, Dhat, Zhat, y - yhat, D - Dhat, Z - Zhat, drZ, drD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71", + "metadata": { + "id": "71" + }, + "outputs": [], + "source": [ + "def summary(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, drZ, drD, X, Z, D, y, *, name):\n", + " '''\n", + " Convenience summary function that takes the results of the DML function\n", + " and summarizes several estimation quantities and performance metrics.\n", + " '''\n", + " return pd.DataFrame({'estimate': point, # point estimate\n", + " 'stderr': stderr, # standard error\n", + " 'lower': point - 1.96 * stderr, # lower end of 95% confidence interval\n", + " 'upper': point + 1.96 * stderr, # upper end of 95% confidence interval\n", + " 'rmse y': np.sqrt(np.mean(resy**2)), # RMSE of model that predicts outcome y\n", + " 'rmse D': np.sqrt(np.mean(resD**2)), # RMSE of model that predicts treatment D\n", + " 'rmse Z': np.sqrt(np.mean(resZ**2)), # RMSE of model that predicts treatment D\n", + " 'accuracy D': np.mean(np.abs(resD) < .5), # binary classification accuracy of model for D\n", + " 'accuracy Z': np.mean(np.abs(resZ) < .5), # binary classification accuracy of model for Z\n", + " }, index=[name])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72", + "metadata": { + "id": "72" + }, + "outputs": [], + "source": [ + "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", + "lassoy = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", + "lgrd = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", + "lgrz = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", + "result = iiv(X, Z, D, y, lassoy, lassoy, lgrd, lgrd, lgrz, nfolds=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73", + "metadata": { + "id": "73" + }, + "outputs": [], + "source": [ + "tableiiv = summary(*result, X, Z, D, y, name='lasso/logistic')\n", + "tableiiv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74", + "metadata": { + "id": "74" + }, + "outputs": [], + "source": [ + "rfy = make_pipeline(transformer, RandomForestRegressor(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", + "rfd = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", + "rfz = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", + "result = iiv(X, Z, D, y, rfy, rfy, rfd, rfd, rfz, nfolds=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75", + "metadata": { + "id": "75" + }, + "outputs": [], + "source": [ + "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='random forest')])\n", + "tableiiv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76", + "metadata": { + "id": "76" + }, + "outputs": [], + "source": [ + "dtry = make_pipeline(transformer, DecisionTreeRegressor(min_samples_leaf=10, ccp_alpha=.001))\n", + "dtrd = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", + "dtrz = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", + "result = iiv(X, Z, D, y, dtry, dtry, dtrd, dtrd, dtrz, nfolds=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77", + "metadata": { + "id": "77" + }, + "outputs": [], + "source": [ + "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='decision trees')])\n", + "tableiiv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78", + "metadata": { + "id": "78" + }, + "outputs": [], + "source": [ + "gbfy = make_pipeline(transformer, GradientBoostingRegressor(max_depth=2, n_iter_no_change=5))\n", + "gbfd = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", + "gbfz = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", + "result = iiv(X, Z, D, y, gbfy, gbfy, gbfd, gbfd, gbfz, nfolds=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79", + "metadata": { + "id": "79" + }, + "outputs": [], + "source": [ + "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='boosted trees')])\n", + "tableiiv" + ] + }, + { + "cell_type": "markdown", + "id": "80", + "metadata": { + "id": "80" + }, + "source": [ + "## Semi-Crossfitting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81", + "metadata": { + "id": "81" + }, + "outputs": [], + "source": [ + "from flaml import AutoML\n", + "flamly0 = make_pipeline(transformer, AutoML(time_budget=60, task='regression', early_stop=True,\n", + " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", + "flamly1 = make_pipeline(transformer, AutoML(time_budget=60, task='regression', early_stop=True,\n", + " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", + "flamld1 = make_pipeline(transformer, AutoML(time_budget=60, task='classification', early_stop=True,\n", + " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", + "flamlz = make_pipeline(transformer, AutoML(time_budget=60, task='classification', early_stop=True,\n", + " eval_method='cv', n_splits=3, metric='r2', verbose=0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82", + "metadata": { + "id": "82" + }, + "outputs": [], + "source": [ + "flamly0.fit(X[Z == 0], y[Z == 0])\n", + "besty0 = make_pipeline(transformer, clone(flamly0[-1].best_model_for_estimator(flamly0[-1].best_estimator)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83", + "metadata": { + "id": "83" + }, + "outputs": [], + "source": [ + "flamly1.fit(X[Z == 1], y[Z == 1])\n", + "besty1 = make_pipeline(transformer, clone(flamly1[-1].best_model_for_estimator(flamly1[-1].best_estimator)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84", + "metadata": { + "id": "84" + }, + "outputs": [], + "source": [ + "from sklearn.dummy import DummyClassifier\n", + "bestd0 = DummyClassifier() # since D=0 whenever Z=0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85", + "metadata": { + "id": "85" + }, + "outputs": [], + "source": [ + "flamld1.fit(X[Z == 1], D[Z == 1])\n", + "bestd1 = make_pipeline(transformer, clone(flamld1[-1].best_model_for_estimator(flamld1[-1].best_estimator)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86", + "metadata": { + "id": "86" + }, + "outputs": [], + "source": [ + "flamlz.fit(X, Z)\n", + "bestz = make_pipeline(transformer, clone(flamlz[-1].best_model_for_estimator(flamlz[-1].best_estimator)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87", + "metadata": { + "id": "87" + }, + "outputs": [], + "source": [ + "result = iiv(X, Z, D, y, besty0, besty1, bestd1, bestd0, bestz, nfolds=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88", + "metadata": { + "id": "88" + }, + "outputs": [], + "source": [ + "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='automl (semi-cfit)')])\n", + "tableiiv" + ] + }, + { + "cell_type": "markdown", + "id": "89", + "metadata": { + "id": "89" + }, + "source": [ + "Comparing with the PLR model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90", + "metadata": { + "id": "90" + }, + "outputs": [], + "source": [ + "table" + ] + }, + { + "cell_type": "markdown", + "id": "91", + "metadata": { + "id": "91" + }, + "source": [ + "We find that the PLR model estimates of the effect are generally larger; though both sets of results have overlapping confidence intervals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92", + "metadata": { + "id": "92" + }, + "outputs": [], + "source": [ + "def iivm_robust_inference(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, drZ, drD, X, Z, D, y, *,\n", + " grid, alpha=0.05):\n", + " '''\n", + " Inference in the partially linear IV model that is robust to weak identification.\n", + " grid: grid of theta values to search over when trying to identify the confidence region\n", + " alpha: confidence level\n", + " '''\n", + " n = X.shape[0]\n", + " thr = scipy.stats.chi2.ppf(1 - alpha, df=1)\n", + " accept = []\n", + " for theta in grid:\n", + " moment = drZ - theta * drD\n", + " test = n * np.mean(moment)**2 / np.var(moment)\n", + " if test <= thr:\n", + " accept.append(theta)\n", + " return accept" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93", + "metadata": { + "id": "93" + }, + "outputs": [], + "source": [ + "region = iivm_robust_inference(*result, X, Z, D, y, grid=np.linspace(0, 20000, 10000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94", + "metadata": { + "id": "94" + }, + "outputs": [], + "source": [ + "np.min(region), np.max(region)" + ] + }, + { + "cell_type": "markdown", + "id": "95", + "metadata": { + "id": "95" + }, + "source": [ + "We find again that the robust inference confidence region is almost identical to the normal based inference. We are most probably in the strong instrument regime. We can check the t-statistic for the effect of the instrument on the treatment to verify this." + ] + }, + { + "cell_type": "markdown", + "id": "96", + "metadata": { + "id": "96" + }, + "source": [ + "# Using EconML\n", + "\n", + "There exist nice packages out there that can help us do our estimation with the simple call of a function. Such packages include EconML (Python) and DoubleML (Python and R). We run through PLR IV with EconML and IIVM with DoubleML to illustrate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97", + "metadata": { + "id": "97" + }, + "outputs": [], + "source": [ + "!pip install --upgrade \"econml==0.17.0\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98", + "metadata": { + "id": "98" + }, + "outputs": [], + "source": [ + "W = StandardScaler().fit_transform(transformer.fit_transform(X))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99", + "metadata": { + "id": "99" + }, + "outputs": [], + "source": [ + "from econml.iv.dml import OrthoIV\n", + "\n", + "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", + "plriv = OrthoIV(model_y_xw=LassoCV(cv=cv),\n", + " model_t_xw=LogisticRegressionCV(cv=cv),\n", + " model_z_xw=LogisticRegressionCV(cv=cv),\n", + " cv=3, discrete_treatment=True, discrete_instrument=True, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "100", + "metadata": { + "id": "100" + }, + "outputs": [], + "source": [ + "plriv.fit(y, D, Z=Z, W=W)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "101", + "metadata": { + "id": "101" + }, + "outputs": [], + "source": [ + "plriv.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "102", + "metadata": { + "id": "102" + }, + "source": [ + "EconML does not yet support LATE estimation under the fully interactive IV model.\n", + "\n", + "It does support however a more general IV model where the variables `X` are allowed to alter the effect heterogeneity and the compliance heterogeneity, but the un-observed confounder is not allowed to jointly alter both. In other words, it assumes a structural equation model of the form:\n", + "\\begin{eqnarray*}\n", + "Y & := & g_Y(X, \\epsilon_Y) D + f_Y(A, X, \\epsilon_Y), \\\\\n", + "D & := & f_D(Z, X, A, \\epsilon_D), \\\\\n", + "Z & := & f_Z(X, \\epsilon_Z),\\\\\n", + "A & : = & f_A(X, \\epsilon_A), \\\\\n", + "X & := & \\epsilon_X,\n", + "\\end{eqnarray*}\n", + "where $A$ is a vector of un-observed confounders. Under these assumptions the average treatment effect is identifiable (not just the local average treatment effect). In particular, the average treatment effect can be identified as:\n", + "\\begin{align}\n", + "\\alpha := E[Y(1) - Y(0)] = E\\left[ \\frac{Cov(Y, Z\\mid X)}{Cov(D, Z\\mid X)} \\right] = E\\left[ \\frac{E[\\tilde{Y} \\tilde{Z}\\mid X]}{E[\\tilde{Z} \\tilde{D}\\mid X]} \\right]\n", + "\\end{align}\n", + "where for any variable $V$ we have $\\tilde{V}=V-E[V|X]$.\n", + "\n", + "However, the variance of this method can be quite larger than the LATE method, since it uses a local compliance measure, i.e. $E[\\tilde{Z} \\tilde{D}\\mid X]$, which can be small for some regions of $X$. This extra variance stems from the fact that we are going after a more challenging causal quantity which is the average treatment effect, instead of the local average treatment effect, and hence we need to re-weight the data based on compliance levels, conditional on observable covariates $X$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "103", + "metadata": { + "id": "103" + }, + "outputs": [], + "source": [ + "from econml.iv.dr import LinearDRIV\n", + "\n", + "driv = LinearDRIV(model_y_xw=LassoCV(cv=cv),\n", + " model_t_xw=LogisticRegressionCV(cv=cv),\n", + " model_t_xwz=LogisticRegressionCV(cv=cv),\n", + " model_tz_xw=LassoCV(cv=cv),\n", + " flexible_model_effect=LassoCV(cv=cv),\n", + " projection=True,\n", + " discrete_instrument=True, discrete_treatment=True, cv=3, cov_clip=0.01, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "104", + "metadata": { + "id": "104" + }, + "outputs": [], + "source": [ + "driv.fit(y, D, Z=Z, W=W)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "105", + "metadata": { + "id": "105" + }, + "outputs": [], + "source": [ + "driv.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "106", + "metadata": { + "id": "106" + }, + "source": [ + "# Using the DoubleML Package\n", + "\n", + "We run through IIVM using `DoubleML` below to illustrate. The `DoubleML` package internally builds on `mlr3`. We use the meta package `mlr3` to generate predictions with machine learning methods. A comprehensive introduction and description of the `mlr3` package is provided in the [mlr3book](https://mlr3book.mlr-org.com/). A list of all learners that you can use in `mlr3` can be found [here](https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html). The entry in the columns *mlr3 Package* and *Packages* indicate which packages must be installed/loaded in your R session.\n", + "\n", + "You find additional information about `DoubleML` on the package on the package website https://docs.doubleml.org/ and the R documentation page https://docs.doubleml.org/r/stable/." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "107", + "metadata": { + "id": "107" + }, + "outputs": [], + "source": [ + "!pip install doubleml~=0.7.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "108", + "metadata": { + "id": "108" + }, + "outputs": [], + "source": [ + "from doubleml import DoubleMLData\n", + "dml_data = DoubleMLData.from_arrays(W, y, D, z=Z)\n", + "print(dml_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "109", + "metadata": { + "id": "109" + }, + "outputs": [], + "source": [ + "import doubleml as dml\n", + "\n", + "\n", + "class RegWrapper(BaseEstimator):\n", + "\n", + " def __init__(self, clf):\n", + " self.clf = clf\n", + "\n", + " def fit(self, X, y):\n", + " self.clf_ = clone(self.clf).fit(X, y)\n", + " return self\n", + "\n", + " def predict(self, X):\n", + " return self.clf_.predict_proba(X)[:, 1]\n", + "\n", + "\n", + "dml_plr_obj = dml.DoubleMLPLIV(dml_data,\n", + " LassoCV(cv=cv),\n", + " RegWrapper(LogisticRegressionCV(cv=cv)),\n", + " RegWrapper(LogisticRegressionCV(cv=cv)),\n", + " n_folds=3)\n", + "print(dml_plr_obj.fit())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "110", + "metadata": { + "id": "110" + }, + "outputs": [], + "source": [ + "import doubleml as dml\n", + "\n", + "\n", + "class Wrapper(BaseEstimator):\n", + "\n", + " def __init__(self, clf):\n", + " self.clf = clf\n", + "\n", + " def fit(self, X, y):\n", + " if np.mean(y) == 0 or np.mean(y) == 1:\n", + " self.clf_ = np.mean(y)\n", + " else:\n", + " self.clf_ = clone(self.clf).fit(X, y)\n", + " self.classes_ = np.array([0, 1])\n", + " return self\n", + "\n", + " def predict_proba(self, X):\n", + " probs = np.zeros((X.shape[0], 2))\n", + " if self.clf_ == 0:\n", + " probs[:, 0] = 1\n", + " return probs\n", + " if self.clf_ == 1:\n", + " probs[:, 1] = 1\n", + " return probs\n", + " return self.clf_.predict_proba(X)\n", + "\n", + " def predict(self, X):\n", + " return self.predict_proba(X)[:, 1] >= .5\n", + "\n", + "\n", + "dml_plr_obj = dml.DoubleMLIIVM(dml_data,\n", + " LassoCV(cv=cv),\n", + " Wrapper(LogisticRegressionCV(cv=cv)),\n", + " Wrapper(LogisticRegressionCV(cv=cv)),\n", + " n_folds=3)\n", + "print(dml_plr_obj.fit())" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, "papermill": { - "duration": 0.041678, - "end_time": "2021-03-24T14:13:54.640532", - "exception": false, - "start_time": "2021-03-24T14:13:54.598854", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "As discussed, these estimates are biased since they do not account for saver heterogeneity and endogeneity of participation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19", - "metadata": { - "id": "a9354414" - }, - "outputs": [], - "source": [ - "y = data['net_tfa'].values\n", - "Z = data['e401'].values\n", - "D = data['p401'].values\n", - "X = data.drop(['e401', 'p401', 'a401', 'tw', 'tfa', 'net_tfa', 'tfa_he',\n", - " 'hval', 'hmort', 'hequity',\n", - " 'nifa', 'net_nifa', 'net_n401', 'ira',\n", - " 'dum91', 'icat', 'ecat', 'zhat',\n", - " 'i1', 'i2', 'i3', 'i4', 'i5', 'i6', 'i7',\n", - " 'a1', 'a2', 'a3', 'a4', 'a5'], axis=1)\n", - "X.columns" - ] - }, - { - "cell_type": "markdown", - "id": "20", - "metadata": { - "id": "c4c3e489" - }, - "source": [ - "### We define a transformer that constructs the engineered features for controls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21", - "metadata": { - "id": "a2cfdf4c" - }, - "outputs": [], - "source": [ - "!pip install formulaic" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22", - "metadata": { - "id": "53b1283d" - }, - "outputs": [], - "source": [ - "from sklearn.base import TransformerMixin\n", - "from formulaic import Formula\n", - "\n", - "\n", - "class FormulaTransformer(TransformerMixin, BaseEstimator):\n", - "\n", - " def __init__(self, formula, array=False):\n", - " self.formula = formula\n", - " self.array = array\n", - "\n", - " def fit(self, X, y=None):\n", - " return self\n", - "\n", - " def transform(self, X, y=None):\n", - " df = Formula(self.formula).get_model_matrix(X)\n", - " if self.array:\n", - " return df.values\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23", - "metadata": { - "id": "824ee320" - }, - "outputs": [], - "source": [ - "transformer = FormulaTransformer(\"0 + poly(age, degree=6, raw=True) + poly(inc, degree=8, raw=True) \"\n", - " \"+ poly(educ, degree=4, raw=True) + poly(fsize, degree=2, raw=True) \"\n", - " \"+ male + marr + twoearn + db + pira + hown\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24", - "metadata": { - "id": "b35bbd34" - }, - "outputs": [], - "source": [ - "transformer.fit_transform(X).head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25", - "metadata": { - "id": "8c5e20e7" - }, - "outputs": [], - "source": [ - "transformer = FormulaTransformer(\"0 + poly(age, degree=6, raw=True) + poly(inc, degree=8, raw=True) \"\n", - " \"+ poly(educ, degree=4, raw=True) + poly(fsize, degree=2, raw=True) \"\n", - " \"+ male + marr + twoearn + db + pira + hown\", array=True)" - ] - }, - { - "cell_type": "markdown", - "id": "26", - "metadata": { - "id": "de8e13e7" - }, - "source": [ - "# Instrumental Variables: Effect of 401k Participation on Financial Assets" - ] - }, - { - "cell_type": "markdown", - "id": "27", - "metadata": { - "id": "61e02e77" - }, - "source": [ - "## Double ML IV under Partial Linearity" - ] - }, - { - "cell_type": "markdown", - "id": "28", - "metadata": { - "id": "83869c99" - }, - "source": [ - "Now, we consider estimation of average treatment effects of participation in 401k, i.e. `p401`, with the binary instrument being eligibility in 401k, i.e. `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. We consider a partially linear structural equation model:\n", - "\\begin{eqnarray*}\n", - "Y & := & g_Y(\\epsilon_Y) D + f_Y(A, X, \\epsilon_Y), \\\\\n", - "D & := & f_D(Z, X, A, \\epsilon_D), \\\\\n", - "Z & := & f_Z(X, \\epsilon_Z),\\\\\n", - "A & : = & f_A(X, \\epsilon_A), \\\\\n", - "X & := & \\epsilon_X,\n", - "\\end{eqnarray*}\n", - "where $A$ is a vector of un-observed confounders.\n", - "\n", - "Under this structural equation model, the average treatment effect:\n", - "\\begin{align}\n", - "\\alpha = E[Y(1) - Y(0)]\n", - "\\end{align}\n", - "can be identified by the moment restriction:\n", - "\\begin{align}\n", - "E[(\\tilde{Y} - \\alpha \\tilde{D}) \\tilde{Z}] = 0\n", - "\\end{align}\n", - "where for any variable $V$, we denote with $\\tilde{V} = V - E[V|X]$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29", - "metadata": { - "id": "0d743ebe" - }, - "outputs": [], - "source": [ - "modely = make_pipeline(transformer, StandardScaler(), LassoCV())\n", - "modeld = make_pipeline(transformer, StandardScaler(), LassoCV())\n", - "modelz = make_pipeline(transformer, StandardScaler(), LassoCV())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30", - "metadata": { - "id": "e7bd792d" - }, - "outputs": [], - "source": [ - "resy = y - modely.fit(X, y).predict(X)\n", - "resZ = Z - modelz.fit(X, Z).predict(X) # instrument is e401k (eligibility)\n", - "resD = D - modeld.fit(X, D).predict(X) # treatment is p401k (participation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31", - "metadata": { - "id": "7ebedcb0" - }, - "outputs": [], - "source": [ - "np.mean(resy * resZ) / np.mean(resD * resZ)" - ] - }, - { - "cell_type": "markdown", - "id": "32", - "metadata": { - "id": "cH3YkvuSZfP0" - }, - "source": [ - "Recall if we want to do inference, we need to either use the theoretically driven penalty parameter for Lasso or perform cross-fitting." - ] - }, - { - "cell_type": "markdown", - "id": "33", - "metadata": { - "id": "b218c25e" - }, - "source": [ - "### DML with Non-Linear ML Models and Cross-fitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34", - "metadata": { - "id": "333c19ed" - }, - "outputs": [], - "source": [ - "def dml(X, Z, D, y, modely, modeld, modelz, *, nfolds, classifier=False):\n", - " '''\n", - " DML for the Partially Linear Model setting with cross-fitting\n", - "\n", - " Input\n", - " -----\n", - " X: the controls\n", - " Z: the instrument\n", - " D: the treatment\n", - " y: the outcome\n", - " modely: the ML model for predicting the outcome y\n", - " modeld: the ML model for predicting the treatment D\n", - " modelz: the ML model for predicting the instrument Z\n", - " nfolds: the number of folds in cross-fitting\n", - " classifier: bool, whether the modeld is a classifier or a regressor\n", - "\n", - " Output\n", - " ------\n", - " point: the point estimate of the treatment effect of D on y\n", - " stderr: the standard error of the treatment effect\n", - " yhat: the cross-fitted predictions for the outcome y\n", - " Dhat: the cross-fitted predictions for the treatment D\n", - " Zhat: the cross-fitted predictions for the instrument Z\n", - " resy: the outcome residuals\n", - " resD: the treatment residuals\n", - " resZ: the instrument residuals\n", - " epsilon: the final residual-on-residual OLS regression residual\n", - " '''\n", - " cv = KFold(n_splits=nfolds, shuffle=True, random_state=123) # shuffled k-folds\n", - " yhat = cross_val_predict(modely, X, y, cv=cv, n_jobs=-1) # out-of-fold predictions for y\n", - " # out-of-fold predictions for D\n", - " # use predict or predict_proba dependent on classifier or regressor for D\n", - " if classifier:\n", - " Dhat = cross_val_predict(modeld, X, D, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", - " Zhat = cross_val_predict(modelz, X, Z, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", - " else:\n", - " Dhat = cross_val_predict(modeld, X, D, cv=cv, n_jobs=-1)\n", - " Zhat = cross_val_predict(modelz, X, Z, cv=cv, n_jobs=-1)\n", - " # calculate outcome and treatment residuals\n", - " resy = y - yhat\n", - " resD = D - Dhat\n", - " resZ = Z - Zhat\n", - " # final stage ols based point estimate and standard error\n", - " point = np.mean(resy * resZ) / np.mean(resD * resZ)\n", - " epsilon = resy - point * resD\n", - " var = np.mean(epsilon**2 * resZ**2) / np.mean(resD * resZ)**2\n", - " stderr = np.sqrt(var / X.shape[0])\n", - " return point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35", - "metadata": { - "id": "7d01b969" - }, - "outputs": [], - "source": [ - "def summary(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon, X, Z, D, y, *, name):\n", - " '''\n", - " Convenience summary function that takes the results of the DML function\n", - " and summarizes several estimation quantities and performance metrics.\n", - " '''\n", - " return pd.DataFrame({'estimate': point, # point estimate\n", - " 'stderr': stderr, # standard error\n", - " 'lower': point - 1.96 * stderr, # lower end of 95% confidence interval\n", - " 'upper': point + 1.96 * stderr, # upper end of 95% confidence interval\n", - " 'rmse y': np.sqrt(np.mean(resy**2)), # RMSE of model that predicts outcome y\n", - " 'rmse D': np.sqrt(np.mean(resD**2)), # RMSE of model that predicts treatment D\n", - " 'rmse Z': np.sqrt(np.mean(resZ**2)), # RMSE of model that predicts treatment D\n", - " 'accuracy D': np.mean(np.abs(resD) < .5), # binary classification accuracy of model for D\n", - " 'accuracy Z': np.mean(np.abs(resZ) < .5), # binary classification accuracy of model for Z\n", - " }, index=[name])" - ] - }, - { - "cell_type": "markdown", - "id": "36", - "metadata": { - "id": "50821e2a" - }, - "source": [ - "#### Double Lasso with Cross-Fitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37", - "metadata": { - "id": "3245907f" - }, - "outputs": [], - "source": [ - "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", - "lassoy = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", - "lassod = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", - "lassoz = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", - "result = dml(X, Z, D, y, lassoy, lassod, lassoz, nfolds=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38", - "metadata": { - "id": "cb8e6883" - }, - "outputs": [], - "source": [ - "table = summary(*result, X, Z, D, y, name='double lasso')\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "39", - "metadata": { - "id": "30317504" - }, - "source": [ - "#### Using a Penalized Logistic Regression for D" - ] - }, - { - "cell_type": "markdown", - "id": "40", - "metadata": { - "id": "TCnwbZAk3hPd" - }, - "source": [ - "Note the default logistic regression uses an ℓ2 penalty. You can use the ℓ1 penalty as well, but computation will take longer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41", - "metadata": { - "id": "24237b0c" - }, - "outputs": [], - "source": [ - "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", - "lassoy = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", - "lgrd = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", - "lgrz = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", - "result = dml(X, Z, D, y, lassoy, lgrd, lgrz, nfolds=3, classifier=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42", - "metadata": { - "id": "de68f123" - }, - "outputs": [], - "source": [ - "table = pd.concat([table, summary(*result, X, Z, D, y, name='lasso/logistic')])\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "43", - "metadata": { - "id": "1b45a835" - }, - "source": [ - "### Random Forests" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44", - "metadata": { - "id": "4007ff4e" - }, - "outputs": [], - "source": [ - "rfy = make_pipeline(transformer, RandomForestRegressor(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", - "rfd = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", - "rfz = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", - "result = dml(X, Z, D, y, rfy, rfd, rfz, nfolds=3, classifier=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45", - "metadata": { - "id": "801cf22e" - }, - "outputs": [], - "source": [ - "table = pd.concat([table, summary(*result, X, Z, D, y, name='random forest')])\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "46", - "metadata": { - "id": "f831c007" - }, - "source": [ - "### Decision Trees" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47", - "metadata": { - "id": "36d6e07a" - }, - "outputs": [], - "source": [ - "dtry = make_pipeline(transformer, DecisionTreeRegressor(min_samples_leaf=10, ccp_alpha=.001))\n", - "dtrd = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", - "dtrz = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", - "result = dml(X, Z, D, y, dtry, dtrd, dtrz, nfolds=3, classifier=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48", - "metadata": { - "id": "2cdb226d" - }, - "outputs": [], - "source": [ - "table = pd.concat([table, summary(*result, X, Z, D, y, name='decision tree')])\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "49", - "metadata": { - "id": "58ccc781" - }, - "source": [ - "### Boosted Trees" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50", - "metadata": { - "id": "cedPovUlUcJt" - }, - "outputs": [], - "source": [ - "gbfy = make_pipeline(transformer, GradientBoostingRegressor(max_depth=2, n_iter_no_change=5))\n", - "gbfd = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", - "gbfz = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", - "result = dml(X, Z, D, y, gbfy, gbfd, gbfz, nfolds=3, classifier=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51", - "metadata": { - "id": "76fd3e93" - }, - "outputs": [], - "source": [ - "table = pd.concat([table, summary(*result, X, Z, D, y, name='boosted forest')])\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "52", - "metadata": { - "id": "911a6099" - }, - "source": [ - "## Semi-Crossfitting and AutoML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53", - "metadata": { - "id": "EEaB50ly5FhE" - }, - "outputs": [], - "source": [ - "!pip install flaml" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54", - "metadata": { - "id": "32bfd161" - }, - "outputs": [], - "source": [ - "from flaml import AutoML\n", - "\n", - "flamly = make_pipeline(transformer, AutoML(time_budget=100, task='regression', early_stop=True,\n", - " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", - "flamld = make_pipeline(transformer, AutoML(time_budget=100, task='classification', early_stop=True,\n", - " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", - "flamlz = make_pipeline(transformer, AutoML(time_budget=100, task='classification', early_stop=True,\n", - " eval_method='cv', n_splits=3, metric='r2', verbose=0))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55", - "metadata": { - "id": "e6bf41b6" - }, - "outputs": [], - "source": [ - "flamly.fit(X, y)\n", - "besty = make_pipeline(transformer, clone(flamly[-1].best_model_for_estimator(flamly[-1].best_estimator)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56", - "metadata": { - "id": "0841262d" - }, - "outputs": [], - "source": [ - "flamld.fit(X, D)\n", - "bestd = make_pipeline(transformer, clone(flamld[-1].best_model_for_estimator(flamld[-1].best_estimator)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57", - "metadata": { - "id": "d0a3a5fd" - }, - "outputs": [], - "source": [ - "flamlz.fit(X, Z)\n", - "bestz = make_pipeline(transformer, clone(flamlz[-1].best_model_for_estimator(flamlz[-1].best_estimator)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58", - "metadata": { - "id": "87e10995" - }, - "outputs": [], - "source": [ - "result = dml(X, Z, D, y, besty, bestd, bestz, nfolds=3, classifier=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59", - "metadata": { - "id": "dd2fd52a" - }, - "outputs": [], - "source": [ - "table = pd.concat([table, summary(*result, X, Z, D, y, name='automl (semi-cfit)')])\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "60", - "metadata": { - "id": "a14f6004" - }, - "source": [ - "## Inference Robust to Weak Identification" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61", - "metadata": { - "id": "3622cc4d" - }, - "outputs": [], - "source": [ - "def robust_inference(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, epsilon, X, Z, D, y, *, grid, alpha=0.05):\n", - " '''\n", - " Inference in the partially linear IV model that is robust to weak identification.\n", - " grid: grid of theta values to search over when trying to identify the confidence region\n", - " alpha: confidence level\n", - " '''\n", - " n = X.shape[0]\n", - " thr = scipy.stats.chi2.ppf(1 - alpha, df=1)\n", - " accept = []\n", - " for theta in grid:\n", - " moment = (resy - theta * resD) * resZ\n", - " test = n * np.mean(moment)**2 / np.var(moment)\n", - " if test <= thr:\n", - " accept.append(theta)\n", - " return accept" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62", - "metadata": { - "id": "9f668209" - }, - "outputs": [], - "source": [ - "region = robust_inference(*result, X, Z, D, y, grid=np.linspace(0, 20000, 10000))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63", - "metadata": { - "id": "f6eb57a1" - }, - "outputs": [], - "source": [ - "np.min(region), np.max(region)" - ] - }, - { - "cell_type": "markdown", - "id": "64", - "metadata": { - "id": "8326cd85" - }, - "source": [ - "We find that the robust inference confidence region is almost identical to the normal based inference. We are most probably in the strong instrument regime. We can check the t-statistic for the effect of the instrument on the treatment, to verify that" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65", - "metadata": { - "id": "e0e7e6d9" - }, - "outputs": [], - "source": [ - "beta = np.mean(resZ * resD) / np.mean(resZ**2)\n", - "var_beta = np.mean((resD - beta * resZ)**2 * resZ**2) / np.mean(resZ**2)**2\n", - "se_beta = np.sqrt(var_beta / resD.shape[0])\n", - "print(np.abs(beta) / se_beta)" - ] - }, - { - "cell_type": "markdown", - "id": "66", - "metadata": { - "id": "74e607de" - }, - "source": [ - "Since the $t$-statistic is very large, the normal based approximation and confidence intervals should be fine. We can also get this $t$-statistic by simply using the statsmodels package" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67", - "metadata": { - "id": "7be6a610" - }, - "outputs": [], - "source": [ - "from statsmodels.api import OLS\n", - "OLS(endog=resD, exog=resZ, hasconst=False).fit(cov_type='HC0').summary()" - ] - }, - { - "cell_type": "markdown", - "id": "68", - "metadata": { - "id": "db39aa34" - }, - "source": [ - "# Interactive IV Model and LATE" - ] - }, - { - "cell_type": "markdown", - "id": "69", - "metadata": { - "id": "7255c0a8" - }, - "source": [ - "Now, we consider estimation of local average treatment effects (LATE) of participation `p401`, with the binary instrument `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. Here the structural equation model is:\n", - "\\begin{eqnarray}\n", - "Y &:=& f_Y (D, X, A, \\epsilon_Y) \\\\\n", - "D &:= & f_D(Z, X, A, \\epsilon_D) \\in \\{0,1\\}, \\\\\n", - "Z &:= & f_Z(X,\\epsilon_Z) \\in \\{0,1\\}, \\\\\n", - "X &:=& \\epsilon_X, \\quad A = \\epsilon_A,\n", - "\\end{eqnarray}\n", - "where $\\epsilon$'s are all exogenous and independent,\n", - "and\n", - "$$\n", - "z \\mapsto f_D(z , A, X, \\epsilon_D) \\text{ is weakly increasing (weakly monotone)}.\n", - "$$\n", - "and $A$ is a vector of unobserved confounders. Note that in our setting monotonicity is satisfied, since participation is only feasible when it is eligible. Thus we have that $D=0$ whenever $Z=0$. Thus it can only be that $f_D(1, A, X, \\epsilon_D) \\geq 0 = f_D(0, A, X, \\epsilon_D)$.\n", - "\n", - "In this case, we can estimate the local average treatment effect (LATE):\n", - "$$\n", - "\\alpha = E[Y(1) - Y(0) | D(1) > D(0)]\n", - "$$\n", - "This can be identified using the Neyman orthogonal moment equation:\n", - "\\begin{align}\n", - "E\\left[g(1, X) - g(0, X) + H(Z) (Y - g(Z, X)) - \\alpha \\cdot (m(1, X) - m(0, X) + H(Z) (D - m(Z, X))\\right] = 0\n", - "\\end{align}\n", - "where\n", - "\\begin{align}\n", - "g(Z,X) =~& E[Y|Z,X],\\\\\n", - "m(Z,X) =~& E[D|Z,X],\\\\\n", - "H(Z) =~& \\frac{Z}{Pr(Z=1|X)} - \\frac{1 - Z}{1 - Pr(Z=1|X)}\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70", - "metadata": { - "id": "efc66d4b" - }, - "outputs": [], - "source": [ - "def iiv(X, Z, D, y, modely0, modely1, modeld1, modeld0, modelz, *, trimming=0.01, nfolds):\n", - " '''\n", - " DML for the Interactive IV Model setting with cross-fitting\n", - "\n", - " Input\n", - " -----\n", - " X: the controls\n", - " D: the treatment\n", - " y: the outcome\n", - " modely0: the ML model for predicting the outcome y in the Z=0 population\n", - " modely1: the ML model for predicting the outcome y in the Z=1 population\n", - " modeld0: the ML model for predicting the treatment D in the Z=0 population\n", - " modeld1: the ML model for predicting the treatment D in the Z=1 population\n", - " modelz: the ML model for predicting the instrument Z\n", - " trimming: threshold below which to trim propensities\n", - " nfolds: the number of folds in cross-fitting\n", - "\n", - " Output\n", - " ------\n", - " point: the point estimate of the treatment effect of D on y\n", - " stderr: the standard error of the treatment effect\n", - " yhat: the cross-fitted predictions for the outcome y\n", - " Dhat: the cross-fitted predictions for the outcome D\n", - " resy: the outcome residuals\n", - " resD: the treatment residuals\n", - " drhat: the doubly robust quantity for each sample\n", - " '''\n", - " cv = KFold(n_splits=nfolds, shuffle=True, random_state=123)\n", - "\n", - " yhat0, yhat1 = np.zeros(y.shape), np.zeros(y.shape)\n", - " Dhat0, Dhat1 = np.zeros(D.shape), np.zeros(D.shape)\n", - "\n", - " # we will fit a model E[Y| D, X] by fitting a separate model for D==0\n", - " # and a separate model for D==1.\n", - " for train, test in cv.split(X, y):\n", - " # train an outcome model on training data that received Z=0 and predict outcome on all data in test set\n", - " yhat0[test] = clone(modely0).fit(X.iloc[train][Z[train] == 0], y[train][Z[train] == 0]).predict(X.iloc[test])\n", - " # train an outcome model on training data that received Z=1 and predict outcome on all data in test set\n", - " yhat1[test] = clone(modely1).fit(X.iloc[train][Z[train] == 1], y[train][Z[train] == 1]).predict(X.iloc[test])\n", - " # train a treatment model on training data that received Z=0 and predict treatment on all data in test set\n", - " if np.mean(D[train][Z[train] == 0]) > 0: # it could be that D=0, whenever Z=0 deterministically\n", - " modeld0_ = clone(modeld0).fit(X.iloc[train][Z[train] == 0], D[train][Z[train] == 0])\n", - " Dhat0[test] = modeld0_.predict_proba(X.iloc[test])[:, 1]\n", - " # train a treamtent model on training data that received Z=1 and predict treatment on all data in test set\n", - " if np.mean(D[train][Z[train] == 1]) < 1: # it could be that D=1, whenever Z=1 deterministically\n", - " modeld1_ = clone(modeld1).fit(X.iloc[train][Z[train] == 1], D[train][Z[train] == 1])\n", - " Dhat1[test] = modeld1_.predict_proba(X.iloc[test])[:, 1]\n", - " else:\n", - " Dhat1[test] = 1\n", - "\n", - " # prediction of treatment and outcome for observed instrument\n", - " yhat = yhat0 * (1 - Z) + yhat1 * Z\n", - " Dhat = Dhat0 * (1 - Z) + Dhat1 * Z\n", - " # propensity scores\n", - " Zhat = cross_val_predict(modelz, X, Z, cv=cv, method='predict_proba', n_jobs=-1)[:, 1]\n", - " Zhat = np.clip(Zhat, trimming, 1 - trimming)\n", - " # doubly robust quantity for every sample\n", - " HZ = Z / Zhat - (1 - Z) / (1 - Zhat)\n", - " drZ = yhat1 - yhat0 + (y - yhat) * HZ\n", - " drD = Dhat1 - Dhat0 + (D - Dhat) * HZ\n", - " point = np.mean(drZ) / np.mean(drD)\n", - " psi = drZ - point * drD\n", - " Jhat = np.mean(drD)\n", - " var = np.mean(psi**2) / Jhat**2\n", - " stderr = np.sqrt(var / X.shape[0])\n", - " return point, stderr, yhat, Dhat, Zhat, y - yhat, D - Dhat, Z - Zhat, drZ, drD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71", - "metadata": { - "id": "7d65020c" - }, - "outputs": [], - "source": [ - "def summary(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, drZ, drD, X, Z, D, y, *, name):\n", - " '''\n", - " Convenience summary function that takes the results of the DML function\n", - " and summarizes several estimation quantities and performance metrics.\n", - " '''\n", - " return pd.DataFrame({'estimate': point, # point estimate\n", - " 'stderr': stderr, # standard error\n", - " 'lower': point - 1.96 * stderr, # lower end of 95% confidence interval\n", - " 'upper': point + 1.96 * stderr, # upper end of 95% confidence interval\n", - " 'rmse y': np.sqrt(np.mean(resy**2)), # RMSE of model that predicts outcome y\n", - " 'rmse D': np.sqrt(np.mean(resD**2)), # RMSE of model that predicts treatment D\n", - " 'rmse Z': np.sqrt(np.mean(resZ**2)), # RMSE of model that predicts treatment D\n", - " 'accuracy D': np.mean(np.abs(resD) < .5), # binary classification accuracy of model for D\n", - " 'accuracy Z': np.mean(np.abs(resZ) < .5), # binary classification accuracy of model for Z\n", - " }, index=[name])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72", - "metadata": { - "id": "7d894f58" - }, - "outputs": [], - "source": [ - "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", - "lassoy = make_pipeline(transformer, StandardScaler(), LassoCV(cv=cv))\n", - "lgrd = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", - "lgrz = make_pipeline(transformer, StandardScaler(), LogisticRegressionCV(cv=cv))\n", - "result = iiv(X, Z, D, y, lassoy, lassoy, lgrd, lgrd, lgrz, nfolds=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73", - "metadata": { - "id": "c663299a" - }, - "outputs": [], - "source": [ - "tableiiv = summary(*result, X, Z, D, y, name='lasso/logistic')\n", - "tableiiv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74", - "metadata": { - "id": "8617137d" - }, - "outputs": [], - "source": [ - "rfy = make_pipeline(transformer, RandomForestRegressor(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", - "rfd = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", - "rfz = make_pipeline(transformer, RandomForestClassifier(n_estimators=100, min_samples_leaf=10, ccp_alpha=.001))\n", - "result = iiv(X, Z, D, y, rfy, rfy, rfd, rfd, rfz, nfolds=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75", - "metadata": { - "id": "365ff0f4" - }, - "outputs": [], - "source": [ - "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='random forest')])\n", - "tableiiv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76", - "metadata": { - "id": "571112a4" - }, - "outputs": [], - "source": [ - "dtry = make_pipeline(transformer, DecisionTreeRegressor(min_samples_leaf=10, ccp_alpha=.001))\n", - "dtrd = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", - "dtrz = make_pipeline(transformer, DecisionTreeClassifier(min_samples_leaf=10, ccp_alpha=.001))\n", - "result = iiv(X, Z, D, y, dtry, dtry, dtrd, dtrd, dtrz, nfolds=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77", - "metadata": { - "id": "04228398" - }, - "outputs": [], - "source": [ - "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='decision trees')])\n", - "tableiiv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78", - "metadata": { - "id": "cba4c9e9" - }, - "outputs": [], - "source": [ - "gbfy = make_pipeline(transformer, GradientBoostingRegressor(max_depth=2, n_iter_no_change=5))\n", - "gbfd = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", - "gbfz = make_pipeline(transformer, GradientBoostingClassifier(max_depth=2, n_iter_no_change=5))\n", - "result = iiv(X, Z, D, y, gbfy, gbfy, gbfd, gbfd, gbfz, nfolds=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79", - "metadata": { - "id": "b221dba2" - }, - "outputs": [], - "source": [ - "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='boosted trees')])\n", - "tableiiv" - ] - }, - { - "cell_type": "markdown", - "id": "80", - "metadata": { - "id": "9c96e854" - }, - "source": [ - "## Semi-Crossfitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81", - "metadata": { - "id": "457b513f" - }, - "outputs": [], - "source": [ - "from flaml import AutoML\n", - "flamly0 = make_pipeline(transformer, AutoML(time_budget=60, task='regression', early_stop=True,\n", - " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", - "flamly1 = make_pipeline(transformer, AutoML(time_budget=60, task='regression', early_stop=True,\n", - " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", - "flamld1 = make_pipeline(transformer, AutoML(time_budget=60, task='classification', early_stop=True,\n", - " eval_method='cv', n_splits=3, metric='r2', verbose=0))\n", - "flamlz = make_pipeline(transformer, AutoML(time_budget=60, task='classification', early_stop=True,\n", - " eval_method='cv', n_splits=3, metric='r2', verbose=0))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82", - "metadata": { - "id": "621d086a" - }, - "outputs": [], - "source": [ - "flamly0.fit(X[Z == 0], y[Z == 0])\n", - "besty0 = make_pipeline(transformer, clone(flamly0[-1].best_model_for_estimator(flamly0[-1].best_estimator)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83", - "metadata": { - "id": "a348199a" - }, - "outputs": [], - "source": [ - "flamly1.fit(X[Z == 1], y[Z == 1])\n", - "besty1 = make_pipeline(transformer, clone(flamly1[-1].best_model_for_estimator(flamly1[-1].best_estimator)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84", - "metadata": { - "id": "4b8c3648" - }, - "outputs": [], - "source": [ - "from sklearn.dummy import DummyClassifier\n", - "bestd0 = DummyClassifier() # since D=0 whenever Z=0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85", - "metadata": { - "id": "e7326607" - }, - "outputs": [], - "source": [ - "flamld1.fit(X[Z == 1], D[Z == 1])\n", - "bestd1 = make_pipeline(transformer, clone(flamld1[-1].best_model_for_estimator(flamld1[-1].best_estimator)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86", - "metadata": { - "id": "0cad169d" - }, - "outputs": [], - "source": [ - "flamlz.fit(X, Z)\n", - "bestz = make_pipeline(transformer, clone(flamlz[-1].best_model_for_estimator(flamlz[-1].best_estimator)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87", - "metadata": { - "id": "50a9df7f" - }, - "outputs": [], - "source": [ - "result = iiv(X, Z, D, y, besty0, besty1, bestd0, bestd1, bestz, nfolds=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88", - "metadata": { - "id": "788a551b" - }, - "outputs": [], - "source": [ - "tableiiv = pd.concat([tableiiv, summary(*result, X, Z, D, y, name='automl (semi-cfit)')])\n", - "tableiiv" - ] - }, - { - "cell_type": "markdown", - "id": "89", - "metadata": { - "id": "062e99e8" - }, - "source": [ - "Comparing with the PLR model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90", - "metadata": { - "id": "7f36700c" - }, - "outputs": [], - "source": [ - "table" - ] - }, - { - "cell_type": "markdown", - "id": "91", - "metadata": { - "id": "a1ecb257" - }, - "source": [ - "We find that the PLR model overestimates the effect by around 1k; though both sets of results have overlapping confidence intervals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92", - "metadata": { - "id": "e2b38fb1" - }, - "outputs": [], - "source": [ - "def iivm_robust_inference(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ, drZ, drD, X, Z, D, y, *,\n", - " grid, alpha=0.05):\n", - " '''\n", - " Inference in the partially linear IV model that is robust to weak identification.\n", - " grid: grid of theta values to search over when trying to identify the confidence region\n", - " alpha: confidence level\n", - " '''\n", - " n = X.shape[0]\n", - " thr = scipy.stats.chi2.ppf(1 - alpha, df=1)\n", - " accept = []\n", - " for theta in grid:\n", - " moment = drZ - theta * drD\n", - " test = n * np.mean(moment)**2 / np.var(moment)\n", - " if test <= thr:\n", - " accept.append(theta)\n", - " return accept" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93", - "metadata": { - "id": "60ace0a2" - }, - "outputs": [], - "source": [ - "region = iivm_robust_inference(*result, X, Z, D, y, grid=np.linspace(0, 20000, 10000))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94", - "metadata": { - "id": "f7bb488d" - }, - "outputs": [], - "source": [ - "np.min(region), np.max(region)" - ] - }, - { - "cell_type": "markdown", - "id": "95", - "metadata": { - "id": "225cb858" - }, - "source": [ - "We find again that the robust inference confidence region is almost identical to the normal based inference. We are most probably in the strong instrument regime. We can check the t-statistic for the effect of the instrument on the treatment to verify this." - ] - }, - { - "cell_type": "markdown", - "id": "96", - "metadata": { - "id": "8cda6326" - }, - "source": [ - "# Using EconML\n", - "\n", - "There exist nice packages out there that can help us do our estimation with the simple call of a function. Such packages include EconML (Python) and DoubleML (Python and R). We run through PLR IV with EconML and IIVM with DoubleML to illustrate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97", - "metadata": { - "id": "257b9330" - }, - "outputs": [], - "source": [ - "!pip install econml==0.14.1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98", - "metadata": { - "id": "6108d89d" - }, - "outputs": [], - "source": [ - "W = StandardScaler().fit_transform(transformer.fit_transform(X))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99", - "metadata": { - "id": "49587174" - }, - "outputs": [], - "source": [ - "from econml.iv.dml import OrthoIV\n", - "\n", - "cv = KFold(n_splits=5, shuffle=True, random_state=123)\n", - "plriv = OrthoIV(model_y_xw=LassoCV(cv=cv),\n", - " model_t_xw=LogisticRegressionCV(cv=cv),\n", - " model_z_xw=LogisticRegressionCV(cv=cv),\n", - " cv=3, discrete_treatment=True, discrete_instrument=True, random_state=123)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "100", - "metadata": { - "id": "54524cc0" - }, - "outputs": [], - "source": [ - "plriv.fit(y, D, Z=Z, W=W)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "101", - "metadata": { - "id": "fa3f1497" - }, - "outputs": [], - "source": [ - "plriv.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "102", - "metadata": { - "id": "216ed55d" - }, - "source": [ - "EconML does not yet support LATE estimation under the fully interactive IV model.\n", - "\n", - "It does support however a more general IV model where the variables `X` are allowed to alter the effect heterogeneity and the compliance heterogeneity, but the un-observed confounder is not allowed to jointly alter both. In other words, it assumes a structural equation model of the form:\n", - "\\begin{eqnarray*}\n", - "Y & := & g_Y(X, \\epsilon_Y) D + f_Y(A, X, \\epsilon_Y), \\\\\n", - "D & := & f_D(Z, X, A, \\epsilon_D), \\\\\n", - "Z & := & f_Z(X, \\epsilon_Z),\\\\\n", - "A & : = & f_A(X, \\epsilon_A), \\\\\n", - "X & := & \\epsilon_X,\n", - "\\end{eqnarray*}\n", - "where $A$ is a vector of un-observed confounders. Under these assumptions the average treatment effect is identifiable (not just the local average treatment effect). In particular, the average treatment effect can be identified as:\n", - "\\begin{align}\n", - "\\alpha := E[Y(1) - Y(0)] = E\\left[ \\frac{Cov(Y, Z\\mid X)}{Cov(D, Z\\mid X)} \\right] = E\\left[ \\frac{E[\\tilde{Y} \\tilde{Z}\\mid X]}{E[\\tilde{Z} \\tilde{D}\\mid X]} \\right]\n", - "\\end{align}\n", - "where for any variable $V$ we have $\\tilde{V}=V-E[V|X]$.\n", - "\n", - "However, the variance of this method can be quite larger than the LATE method, since it uses a local compliance measure, i.e. $E[\\tilde{Z} \\tilde{D}\\mid X]$, which can be small for some regions of $X$. This extra variance stems from the fact that we are going after a more challenging causal quantity which is the average treatment effect, instead of the local average treatment effect, and hence we need to re-weight the data based on compliance levels, conditional on observable covariates $X$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "103", - "metadata": { - "id": "122ab658" - }, - "outputs": [], - "source": [ - "from econml.iv.dr import LinearDRIV\n", - "\n", - "driv = LinearDRIV(model_y_xw=LassoCV(cv=cv),\n", - " model_t_xw=LogisticRegressionCV(cv=cv),\n", - " model_t_xwz=LogisticRegressionCV(cv=cv),\n", - " model_tz_xw=LassoCV(cv=cv),\n", - " flexible_model_effect=LassoCV(cv=cv),\n", - " projection=True,\n", - " discrete_instrument=True, discrete_treatment=True, cv=3, cov_clip=0.01, random_state=123)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "104", - "metadata": { - "id": "7e878a86" - }, - "outputs": [], - "source": [ - "driv.fit(y, D, Z=Z, W=W)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "105", - "metadata": { - "id": "dd8e664a" - }, - "outputs": [], - "source": [ - "driv.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "106", - "metadata": { - "id": "a629b091" - }, - "source": [ - "# Using the DoubleML Package\n", - "\n", - "We run through IIVM using `DoubleML` below to illustrate. The `DoubleML` package internally builds on `mlr3`. We use the meta package `mlr3` to generate predictions with machine learning methods. A comprehensive introduction and description of the `mlr3` package is provided in the [mlr3book](https://mlr3book.mlr-org.com/). A list of all learners that you can use in `mlr3` can be found [here](https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html). The entry in the columns *mlr3 Package* and *Packages* indicate which packages must be installed/loaded in your R session.\n", - "\n", - "You find additional information about `DoubleML` on the package on the package website https://docs.doubleml.org/ and the R documentation page https://docs.doubleml.org/r/stable/." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "107", - "metadata": { - "id": "4f29d8e7" - }, - "outputs": [], - "source": [ - "!pip install doubleml~=0.7.0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "108", - "metadata": { - "id": "ff0bca33" - }, - "outputs": [], - "source": [ - "from doubleml import DoubleMLData\n", - "dml_data = DoubleMLData.from_arrays(W, y, D, z=Z)\n", - "print(dml_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "109", - "metadata": { - "id": "47f735fb" - }, - "outputs": [], - "source": [ - "import doubleml as dml\n", - "\n", - "\n", - "class RegWrapper(BaseEstimator):\n", - "\n", - " def __init__(self, clf):\n", - " self.clf = clf\n", - "\n", - " def fit(self, X, y):\n", - " self.clf_ = clone(self.clf).fit(X, y)\n", - " return self\n", - "\n", - " def predict(self, X):\n", - " return self.clf_.predict_proba(X)[:, 1]\n", - "\n", - "\n", - "dml_plr_obj = dml.DoubleMLPLIV(dml_data,\n", - " LassoCV(cv=cv),\n", - " RegWrapper(LogisticRegressionCV(cv=cv)),\n", - " RegWrapper(LogisticRegressionCV(cv=cv)),\n", - " n_folds=3)\n", - "print(dml_plr_obj.fit())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "110", - "metadata": { - "id": "bdd0ac4d" - }, - "outputs": [], - "source": [ - "import doubleml as dml\n", - "\n", - "\n", - "class Wrapper(BaseEstimator):\n", - "\n", - " def __init__(self, clf):\n", - " self.clf = clf\n", - "\n", - " def fit(self, X, y):\n", - " if np.mean(y) == 0 or np.mean(y) == 1:\n", - " self.clf_ = np.mean(y)\n", - " else:\n", - " self.clf_ = clone(self.clf).fit(X, y)\n", - " self.classes_ = np.array([0, 1])\n", - " return self\n", - "\n", - " def predict_proba(self, X):\n", - " probs = np.zeros((X.shape[0], 2))\n", - " if self.clf_ == 0:\n", - " probs[:, 0] = 1\n", - " return probs\n", - " if self.clf_ == 1:\n", - " probs[:, 1] = 1\n", - " return probs\n", - " return self.clf_.predict_proba(X)\n", - "\n", - " def predict(self, X):\n", - " return self.predict_proba(X)[:, 1] >= .5\n", - "\n", - "\n", - "dml_plr_obj = dml.DoubleMLIIVM(dml_data,\n", - " LassoCV(cv=cv),\n", - " Wrapper(LogisticRegressionCV(cv=cv)),\n", - " Wrapper(LogisticRegressionCV(cv=cv)),\n", - " n_folds=3)\n", - "print(dml_plr_obj.fit())" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - }, - "papermill": { - "default_parameters": {}, - "duration": 537.547458, - "end_time": "2021-03-24T14:22:44.931595", - "environment_variables": {}, - "exception": null, - "input_path": "__notebook__.ipynb", - "output_path": "__notebook__.ipynb", - "parameters": {}, - "start_time": "2021-03-24T14:13:47.384137", - "version": "2.3.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "default_parameters": {}, + "duration": 537.547458, + "end_time": "2021-03-24T14:22:44.931595", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2021-03-24T14:13:47.384137", + "version": "2.3.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/AC2/r-dml-401k-IV.irnb b/AC2/r-dml-401k-IV.irnb index 213fda6c..15a6dd6a 100644 --- a/AC2/r-dml-401k-IV.irnb +++ b/AC2/r-dml-401k-IV.irnb @@ -1,2470 +1,2484 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "0", - "metadata": { - "id": "f02fa044", - "papermill": { - "duration": 0.012988, - "end_time": "2022-04-19T09:06:48.772902", - "exception": false, - "start_time": "2022-04-19T09:06:48.759914", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Inference on Predictive and Causal Effects in High-Dimensional Nonlinear Models" - ] - }, - { - "cell_type": "markdown", - "id": "1", - "metadata": { - "id": "23154404", - "papermill": { - "duration": 0.009437, - "end_time": "2022-04-19T09:06:48.791895", - "exception": false, - "start_time": "2022-04-19T09:06:48.782458", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Impact of 401(k) on Financial Wealth\n", - "\n", - "We consider estimation of the effect of 401(k) participation\n", - "on accumulated assets. 401(k) plans are pension accounts sponsored by employers. The key problem in determining the effect of participation in 401(k) plans on accumulated assets is saver heterogeneity coupled with the fact that the decision to enroll in a 401(k) is non-random. It is generally recognized that some people have a higher preference for saving than others. It also seems likely that those individuals with high unobserved preference for saving would be most likely to choose to participate in tax-advantaged retirement savings plans and would tend to have otherwise high amounts of accumulated assets. The presence of unobserved savings preferences with these properties then implies that conventional estimates that do not account for saver heterogeneity and endogeneity of participation will be biased upward, tending to overstate the savings effects of 401(k) participation.\n", - "\n", - "One can argue that eligibility for enrolling in a 401(k) plan in this data can be taken as exogenous after conditioning on a few observables of which the most important for their argument is income. The basic idea is that, at least around the time 401(k)’s initially became available, people were unlikely to be basing their employment decisions on whether an employer offered a 401(k) but would instead focus on income and other aspects of the job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2", - "metadata": { - "id": "KmAkbDiVE7wm", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "install.packages(\"xtable\")\n", - "install.packages(\"hdm\")\n", - "install.packages(\"sandwich\")\n", - "install.packages(\"ggplot2\")\n", - "install.packages(\"randomForest\")\n", - "install.packages(\"glmnet\")\n", - "install.packages(\"rpart\")\n", - "install.packages(\"data.table\")\n", - "install.packages(\"gbm\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "library(xtable)\n", - "library(hdm)\n", - "library(sandwich)\n", - "library(ggplot2)\n", - "library(randomForest)\n", - "library(data.table)\n", - "library(glmnet)\n", - "library(rpart)\n", - "library(gbm)\n", - "\n", - "set.seed(123)" - ] - }, - { - "cell_type": "markdown", - "id": "4", - "metadata": { - "id": "7e23cba0", - "papermill": { - "duration": 0.009588, - "end_time": "2022-04-19T09:06:48.810853", - "exception": false, - "start_time": "2022-04-19T09:06:48.801265", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Data\n", - "\n", - "The raw dataset can be found [here](https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/401k.csv).\n", - "The data set can be loaded from the `hdm` package for R directly by typing:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5", - "metadata": { - "id": "c442abdc", - "papermill": { - "duration": 0.46397, - "end_time": "2022-04-19T09:06:49.283933", - "exception": false, - "start_time": "2022-04-19T09:06:48.819963", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "data(pension)\n", - "data <- pension\n", - "dim(data)" - ] - }, - { - "cell_type": "markdown", - "id": "6", - "metadata": { - "id": "e47fa9d3", - "papermill": { - "duration": 0.009462, - "end_time": "2022-04-19T09:06:49.302928", - "exception": false, - "start_time": "2022-04-19T09:06:49.293466", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "See the \"Details\" section on the description of the data set, which can be accessed by\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7", - "metadata": { - "id": "00e04b82", - "papermill": { - "duration": 0.35227, - "end_time": "2022-04-19T09:06:49.664810", - "exception": false, - "start_time": "2022-04-19T09:06:49.312540", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "help(pension)" - ] - }, - { - "cell_type": "markdown", - "id": "8", - "metadata": { - "id": "24b41e4a", - "papermill": { - "duration": 0.009357, - "end_time": "2022-04-19T09:06:49.683784", - "exception": false, - "start_time": "2022-04-19T09:06:49.674427", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "The data consist of 9,915 observations at the household level drawn from the 1991 Survey of Income and Program Participation (SIPP). All the variables are referred to 1990. We use net financial assets (*net\\_tfa*) as the outcome variable, $Y$, in our analysis. The net financial assets are computed as the sum of IRA balances, 401(k) balances, checking accounts, saving bonds, other interest-earning accounts, other interest-earning assets, stocks, and mutual funds less non mortgage debts." - ] - }, - { - "cell_type": "markdown", - "id": "9", - "metadata": { - "id": "ed9d4e82", - "papermill": { - "duration": 0.009242, - "end_time": "2022-04-19T09:06:49.702401", - "exception": false, - "start_time": "2022-04-19T09:06:49.693159", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Among the $9915$ individuals, $3682$ are eligible to participate in the program. The variable *e401* indicates eligibility and *p401* indicates participation, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10", - "metadata": { - "id": "63519184", - "papermill": { - "duration": 0.618528, - "end_time": "2022-04-19T09:06:50.330218", - "exception": false, - "start_time": "2022-04-19T09:06:49.711690", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "hist_e401 <- ggplot(data, aes(x = e401, fill = factor(e401))) +\n", - " geom_bar()\n", - "hist_e401" - ] - }, - { - "cell_type": "markdown", - "id": "11", - "metadata": { - "id": "823d2628", - "papermill": { - "duration": 0.009686, - "end_time": "2022-04-19T09:06:50.349766", - "exception": false, - "start_time": "2022-04-19T09:06:50.340080", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Eligibility is highly associated with financial wealth:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12", - "metadata": { - "id": "5d8faf9c", - "papermill": { - "duration": 0.554613, - "end_time": "2022-04-19T09:06:50.914133", - "exception": false, - "start_time": "2022-04-19T09:06:50.359520", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "dens_net_tfa <- ggplot(data, aes(x = net_tfa, color = factor(e401), fill = factor(e401))) +\n", - " geom_density() +\n", - " xlim(c(-20000, 150000)) +\n", - " facet_wrap(. ~ e401)\n", - "\n", - "dens_net_tfa" - ] - }, - { - "cell_type": "markdown", - "id": "13", - "metadata": { - "id": "0f4f86a7", - "papermill": { - "duration": 0.010335, - "end_time": "2022-04-19T09:06:50.935024", - "exception": false, - "start_time": "2022-04-19T09:06:50.924689", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "The unconditional APE of e401 is about $19559$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14", - "metadata": { - "id": "836c6af7", - "papermill": { - "duration": 0.038096, - "end_time": "2022-04-19T09:06:50.983602", - "exception": false, - "start_time": "2022-04-19T09:06:50.945506", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "e1 <- data[data$e401 == 1, ]\n", - "e0 <- data[data$e401 == 0, ]\n", - "round(mean(e1$net_tfa) - mean(e0$net_tfa), 0)" - ] - }, - { - "cell_type": "markdown", - "id": "15", - "metadata": { - "id": "22b09926", - "papermill": { - "duration": 0.01047, - "end_time": "2022-04-19T09:06:51.004618", - "exception": false, - "start_time": "2022-04-19T09:06:50.994148", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Among the $3682$ individuals that are eligible, $2594$ decided to participate in the program. The unconditional APE of p401 is about $27372$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16", - "metadata": { - "id": "e78aaa58", - "papermill": { - "duration": 0.039305, - "end_time": "2022-04-19T09:06:51.054616", - "exception": false, - "start_time": "2022-04-19T09:06:51.015311", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "p1 <- data[data$p401 == 1, ]\n", - "p0 <- data[data$p401 == 0, ]\n", - "round(mean(p1$net_tfa) - mean(p0$net_tfa), 0)" - ] - }, - { - "cell_type": "markdown", - "id": "17", - "metadata": { - "id": "e0af3c81", - "papermill": { - "duration": 0.010831, - "end_time": "2022-04-19T09:06:51.076114", - "exception": false, - "start_time": "2022-04-19T09:06:51.065283", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "As discussed, these estimates are biased since they do not account for saver heterogeneity and endogeneity of participation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18", - "metadata": { - "id": "A03YWrvUW0Sm", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# instrument variable\n", - "Z <- data[, \"e401\"]\n", - "# treatment variable\n", - "D <- data[, \"p401\"]\n", - "# outcome variable\n", - "y <- data[, \"net_tfa\"]" - ] - }, - { - "cell_type": "markdown", - "id": "19", - "metadata": { - "id": "RVUbOMRRWwBm" - }, - "source": [ - "### We construct the engineered features for controls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20", - "metadata": { - "id": "7vt1hbdBG8cb", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Constructing the controls\n", - "x_formula <- paste(\"~ poly(age, 6, raw=TRUE) + poly(inc, 8, raw=TRUE) + poly(educ, 4, raw=TRUE) + \",\n", - " \"poly(fsize, 2, raw=TRUE) + male + marr + twoearn + db + pira + hown\")\n", - "X <- as.data.table(model.frame(x_formula, pension))\n", - "head(X)" - ] - }, - { - "cell_type": "markdown", - "id": "21", - "metadata": { - "id": "yzNigd7YYVuA" - }, - "source": [ - "# Instrumental Variables: Effect of 401k Participation on Financial Assets" - ] - }, - { - "cell_type": "markdown", - "id": "22", - "metadata": { - "id": "FI2u5KU7YWIF" - }, - "source": [ - "## Double ML IV under Partial Linearity" - ] - }, - { - "cell_type": "markdown", - "id": "23", - "metadata": { - "id": "uhDK6Em_YWSm" - }, - "source": [ - "Now, we consider estimation of average treatment effects of participation in 401k, i.e. `p401`, with the binary instrument being eligibility in 401k, i.e. `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. We consider a partially linear structural equation model:\n", - "\\begin{align}\n", - "Y :=~& g_Y(\\epsilon_Y) D + f_Y(A, X, \\epsilon_Y), \\\\\n", - "D :=~& f_D(Z, X, A, \\epsilon_D), \\\\\n", - "Z :=~& f_Z(X, \\epsilon_Z),\\\\\n", - "A :=~& f_A(X, \\epsilon_A), \\\\\n", - "X :=~& \\epsilon_X,\n", - "\\end{align}\n", - "where $A$ is a vector of un-observed confounders.\n", - "\n", - "Under this structural equation model, the average treatment effect:\n", - "\\begin{align}\n", - "\\alpha = E[Y(1) - Y(0)]\n", - "\\end{align}\n", - "can be identified by the moment restriction:\n", - "\\begin{align}\n", - "E[(\\tilde{Y} - \\alpha \\tilde{D}) \\tilde{Z}] = 0\n", - "\\end{align}\n", - "where for any variable $V$, we denote with $\\tilde{V} = V - E[V|X]$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24", - "metadata": { - "id": "bdUGB53AYf3S", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "set.seed(1)\n", - "# family gaussian means that we'll be using square loss\n", - "yfit_lasso_cv <- cv.glmnet(as.matrix(X), y, family = \"gaussian\", alpha = 1)\n", - "# family gaussian means that we'll be using square loss\n", - "dfit_lasso_cv <- cv.glmnet(as.matrix(X), D, family = \"gaussian\", alpha = 1)\n", - "# family gaussian means that we'll be using square loss\n", - "zfit_lasso_cv <- cv.glmnet(as.matrix(X), Z, family = \"gaussian\", alpha = 1)\n", - "\n", - "\n", - "yhat_lasso_cv <- predict(yfit_lasso_cv, newx = as.matrix(X)) # predictions\n", - "dhat_lasso_cv <- predict(dfit_lasso_cv, newx = as.matrix(X)) # predictions\n", - "zhat_lasso_cv <- predict(zfit_lasso_cv, newx = as.matrix(X)) # predictions\n", - "\n", - "resy <- y - yhat_lasso_cv\n", - "resD <- D - dhat_lasso_cv\n", - "resZ <- Z - zhat_lasso_cv\n", - "\n", - "# Estimate\n", - "mean(resy * resZ) / mean(resZ * resD)" - ] - }, - { - "cell_type": "markdown", - "id": "25", - "metadata": { - "id": "Fw1ZxeBKZcRm" - }, - "source": [ - "Recall if we want to do inference, we need to either use the theoretically driven penalty paramter for Lasso or perform cross-fitting." - ] - }, - { - "cell_type": "markdown", - "id": "26", - "metadata": { - "id": "jnBOtXXuZnkz" - }, - "source": [ - "### DML with Non-Linear ML Models and Cross-fitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27", - "metadata": { - "id": "K_vQlMYmz91I", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML for PLIVM with D and Z as classifiers or regressors\n", - "dml2_for_plivm <- function(x, d, z, y, dreg, yreg, zreg, nfold = 5, method = \"regression\") {\n", - " nobs <- nrow(x)\n", - " foldid <- rep.int(1:nfold, times = ceiling(nobs / nfold))[sample.int(nobs)]\n", - " I <- split(1:nobs, foldid)\n", - " # create residualized objects to fill\n", - " ytil <- dtil <- ztil <- rep(NA, nobs)\n", - " # obtain cross-fitted residuals\n", - " cat(\"fold: \")\n", - " for (b in seq_along(I)) {\n", - " if (method == \"randomforest\") {\n", - " # take a fold out\n", - " dfit <- dreg(x[-I[[b]], ], d[-I[[b]]])\n", - " zfit <- zreg(x[-I[[b]], ], z[-I[[b]]])\n", - " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]])\n", - " # predict the fold out\n", - " dhat <- predict(dfit, x[I[[b]], ], type = \"prob\")[, 2] # type = \"prob\" is like predict_proba in scikitlearn\n", - " zhat <- predict(zfit, x[I[[b]], ], type = \"prob\")[, 2]\n", - " # default type = \"response\" for regression for RF, type = \"vector\" for regression for Decision Trees\n", - " yhat <- predict(yfit, x[I[[b]], ])\n", - " # record residual\n", - " # as.numeric will turn d = as.factor(d) from 0,1 to 1,2 so subtract 1!\n", - " dtil[I[[b]]] <- (as.numeric(d[I[[b]]]) - 1 - dhat)\n", - " ztil[I[[b]]] <- (as.numeric(z[I[[b]]]) - 1 - zhat)\n", - " ytil[I[[b]]] <- (y[I[[b]]] - yhat)\n", - " } else if (method == \"regression\") { # works for both boosted trees and glmnet\n", - " # take a fold out\n", - " dfit <- dreg(x[-I[[b]], ], d[-I[[b]]])\n", - " zfit <- zreg(x[-I[[b]], ], z[-I[[b]]])\n", - " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]])\n", - " # predict the fold out\n", - " dhat <- predict(dfit, x[I[[b]], ], type = \"response\")\n", - " zhat <- predict(zfit, x[I[[b]], ], type = \"response\")\n", - " yhat <- predict(yfit, x[I[[b]], ], type = \"response\")\n", - " # record residual\n", - " dtil[I[[b]]] <- (d[I[[b]]] - dhat)\n", - " ztil[I[[b]]] <- (z[I[[b]]] - zhat)\n", - " ytil[I[[b]]] <- (y[I[[b]]] - yhat)\n", - " } else if (method == \"decisiontrees\") {\n", - " # take a fold out\n", - " dfit <- dreg(x[-I[[b]], ], as.factor(d)[-I[[b]]])\n", - " zfit <- zreg(x[-I[[b]], ], as.factor(z)[-I[[b]]])\n", - " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]])\n", - " # predict the fold out\n", - " dhat <- predict(dfit, x[I[[b]], ])[, 2]\n", - " zhat <- predict(zfit, x[I[[b]], ])[, 2]\n", - " yhat <- predict(yfit, x[I[[b]], ])\n", - " # record residual\n", - " dtil[I[[b]]] <- (d[I[[b]]] - dhat)\n", - " ztil[I[[b]]] <- (z[I[[b]]] - zhat)\n", - " ytil[I[[b]]] <- (y[I[[b]]] - yhat)\n", - " }\n", - "\n", - " cat(b, \" \")\n", - " }\n", - " ivfit <- tsls(y = ytil, d = dtil, x = NULL, z = ztil, intercept = FALSE)\n", - " coef_est <- ivfit$coef # extract coefficient\n", - " se <- ivfit$se # record standard error\n", - " cat(sprintf(\"\\ncoef (se) = %g (%g)\\n\", coef_est, se))\n", - "\n", - " return(list(coef_est = coef_est, se = se, dtil = dtil, ytil = ytil, ztil = ztil))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28", - "metadata": { - "id": "puSCLNvofQxA", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "summary_for_plivm <- function(point, stderr, resy, resD, resZ, name) {\n", - " data <- data.frame(\n", - " estimate = point, # point estimate\n", - " stderr = stderr, # standard error\n", - " lower = point - 1.96 * stderr, # lower end of 95% confidence interval\n", - " upper = point + 1.96 * stderr, # upper end of 95% confidence interval\n", - " `rmse y` = sqrt(mean(resy^2)), # RMSE of model that predicts outcome y\n", - " `rmse D` = sqrt(mean(resD^2)), # RMSE of model that predicts treatment D\n", - " `rmse Z` = sqrt(mean(resZ^2)), # RMSE of model that predicts treatment D\n", - " `accuracy D` = mean(abs(resD) < 0.5), # binary classification accuracy of model for D\n", - " `accuracy Z` = mean(abs(resZ) < 0.5) # binary classification accuracy of model for Z\n", - " )\n", - " rownames(data) <- name\n", - " return(data)\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "29", - "metadata": { - "id": "1Z5vrvrlbuPj" - }, - "source": [ - "#### Double Lasso with Cross-Fitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30", - "metadata": { - "id": "vBJm7BkUYgsG", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with LassoCV\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Lasso CV \\n\"))\n", - "\n", - "dreg_lasso_cv <- function(x, d) {\n", - " cv.glmnet(x, d, family = \"gaussian\", alpha = 1, nfolds = 5)\n", - "}\n", - "yreg_lasso_cv <- function(x, y) {\n", - " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", - "}\n", - "zreg_lasso_cv <- function(x, z) {\n", - " cv.glmnet(x, z, family = \"gaussian\", alpha = 1, nfolds = 5)\n", - "}\n", - "\n", - "dml2_results <- dml2_for_plivm(as.matrix(X), D, Z, y, dreg_lasso_cv, yreg_lasso_cv, zreg_lasso_cv,\n", - " nfold = 5, method = \"regression\")\n", - "sum_lasso_cv <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", - " dml2_results$ztil, name = \"LassoCV\")\n", - "tableplr <- data.frame()\n", - "tableplr <- rbind(sum_lasso_cv)\n", - "tableplr\n", - "\n", - "ytil_lasso <- dml2_results$ytil\n", - "dtil_lasso <- dml2_results$dtil\n", - "ztil_lasso <- dml2_results$ztil" - ] - }, - { - "cell_type": "markdown", - "id": "31", - "metadata": { - "id": "pyrem2YniNls" - }, - "source": [ - "#### Using a $\\ell_2$ Penalized Logistic Regression for D and Z" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32", - "metadata": { - "id": "FM6WvQXKYgxL", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Lasso/Ridge\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Lasso/Logistic \\n\"))\n", - "\n", - "dreg_ridge_cv <- function(x, d) {\n", - " cv.glmnet(x, d, family = \"binomial\", alpha = 0, nfolds = 5)\n", - "}\n", - "yreg_ridge_cv <- function(x, y) {\n", - " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", - "}\n", - "zreg_ridge_cv <- function(x, z) {\n", - " cv.glmnet(x, z, family = \"binomial\", alpha = 0, nfolds = 5)\n", - "}\n", - "\n", - "dml2_results <- dml2_for_plivm(as.matrix(X), D, Z, y, dreg_ridge_cv, yreg_ridge_cv, zreg_ridge_cv,\n", - " nfold = 5, method = \"regression\")\n", - "sum_lasso_ridge_cv <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", - " dml2_results$ztil, name = \"LassoCV/LogisticCV\")\n", - "tableplr <- rbind(tableplr, sum_lasso_ridge_cv)\n", - "tableplr\n", - "\n", - "ytil_ridge <- dml2_results$ytil\n", - "dtil_ridge <- dml2_results$dtil\n", - "ztil_ridge <- dml2_results$ztil" - ] - }, - { - "cell_type": "markdown", - "id": "33", - "metadata": { - "id": "yfTdX3__jcwI" - }, - "source": [ - "### Random Forests" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34", - "metadata": { - "id": "mMvJT6NZHW1_", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Random Forest\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Random Forest \\n\"))\n", - "\n", - "dreg_rf <- function(x, d) {\n", - " randomForest(x, d, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "yreg_rf <- function(x, y) {\n", - " randomForest(x, y, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "zreg_rf <- function(x, z) {\n", - " randomForest(x, z, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "\n", - "dml2_results <- dml2_for_plivm(as.matrix(X), as.factor(D), as.factor(Z), y, dreg_rf, yreg_rf, zreg_rf,\n", - " nfold = 5, method = \"randomforest\")\n", - "sum_rf <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", - " dml2_results$ztil, name = \"RF\")\n", - "tableplr <- rbind(tableplr, sum_rf)\n", - "tableplr\n", - "\n", - "ytil_rf <- dml2_results$ytil\n", - "dtil_rf <- dml2_results$dtil\n", - "ztil_rf <- dml2_results$ztil" - ] - }, - { - "cell_type": "markdown", - "id": "35", - "metadata": { - "id": "4I1oVQutjeqE" - }, - "source": [ - "### Decision Trees" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36", - "metadata": { - "id": "ayrnTPeBHW88", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Decision Trees\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Decision Trees \\n\"))\n", - "\n", - "# decision tree takes in X as dataframe, not matrix/array\n", - "dreg_tr <- function(x, d) {\n", - " rpart(as.formula(\"D~.\"), cbind(data.frame(D = d), x), method = \"class\", minbucket = 10, cp = 0.001)\n", - "}\n", - "yreg_tr <- function(x, y) {\n", - " rpart(as.formula(\"y~.\"), cbind(data.frame(y = y), x), minbucket = 10, cp = 0.001)\n", - "}\n", - "zreg_tr <- function(x, z) {\n", - " rpart(as.formula(\"Z~.\"), cbind(data.frame(Z = z), x), method = \"class\", minbucket = 10, cp = 0.001)\n", - "}\n", - "\n", - "dml2_results <- dml2_for_plivm(X, D, Z, y, dreg_tr, yreg_tr, zreg_tr,\n", - " nfold = 5, method = \"decisiontrees\")\n", - "sum_tr <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", - " dml2_results$ztil, name = \"Decision Trees\")\n", - "tableplr <- rbind(tableplr, sum_tr)\n", - "tableplr\n", - "\n", - "ytil_tr <- dml2_results$ytil\n", - "dtil_tr <- dml2_results$dtil\n", - "ztil_tr <- dml2_results$ztil" - ] - }, - { - "cell_type": "markdown", - "id": "37", - "metadata": { - "id": "h7Jo_WXUjgjb" - }, - "source": [ - "### Boosted Trees" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38", - "metadata": { - "id": "nzlszy9zjiSy", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Boosted Trees\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Boosted Trees \\n\"))\n", - "\n", - "# NB: early stopping cannot easily be implemented with gbm\n", - "## set n.trees = best, where best <- gbm.perf(dreg_boost, plot.it = FALSE)\n", - "dreg_boost <- function(x, d) {\n", - " gbm(as.formula(\"D~.\"), cbind(data.frame(D = d), x), distribution = \"bernoulli\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "yreg_boost <- function(x, y) {\n", - " gbm(as.formula(\"y~.\"), cbind(data.frame(y = y), x), distribution = \"gaussian\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "zreg_boost <- function(x, z) {\n", - " gbm(as.formula(\"Z~.\"), cbind(data.frame(Z = z), x), distribution = \"bernoulli\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "\n", - "# passing these through regression as type=\"response\", and D and Z should not be factors!\n", - "dml2_results <- dml2_for_plivm(X, D, Z, y, dreg_boost, yreg_boost, zreg_boost,\n", - " nfold = 5, method = \"regression\")\n", - "sum_boost <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", - " dml2_results$ztil, name = \"Boosted Trees\")\n", - "tableplr <- rbind(tableplr, sum_boost)\n", - "tableplr\n", - "\n", - "ytil_boost <- dml2_results$ytil\n", - "dtil_boost <- dml2_results$dtil\n", - "ztil_boost <- dml2_results$ztil" - ] - }, - { - "cell_type": "markdown", - "id": "39", - "metadata": { - "id": "oQpoYedAc4Ic" - }, - "source": [ - "## Ensembles" - ] - }, - { - "cell_type": "markdown", - "id": "40", - "metadata": { - "id": "_LLsorarc8Mh" - }, - "source": [ - "Boosted trees give the best RMSE for Y, D, and Z, so the ensemble based on choosing the best performing prediction rule is identical to boosting in this case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41", - "metadata": { - "id": "kAePILCadEVh", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Best fit is boosted trees for D, Z, Y\n", - "\n", - "sum_best <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", - " dml2_results$ztil, name = \"Best\")\n", - "tableplr <- rbind(tableplr, sum_best)\n", - "tableplr" - ] - }, - { - "cell_type": "markdown", - "id": "42", - "metadata": { - "id": "KaaDX4kkdIMx" - }, - "source": [ - "We'll form a model average with unconstrained least squares weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43", - "metadata": { - "id": "mCsyY3iJdHm_", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Least squares model average\n", - "\n", - "dhat_lasso <- D - dtil_lasso\n", - "dhat_ridge <- D - dtil_ridge\n", - "dhat_rf <- D - dtil_rf\n", - "dhat_tr <- D - dtil_tr\n", - "dhat_boost <- D - dtil_boost\n", - "\n", - "yhat_lasso <- y - ytil_lasso\n", - "yhat_ridge <- y - ytil_ridge\n", - "yhat_rf <- y - ytil_rf\n", - "yhat_tr <- y - ytil_tr\n", - "yhat_boost <- y - ytil_boost\n", - "\n", - "zhat_lasso <- Z - ztil_lasso\n", - "zhat_ridge <- Z - ztil_ridge\n", - "zhat_rf <- Z - ztil_rf\n", - "zhat_tr <- Z - ztil_tr\n", - "zhat_boost <- Z - ztil_boost\n", - "\n", - "ma_dtil <- lm(D ~ dhat_lasso + dhat_ridge + dhat_rf + dhat_tr + dhat_boost)$residuals\n", - "ma_ytil <- lm(y ~ yhat_lasso + yhat_ridge + yhat_rf + yhat_tr + yhat_boost)$residuals\n", - "ma_ztil <- lm(Z ~ zhat_lasso + zhat_ridge + zhat_rf + zhat_tr + zhat_boost)$residuals\n", - "\n", - "ivfit <- tsls(y = ma_ytil, d = ma_dtil, x = NULL, z = ma_ztil, intercept = FALSE)\n", - "coef_est <- ivfit$coef # extract coefficient\n", - "se <- ivfit$se # record standard error\n", - "\n", - "sum_ma <- summary_for_plivm(coef_est, se, ma_ytil, ma_dtil, ma_ztil, name = \"Model Average\")\n", - "tableplr <- rbind(tableplr, sum_ma)\n", - "tableplr" - ] - }, - { - "cell_type": "markdown", - "id": "44", - "metadata": { - "id": "8OUusM2BpZH4" - }, - "source": [ - "## Inference Robust to Weak Identification\n", - "\n", - "Now we turn toward robustness when the instrument is weak.\n", - "\n", - "Ideally, we would do (semi) cross-fitting with AutoML in order to find good first-stage models and re-run DML with these models. Unfortunately this is not easy to do in R. In the case of semi cross-fitting, we can use R's H20 AutoML trained on the entire training set $y\\sim X$, $D \\sim X$, $Z\\sim X$ to determine the best model (eg ensemble), but H20 does not allow you to extract the best model so we can re-use that in DML.\n", - "\n", - "Thus, in the below analysis of robust inference, we choose Boosted Trees as they perform well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45", - "metadata": { - "id": "UeNF5j1ApYYy", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "robust_inference <- function(point, stderr, resD, resy, resZ, grid, alpha = 0.05) {\n", - " # Inference in the partially linear IV model that is robust to weak identification.\n", - " # grid: grid of theta values to search over when trying to identify the confidence region\n", - " # alpha: confidence level\n", - "\n", - " n <- dim(X)[1]\n", - " thr <- qchisq(1 - alpha, df = 1)\n", - " accept <- c()\n", - "\n", - " for (theta in grid) {\n", - " moment <- (resy - theta * resD) * resZ\n", - " test <- n * mean(moment)^2 / var(moment)\n", - " if (test <= thr) {\n", - " accept <- c(accept, theta)\n", - " }\n", - " }\n", - "\n", - " return(accept)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46", - "metadata": { - "id": "X21PuuUnsa25", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "grid <- seq(0, 20000, length.out = 10000)\n", - "region <- robust_inference(dml2_results$coef_est, dml2_results$stderr, dml2_results$dtil, dml2_results$ytil,\n", - " dml2_results$ztil, grid = grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47", - "metadata": { - "id": "x-ZSzMkVqI45", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "grid <- seq(0, 20000, length.out = 10000)\n", - "region <- robust_inference(dml2_results$coef_est, dml2_results$stderr, dml2_results$dtil, dml2_results$ytil,\n", - " dml2_results$ztil, grid = grid)\n", - "# Calculate min and max\n", - "min_region <- min(region)\n", - "max_region <- max(region)\n", - "\n", - "print(min_region)\n", - "print(max_region)" - ] - }, - { - "cell_type": "markdown", - "id": "48", - "metadata": { - "id": "nKQGPfXWIKmh" - }, - "source": [ - "# Interactive IV Model and LATE" - ] - }, - { - "cell_type": "markdown", - "id": "49", - "metadata": { - "id": "bCayhAlaINjL" - }, - "source": [ - "Now, we consider estimation of local average treatment effects (LATE) of participation `p401`, with the binary instrument `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. Here the structural equation model is:\n", - "\\begin{align}\n", - "Y :=~& f_Y (D, X, A, \\epsilon_Y) \\\\\n", - "D :=~& f_D(Z, X, A, \\epsilon_D) \\in \\{0,1\\}, \\\\\n", - "Z :=~& f_Z(X,\\epsilon_Z) \\in \\{0,1\\}, \\\\\n", - "X :=~& \\epsilon_X, \\quad A = \\epsilon_A,\n", - "\\end{align}\n", - "where $\\epsilon$'s are all exogenous and independent,\n", - "and\n", - "$$\n", - "z \\mapsto f_D(z , A, X, \\epsilon_D) \\text{ is weakly increasing (weakly monotone)}.\n", - "$$\n", - "and $A$ is a vector of unobserved confounders. Note that in our setting monotonicity is satisfied, since participation is only feasible when it is eligible. Thus we have that $D=0$ whenever $Z=0$. Thus it can only be that $f_D(1, A, X, \\epsilon_D) \\geq 0 = f_D(0, A, X, \\epsilon_D)$.\n", - "\n", - "In this case, we can estimate the local average treatment effect (LATE):\n", - "$$\n", - "\\alpha = E[Y(1) - Y(0) | D(1) > D(0)]\n", - "$$\n", - "This can be identified using the Neyman orthogonal moment equation:\n", - "\\begin{align}\n", - "E\\left[g(1, X) - g(0, X) + H(Z) (Y - g(Z, X)) - \\alpha \\cdot (m(1, X) - m(0, X) + H(Z) (D - m(Z, X))\\right] = 0\n", - "\\end{align}\n", - "where\n", - "\\begin{align}\n", - "g(Z,X) =~& E[Y|Z,X],\\\\\n", - "m(Z,X) =~& E[D|Z,X],\\\\\n", - "H(Z) =~& \\frac{Z}{Pr(Z=1|X)} - \\frac{1 - Z}{1 - Pr(Z=1|X)}\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50", - "metadata": { - "id": "rQYifUnFIt5z", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "get_dhat0 <- function(XZ0, DZ0, Xb, dreg0, type = NULL, DZ0factor = NULL) {\n", - " # train a treatment model on training data that received Z=0 and predict treatment on all data in test set\n", - " if (mean(DZ0) > 0) { # it could be that D=0, whenever Z=0 deterministically\n", - " dreg0_ <- dreg0\n", - " if (is.null(DZ0factor)) {\n", - " dfit0 <- dreg0_((XZ0), DZ0)\n", - " } else {\n", - " dfit0 <- dreg0_((XZ0), DZ0factor)\n", - " }\n", - " if (is.null(type)) {\n", - " return(predict(dfit0, (Xb))[, 2])\n", - " } else if (type == \"prob\") {\n", - " return(predict(dfit0, (Xb), type = \"prob\")[, 2])\n", - " } else if (type == \"reponse\") {\n", - " return(predict(dfit0, (Xb), type = \"response\"))\n", - " } else {\n", - " stop(\"Invalid argument `type`.\")\n", - " }\n", - " } else {\n", - " return(0)\n", - " }\n", - "}\n", - "\n", - "get_dhat1 <- function(XZ1, DZ1, Xb, dreg1, type = NULL, DZ1factor = NULL) {\n", - " # train a treamtent model on training data that received Z=1 and predict treatment on all data in test set\n", - " if (mean(DZ1) < 1) { # it could be that D=1, whenever Z=1 deterministically\n", - " dreg1_ <- dreg1\n", - " if (is.null(DZ1factor)) {\n", - " dfit1 <- dreg1_((XZ1), DZ1)\n", - " } else {\n", - " dfit1 <- dreg1_((XZ1), DZ1factor)\n", - " }\n", - " if (is.null(type)) {\n", - " return(predict(dfit1, (Xb))[, 2])\n", - " } else if (type == \"prob\") {\n", - " return(predict(dfit1, (Xb), type = \"prob\")[, 2])\n", - " } else if (type == \"response\") {\n", - " return(predict(dfit1, (Xb), type = \"response\"))\n", - " } else {\n", - " stop(\"Invalid argument `type`.\")\n", - " }\n", - " } else {\n", - " return(1)\n", - " }\n", - "}\n", - "\n", - "# DML for IIVM with D and Z as classifiers or regressors\n", - "dml2_for_iivm <- function(x, d, z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", - " trimming = 0.01, nfold = 5, method = \"classification\", dt = 0, bt = 0) {\n", - " # this implements DML2 algorithm, where there moments are estimated via DML, before constructing\n", - " # the pooled estimate of theta randomly split data into folds\n", - "\n", - " ## NB This method has many if statements to accommodate the various estimators we will use.\n", - " ## Unlike Python's sklearn, all methods have idfferent default arguments in their predict functions.\n", - " ## See official R documentation for details.\n", - "\n", - " yhat0 <- rep(0, length(y))\n", - " yhat1 <- rep(0, length(y))\n", - " dhat0 <- rep(0, length(d))\n", - " dhat1 <- rep(0, length(d))\n", - " zhat <- rep(0, length(Z))\n", - "\n", - " nobs <- nrow(X)\n", - " foldid <- rep.int(1:nfold, times = ceiling(nobs / nfold))[sample.int(nobs)]\n", - " I <- split(1:nobs, foldid)\n", - " # create residualized objects to fill\n", - " ytil <- dtil <- ztil <- rep(NA, nobs)\n", - "\n", - " # obtain cross-fitted residuals\n", - " cat(\"fold: \")\n", - " for (b in seq_along(I)) {\n", - " # define helpful variables\n", - " Xb <- X[I[[b]], ]\n", - " Xnotb <- X[-I[[b]], ]\n", - " Znotb <- Z[-I[[b]]]\n", - "\n", - " # training dfs subsetted on the -I[[b]] fold\n", - " XZ0 <- X[-I[[b]], ][Z[-I[[b]]] == 0]\n", - " yZ0 <- y[-I[[b]]][Z[-I[[b]]] == 0]\n", - " XZ1 <- X[-I[[b]], ][Z[-I[[b]]] == 1]\n", - " yZ1 <- y[-I[[b]]][Z[-I[[b]]] == 1]\n", - " DZ0 <- d[-I[[b]]][Z[-I[[b]]] == 0]\n", - " DZ1 <- d[-I[[b]]][Z[-I[[b]]] == 1]\n", - "\n", - "\n", - " if (method == \"regression\") {\n", - " XZ0 <- as.matrix(XZ0)\n", - " XZ1 <- as.matrix(XZ1)\n", - " Xb <- as.matrix(Xb)\n", - " Xnotb <- as.matrix(Xnotb)\n", - "\n", - " # Train an outcome model on training data that received Z=0 and predict outcome on all data in the test set\n", - " yfit0 <- yreg0((XZ0), yZ0)\n", - " yhat0[I[[b]]] <- predict(yfit0, (Xb)) # default type = \"response\"\n", - "\n", - " # train an outcome model on training data that received Z=1 and predict outcome on all data in test set\n", - " yfit1 <- yreg1((XZ1), yZ1)\n", - " yhat1[I[[b]]] <- predict(yfit1, (Xb))\n", - "\n", - " # train a treatment model on training data that received Z=0 and predict treatment on all data in test set\n", - " # default type = \"response\", but for family binomial it's logg odds\n", - " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, type = \"response\")\n", - " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, type = \"response\")\n", - "\n", - " } else if (method == \"randomforest\") {\n", - " DZ0factor <- as.factor(D)[-I[[b]]][Z[-I[[b]]] == 0]\n", - " DZ1factor <- as.factor(D)[-I[[b]]][Z[-I[[b]]] == 1]\n", - " Znotb <- as.factor(Znotb)\n", - "\n", - " yfit0 <- yreg0((XZ0), yZ0)\n", - " yhat0[I[[b]]] <- predict(yfit0, (Xb), type = \"response\")\n", - " yfit1 <- yreg1((XZ1), yZ1)\n", - " yhat1[I[[b]]] <- predict(yfit1, (Xb), type = \"response\")\n", - "\n", - " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, type = \"prob\", DZ0factor = DZ0factor)\n", - " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, type = \"prob\", DZ1factor = DZ1factor)\n", - "\n", - " } else if (method == \"decisiontrees\") {\n", - " XZ0 <- as.data.frame(XZ0)\n", - " XZ1 <- as.data.frame(XZ1)\n", - " Xb <- as.data.frame(Xb)\n", - " Xnotb <- as.data.frame(Xnotb)\n", - "\n", - " yfit0 <- yreg0((XZ0), yZ0)\n", - " # default type = \"response\" for decision trees for continuous response\n", - " yhat0[I[[b]]] <- predict(yfit0, (Xb))\n", - "\n", - " yfit1 <- yreg1((XZ1), yZ1)\n", - " yhat1[I[[b]]] <- predict(yfit1, (Xb))\n", - "\n", - " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, DZ0factor = as.factor(DZ0))\n", - " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, DZ1factor = as.factor(DZ1))\n", - "\n", - " } else if (method == \"boostedtrees\") {\n", - " XZ0 <- as.data.frame(XZ0)\n", - " XZ1 <- as.data.frame(XZ1)\n", - " Xb <- as.data.frame(Xb)\n", - " Xnotb <- as.data.frame(Xnotb)\n", - "\n", - " yfit0 <- yreg0((XZ0), yZ0)\n", - " yhat0[I[[b]]] <- predict(yfit0, (Xb)) # default type = \"response\" for boosted trees\n", - " yfit1 <- yreg1((XZ1), yZ1)\n", - " yhat1[I[[b]]] <- predict(yfit1, (Xb))\n", - "\n", - " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, type = \"response\")\n", - " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, type = \"response\")\n", - "\n", - " }\n", - "\n", - " # propensity scores:\n", - " if (method == \"regression\") {\n", - " zfit_b <- zreg((Xnotb), Znotb)\n", - " zhat_b <- predict(zfit_b, (Xb), type = \"response\")\n", - " } else if (method == \"randomforest\") {\n", - " zfit_b <- zreg((Xnotb), Znotb)\n", - " zhat_b <- predict(zfit_b, (Xb), type = \"prob\")[, 2]\n", - " } else if (method == \"decisiontrees\") {\n", - " zfit_b <- zreg((Xnotb), as.factor(Znotb))\n", - " zhat_b <- predict(zfit_b, (Xb)) # default is prob, so get second column\n", - " zhat_b <- zhat_b[, 2]\n", - " } else if (method == \"boostedtrees\") {\n", - " zfit_b <- zreg((Xnotb), Znotb)\n", - " zhat_b <- predict(zfit_b, (Xb), type = \"response\")\n", - " }\n", - " zhat_b <- pmax(pmin(zhat_b, 1 - trimming), trimming) # trimming so scores are between [trimming, (1-trimming)]\n", - " zhat[I[[b]]] <- zhat_b\n", - "\n", - " cat(b, \" \")\n", - " }\n", - "\n", - "\n", - " # Prediction of treatment and outcome for observed instrument\n", - " yhat <- yhat0 * (1 - Z) + yhat1 * Z\n", - " dhat <- dhat0 * (1 - Z) + dhat1 * Z\n", - "\n", - " # residuals\n", - " ytil <- y - yhat\n", - " dtil <- D - dhat\n", - " ztil <- Z - zhat\n", - "\n", - " # doubly robust quantity for every sample\n", - " HZ <- Z / zhat - (1 - Z) / (1 - zhat)\n", - " drZ <- yhat1 - yhat0 + (y - yhat) * HZ\n", - " drD <- dhat1 - dhat0 + (D - dhat) * HZ\n", - " coef_est <- mean(drZ) / mean(drD)\n", - " cat(\"point\", coef_est)\n", - " psi <- drZ - coef_est * drD\n", - " Jhat <- mean(drD)\n", - " variance <- mean(psi^2) / Jhat^2\n", - " se <- sqrt(variance / nrow(X))\n", - " cat(\"se\", se)\n", - "\n", - " return(list(coef_est = coef_est, se = se, yhat = yhat, dhat = dhat, zhat = zhat, ytil = ytil,\n", - " dtil = dtil, ztil = ztil, drZ = drZ, drD = drD,\n", - " yhat0 = yhat0, yhat1 = yhat1, dhat0 = dhat0, dhat1 = dhat1))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51", - "metadata": { - "id": "iArB2WQHBXuV", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "summary_for_iivm <- function(coef_est, se, yhat, dhat, zhat, ytil, dtil, ztil, drZ, drD, X, Z, D, y, name) {\n", - " summary_data <- data.frame(\n", - " estimate = coef_est, # point estimate\n", - " se = se, # standard error\n", - " lower = coef_est - 1.96 * se, # lower end of 95% confidence interval\n", - " upper = coef_est + 1.96 * se, # upper end of 95% confidence interval\n", - " rmse_y = sqrt(mean(ytil^2)), # res of model that predicts outcome y\n", - " rmse_D = sqrt(mean(dtil^2)), # res of model that predicts treatment D\n", - " rmse_Z = sqrt(mean(ztil^2)), # res of model that predicts instrument Z\n", - " accuracy_D = mean(abs(dtil) < 0.5), # binary classification accuracy of model for D\n", - " accuracy_Z = mean(abs(ztil) < 0.5) # binary classification accuracy of model for Z\n", - " )\n", - " row.names(summary_data) <- name\n", - " return(summary_data)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52", - "metadata": { - "id": "Tj-8FFF3BXxV", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Lasso/Ridge\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Lasso/Logistic \\n\"))\n", - "# DML with Lasso/Ridge\n", - "dreg0 <- function(x, d) {\n", - " cv.glmnet(x, d, family = \"binomial\", alpha = 0, nfolds = 5)\n", - "}\n", - "dreg1 <- function(x, d) {\n", - " cv.glmnet(x, d, family = \"binomial\", alpha = 0, nfolds = 5)\n", - "}\n", - "yreg0 <- function(x, y) {\n", - " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", - "}\n", - "yreg1 <- function(x, y) {\n", - " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", - "}\n", - "zreg <- function(x, z) {\n", - " cv.glmnet(x, z, family = \"binomial\", alpha = 0, nfolds = 5)\n", - "}\n", - "\n", - "dml2_results <- dml2_for_iivm(as.matrix(X), D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", - " trimming = 0.01, nfold = 5, method = \"regression\")\n", - "sum_lasso_ridge_cv <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", - " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", - " dml2_results$drZ, dml2_results$drD, name = \"LassoCV/LogisticCV\")\n", - "table <- data.frame()\n", - "table <- rbind(table, sum_lasso_ridge_cv)\n", - "table\n", - "\n", - "yhat_lasso <- dml2_results$yhat\n", - "dhat_lasso <- dml2_results$dhat\n", - "yhat0_lasso <- dml2_results$yhat0\n", - "yhat1_lasso <- dml2_results$yhat1\n", - "dhat0_lasso <- dml2_results$dhat0\n", - "dhat1_lasso <- dml2_results$dhat1\n", - "zhat_lasso <- dml2_results$zhat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53", - "metadata": { - "id": "sXjbvMbEkYJd", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Random Forest\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Random Forest \\n\"))\n", - "\n", - "dreg0 <- function(x, d) {\n", - " randomForest(x, d, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "dreg1 <- function(x, d) {\n", - " randomForest(x, d, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "yreg0 <- function(x, y) {\n", - " randomForest(x, y, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "yreg1 <- function(x, y) {\n", - " randomForest(x, y, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "zreg <- function(x, z) {\n", - " randomForest(x, z, ntree = 1000, nodesize = 10)\n", - "} # ML method=Forest\n", - "\n", - "dml2_results <- dml2_for_iivm(X, D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", - " trimming = 0.01, nfold = 5, method = \"randomforest\")\n", - "sum_rf <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", - " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", - " dml2_results$drZ, dml2_results$drD, name = \"RF\")\n", - "table <- rbind(table, sum_rf)\n", - "table\n", - "\n", - "yhat_rf <- dml2_results$yhat\n", - "dhat_rf <- dml2_results$dhat\n", - "yhat0_rf <- dml2_results$yhat0\n", - "yhat1_rf <- dml2_results$yhat1\n", - "dhat0_rf <- dml2_results$dhat0\n", - "dhat1_rf <- dml2_results$dhat1\n", - "zhat_rf <- dml2_results$zhat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54", - "metadata": { - "id": "ZZRXpY8YkYNN", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Decision Trees\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Decision Trees \\n\"))\n", - "\n", - "dreg0 <- function(x, d) {\n", - " rpart(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), method = \"class\", minbucket = 10, cp = 0.001)\n", - "}\n", - "dreg1 <- function(x, d) {\n", - " rpart(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), method = \"class\", minbucket = 10, cp = 0.001)\n", - "}\n", - "yreg0 <- function(x, y) {\n", - " rpart(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), minbucket = 10, cp = 0.001)\n", - "}\n", - "yreg1 <- function(x, y) {\n", - " rpart(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), minbucket = 10, cp = 0.001)\n", - "}\n", - "zreg <- function(x, z) {\n", - " rpart(as.formula(\"Z ~ .\"), cbind(data.frame(Z = z), x), method = \"class\", minbucket = 10, cp = 0.001)\n", - "}\n", - "\n", - "dml2_results <- dml2_for_iivm(X, D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", - " trimming = 0.01, nfold = 5, method = \"decisiontrees\")\n", - "sum_tr <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", - " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", - " dml2_results$drZ, dml2_results$drD, name = \"Decision Trees\")\n", - "table <- rbind(table, sum_tr)\n", - "table\n", - "\n", - "yhat_tr <- dml2_results$yhat\n", - "dhat_tr <- dml2_results$dhat\n", - "yhat0_tr <- dml2_results$yhat0\n", - "yhat1_tr <- dml2_results$yhat1\n", - "dhat0_tr <- dml2_results$dhat0\n", - "dhat1_tr <- dml2_results$dhat1\n", - "zhat_tr <- dml2_results$zhat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55", - "metadata": { - "id": "RYqykjPskYQJ", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# DML with Boosted Trees\n", - "set.seed(123)\n", - "cat(sprintf(\"\\nDML with Boosted Trees \\n\"))\n", - "\n", - "# NB: early stopping cannot easily be implemented with gbm\n", - "## set n.trees = best, where best <- gbm.perf(dreg_boost, plot.it = FALSE)\n", - "dreg0 <- function(x, d) {\n", - " gbm(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), distribution = \"bernoulli\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "dreg1 <- function(x, d) {\n", - " gbm(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), distribution = \"bernoulli\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "yreg0 <- function(x, y) {\n", - " gbm(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), distribution = \"gaussian\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "yreg1 <- function(x, y) {\n", - " gbm(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), distribution = \"gaussian\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "zreg <- function(x, z) {\n", - " gbm(as.formula(\"Z ~ .\"), cbind(data.frame(Z = z), x), distribution = \"bernoulli\",\n", - " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", - "}\n", - "\n", - "# passing these through regression as type=\"response\", and D and Z should not be factors!\n", - "dml2_results <- dml2_for_iivm(X, D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", - " trimming = 0.01, nfold = 5, method = \"boostedtrees\")\n", - "sum_boost <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", - " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", - " dml2_results$drZ, dml2_results$drD, name = \"Boosted Trees\")\n", - "table <- rbind(table, sum_boost)\n", - "table\n", - "\n", - "yhat_boost <- dml2_results$yhat\n", - "dhat_boost <- dml2_results$dhat\n", - "yhat0_boost <- dml2_results$yhat0\n", - "yhat1_boost <- dml2_results$yhat1\n", - "dhat0_boost <- dml2_results$dhat0\n", - "dhat1_boost <- dml2_results$dhat1\n", - "zhat_boost <- dml2_results$zhat" - ] - }, - { - "cell_type": "markdown", - "id": "56", - "metadata": { - "id": "29vcyCYsktQ3" - }, - "source": [ - "## Ensembles" - ] - }, - { - "cell_type": "markdown", - "id": "57", - "metadata": { - "id": "vuyfd9UJkw9G" - }, - "source": [ - "Boosted trees give the best RMSE for D and Z and random forests give the best RMSE for Y." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58", - "metadata": { - "id": "Y9_T5SMUk3Rd", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Best fit is boosted trees for D, Z and random forests for Y\n", - "\n", - "best_yhat0 <- yhat0_rf\n", - "best_yhat1 <- yhat1_rf\n", - "best_yhat <- yhat_rf\n", - "\n", - "best_dhat0 <- dhat0_boost\n", - "best_dhat1 <- dhat1_boost\n", - "best_dhat <- dhat_boost\n", - "\n", - "best_zhat <- zhat_boost\n", - "\n", - "ytil_best <- y - best_yhat\n", - "dtil_best <- D - best_dhat\n", - "ztil_best <- Z - best_zhat\n", - "\n", - "# doubly robust quantity for every sample\n", - "HZ <- Z / best_zhat - (1 - Z) / (1 - best_zhat)\n", - "drZ <- best_yhat1 - best_yhat0 + (y - best_yhat) * HZ\n", - "drD <- best_dhat1 - best_dhat0 + (D - best_dhat) * HZ\n", - "coef_est <- mean(drZ) / mean(drD)\n", - "psi <- drZ - coef_est * drD\n", - "Jhat <- mean(drD)\n", - "variance <- mean(psi^2) / Jhat^2\n", - "se <- sqrt(variance / nrow(X))\n", - "\n", - "sum_best <- summary_for_iivm(coef_est, se, best_yhat, best_dhat, best_zhat,\n", - " ytil_best, dtil_best, ztil_best, drZ, drD, name = \"Best\")\n", - "table <- rbind(table, sum_best)\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "59", - "metadata": { - "id": "RyRS9zNUlMCF" - }, - "source": [ - "We'll form a model average with unconstrained least squares weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60", - "metadata": { - "id": "1H4sCbO2lLpJ", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Least squares model average\n", - "ma_dcoef <- lm(D ~ dhat_lasso + dhat_rf + dhat_tr + dhat_boost - 1)$coef\n", - "ma_ycoef <- lm(y ~ yhat_lasso + yhat_rf + yhat_tr + yhat_boost - 1)$coef\n", - "ma_zcoef <- lm(Z ~ zhat_lasso + zhat_rf + zhat_tr + zhat_boost - 1)$coef\n", - "\n", - "ma_yhat0 <- cbind(yhat0_lasso, yhat0_rf, yhat0_tr, yhat0_boost) %*% as.matrix(ma_ycoef)\n", - "ma_yhat1 <- cbind(yhat1_lasso, yhat1_rf, yhat1_tr, yhat1_boost) %*% as.matrix(ma_ycoef)\n", - "ma_dhat0 <- cbind(dhat0_lasso, dhat0_rf, dhat0_tr, dhat0_boost) %*% as.matrix(ma_dcoef)\n", - "ma_dhat1 <- cbind(dhat1_lasso, dhat1_rf, dhat1_tr, dhat1_boost) %*% as.matrix(ma_dcoef)\n", - "ma_zhat <- cbind(zhat_lasso, zhat_rf, zhat_tr, zhat_boost) %*% as.matrix(ma_zcoef)\n", - "\n", - "# Prediction of treatment and outcome for observed instrument\n", - "ma_yhat <- ma_yhat0 * (1 - Z) + ma_yhat1 * Z\n", - "ma_dhat <- ma_dhat0 * (1 - Z) + ma_dhat1 * Z\n", - "\n", - "# residuals\n", - "ma_ytil <- y - ma_yhat\n", - "ma_dtil <- D - ma_dhat\n", - "ma_ztil <- Z - ma_zhat\n", - "\n", - "# doubly robust quantity for every sample\n", - "HZ <- Z / ma_zhat - (1 - Z) / (1 - ma_zhat)\n", - "drZ <- ma_yhat1 - ma_yhat0 + (y - ma_yhat) * HZ\n", - "drD <- ma_dhat1 - ma_dhat0 + (D - ma_dhat) * HZ\n", - "coef_est <- mean(drZ) / mean(drD)\n", - "psi <- drZ - coef_est * drD\n", - "Jhat <- mean(drD)\n", - "variance <- mean(psi^2) / Jhat^2\n", - "se <- sqrt(variance / nrow(X))\n", - "\n", - "sum_ma <- summary_for_iivm(coef_est, se, ma_yhat, ma_dhat, ma_zhat,\n", - " ma_ytil, ma_dtil, ma_ztil, drZ, drD, name = \"Model Average\")\n", - "table <- rbind(table, sum_ma)\n", - "table" - ] - }, - { - "cell_type": "markdown", - "id": "61", - "metadata": { - "id": "UflbjTEG5SXV" - }, - "source": [ - "Comparing with the PLR model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62", - "metadata": { - "id": "CIS-58oi4sa1", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "tableplr" - ] - }, - { - "cell_type": "markdown", - "id": "63", - "metadata": { - "id": "M4Zi0FPH5VZG" - }, - "source": [ - "We find that the PLR model overestimates the effect by around 1k; though both sets of results have overlapping confidence intervals." - ] - }, - { - "cell_type": "markdown", - "id": "64", - "metadata": { - "id": "VrBkj_pc5qgm" - }, - "source": [ - "\n", - "Again as before, ideally we would do (semi) cross-fitting with AutoML in order to find good first-stage models and re-run DML with these models. Unfortunately this is not easy to do in R.\n", - "\n", - "As before, in the below analysis of robust inference, we choose Boosted Trees as they perform well in RMSE and accuracy on first-stage models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65", - "metadata": { - "id": "bj67nsgcCDoS", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "iivm_robust_inference <- function(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ,\n", - " drZ, drD, X, Z, D, y, grid, alpha = 0.05) {\n", - " # Inference in the partially linear IV model that is robust to weak identification.\n", - " # grid: grid of theta values to search over when trying to identify the confidence region\n", - " # alpha: confidence level\n", - "\n", - " n <- dim(X)[1]\n", - " thr <- qchisq(1 - alpha, df = 1)\n", - " accept <- c()\n", - "\n", - " for (theta in grid) {\n", - " moment <- drZ - theta * drD\n", - " test <- n * mean(moment)^2 / var(moment)\n", - " if (test <= thr) {\n", - " accept <- c(accept, theta)\n", - " }\n", - " }\n", - "\n", - " return(accept)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66", - "metadata": { - "id": "KqgPk1Jm4sdo", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "grid <- seq(0, 20000, length.out = 10000)\n", - "region <- iivm_robust_inference(point = dml2_results$coef_est, stderr = dml2_results$se,\n", - " yhat = dml2_results$yhat, Dhat = dml2_results$dhat, Zhat = dml2_results$zhat,\n", - " resy = dml2_results$ytil, resD = dml2_results$dtil, resZ = dml2_results$ztil,\n", - " drZ = dml2_results$drZ, drD = dml2_results$drD,\n", - " X = X, Z = Z, D = D, y = y, grid = grid)\n", - "\n", - "# Calculate min and max\n", - "min_region <- min(region)\n", - "max_region <- max(region)\n", - "\n", - "print(min_region)\n", - "print(max_region)" - ] - }, - { - "cell_type": "markdown", - "id": "67", - "metadata": { - "id": "akCGDMZJCN3h" - }, - "source": [ - "We find again that the robust inference confidence region is almost identical to the normal based inference. We are most probably in the strong instrument regime. We can check the t-statistic for the effect of the instrument on the treatment to verify this." - ] - }, - { - "cell_type": "markdown", - "id": "68", - "metadata": { - "id": "01de9f24", - "papermill": { - "duration": 0.010725, - "end_time": "2022-04-19T09:06:51.098483", - "exception": false, - "start_time": "2022-04-19T09:06:51.087758", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# DoubleML package\n", - "\n", - "There exist nice packages out there that can help us do our estimation with the simple call of a function. Such packages include `EconML` (Python) and `DoubleML` (Python and R).\n", - "\n", - "We run through IIVM using `DoubleML` below to illustrate. The `DoubleML` package internally builds on `mlr3`. We use the meta package `mlr3` to generate predictions with machine learning methods. A comprehensive introduction and description of the `mlr3` package is provided in the [mlr3book](https://mlr3book.mlr-org.com/). A list of all learners that you can use in `mlr3` can be found [here](https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html). The entry in the columns *mlr3 Package* and *Packages* indicate which packages must be installed/loaded in your R session.\n", - "\n", - "You find additional information about `DoubleML` on the package on the package website https://docs.doubleml.org/ and the R documentation page https://docs.doubleml.org/r/stable/." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69", - "metadata": { - "id": "2846a36a", - "papermill": { - "duration": 20.239271, - "end_time": "2022-04-19T09:07:11.369618", - "exception": false, - "start_time": "2022-04-19T09:06:51.130347", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "install.packages(\"DoubleML\")\n", - "install.packages(\"mlr3learners\")\n", - "install.packages(\"mlr3\")\n", - "install.packages(\"data.table\")\n", - "install.packages(\"ranger\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "library(DoubleML)\n", - "library(mlr3learners)\n", - "library(mlr3)\n", - "library(data.table)\n", - "library(ranger)" - ] - }, - { - "cell_type": "markdown", - "id": "71", - "metadata": { - "id": "2259ae1c", - "papermill": { - "duration": 0.015455, - "end_time": "2022-04-19T09:12:00.920079", - "exception": false, - "start_time": "2022-04-19T09:12:00.904624", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Local Average Treatment Effects of 401(k) Participation on Net Financial Assets" - ] - }, - { - "cell_type": "markdown", - "id": "72", - "metadata": { - "id": "9c27e413", - "papermill": { - "duration": 0.015158, - "end_time": "2022-04-19T09:12:00.950542", - "exception": false, - "start_time": "2022-04-19T09:12:00.935384", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Interactive IV Model (IIVM)" - ] - }, - { - "cell_type": "markdown", - "id": "73", - "metadata": { - "id": "4fa23c70", - "papermill": { - "duration": 0.015304, - "end_time": "2022-04-19T09:12:00.981285", - "exception": false, - "start_time": "2022-04-19T09:12:00.965981", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Now, we consider estimation of local average treatment effects (LATE) of participation with the binary instrument `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. Here the structural equation model is:\n", - "\n", - "\\begin{align}\n", - "Y :=~& g_0(Z,X) + U, &\\quad E[U\\mid Z,X] = 0,\\\\\n", - "D :=~& r_0(Z,X) + V, &\\quad E[V\\mid Z, X] = 0,\\\\\n", - "Z :=~& m_0(X) + \\zeta, &\\quad E[\\zeta \\mid X] = 0.\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74", - "metadata": { - "id": "cb223b75", - "papermill": { - "duration": 0.06823, - "end_time": "2022-04-19T09:12:01.064957", - "exception": false, - "start_time": "2022-04-19T09:12:00.996727", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Constructing the data (as DoubleMLData)\n", - "formula_flex2 <- paste(\"net_tfa ~ p401+ e401 + poly(age, 6, raw=TRUE) + poly(inc, 8, raw=TRUE) + \",\n", - " \"poly(educ, 4, raw=TRUE) + poly(fsize, 2, raw=TRUE) + male + marr + twoearn + db + pira + hown\")\n", - "model_flex2 <- as.data.table(model.frame(formula_flex2, data))\n", - "x_cols <- colnames(model_flex2)[-c(1, 2, 3)]\n", - "data_iv <- DoubleMLData$new(model_flex2, y_col = \"net_tfa\", d_cols = \"p401\", z_cols = \"e401\", x_cols = x_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75", - "metadata": { - "id": "e652ffad", - "papermill": { - "duration": 20.934595, - "end_time": "2022-04-19T09:12:22.014866", - "exception": false, - "start_time": "2022-04-19T09:12:01.080271", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "lgr::get_logger(\"mlr3\")$set_threshold(\"warn\")\n", - "lasso <- lrn(\"regr.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", - "lasso_class <- lrn(\"classif.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", - "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", - " ml_g = lasso,\n", - " ml_m = lasso_class, ml_r = lasso_class, n_folds = 5, subgroups = list(\n", - " always_takers = FALSE,\n", - " never_takers = TRUE\n", - " )\n", - ")\n", - "dml_mliivm$fit(store_predictions = TRUE)\n", - "dml_mliivm$summary()\n", - "lasso_mliivm <- dml_mliivm$coef\n", - "lasso_std_mliivm <- dml_mliivm$se" - ] - }, - { - "cell_type": "markdown", - "id": "76", - "metadata": { - "id": "63103667", - "papermill": { - "duration": 0.015382, - "end_time": "2022-04-19T09:12:22.045989", - "exception": false, - "start_time": "2022-04-19T09:12:22.030607", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "The confidence interval for the local average treatment effect of participation is given by" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77", - "metadata": { - "id": "322855c4", - "papermill": { - "duration": 0.042067, - "end_time": "2022-04-19T09:12:22.103953", - "exception": false, - "start_time": "2022-04-19T09:12:22.061886", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "dml_mliivm$confint(level = 0.95)" - ] - }, - { - "cell_type": "markdown", - "id": "78", - "metadata": { - "id": "2965410d", - "papermill": { - "duration": 0.015374, - "end_time": "2022-04-19T09:12:22.134875", - "exception": false, - "start_time": "2022-04-19T09:12:22.119501", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Here we can also check the accuracy of the model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79", - "metadata": { - "id": "1476fd27", - "papermill": { - "duration": 0.056054, - "end_time": "2022-04-19T09:12:22.206157", - "exception": false, - "start_time": "2022-04-19T09:12:22.150103", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# variables\n", - "y <- as.matrix(pension$net_tfa) # true observations\n", - "d <- as.matrix(pension$p401)\n", - "z <- as.matrix(pension$e401)\n", - "\n", - "# predictions\n", - "dml_mliivm$params_names()\n", - "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(z=0, X)\n", - "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(z=1, X)\n", - "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", - "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(z=0, X)\n", - "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(z=1, X)\n", - "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", - "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80", - "metadata": { - "id": "444c53f4", - "papermill": { - "duration": 0.056945, - "end_time": "2022-04-19T09:12:22.278593", - "exception": false, - "start_time": "2022-04-19T09:12:22.221648", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# cross-fitted RMSE: outcome\n", - "lasso_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", - "lasso_y_mliivm\n", - "\n", - "# cross-fitted RMSE: treatment\n", - "lasso_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", - "lasso_d_mliivm\n", - "\n", - "# cross-fitted RMSE: instrument\n", - "lasso_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", - "lasso_z_mliivm" - ] - }, - { - "cell_type": "markdown", - "id": "81", - "metadata": { - "id": "a7461966", - "papermill": { - "duration": 0.016468, - "end_time": "2022-04-19T09:12:22.311250", - "exception": false, - "start_time": "2022-04-19T09:12:22.294782", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Again, we repeat the procedure for the other machine learning methods:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82", - "metadata": { - "id": "59YzwIcpEnyV", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# needed to run boosting\n", - "remotes::install_github(\"mlr-org/mlr3extralearners\")\n", - "install.packages(\"mlr3extralearners\")\n", - "install.packages(\"mboost\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "library(mlr3extralearners)\n", - "library(mboost)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84", - "metadata": { - "id": "Ec0g3ch3EjAl", - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Forest\n", - "randomForest <- lrn(\"regr.ranger\")\n", - "random_forest_class <- lrn(\"classif.ranger\")\n", - "\n", - "# Trees\n", - "trees <- lrn(\"regr.rpart\")\n", - "trees_class <- lrn(\"classif.rpart\")\n", - "\n", - "# Boosting\n", - "boost <- lrn(\"regr.glmboost\")\n", - "boost_class <- lrn(\"classif.glmboost\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85", - "metadata": { - "id": "3935dfc5", - "papermill": { - "duration": 69.414354, - "end_time": "2022-04-19T09:13:31.742249", - "exception": false, - "start_time": "2022-04-19T09:12:22.327895", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "### random forest ###\n", - "\n", - "lgr::get_logger(\"mlr3\")$set_threshold(\"warn\")\n", - "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", - " ml_g = randomForest,\n", - " ml_m = random_forest_class, ml_r = random_forest_class, n_folds = 3, subgroups = list(\n", - " always_takers = FALSE,\n", - " never_takers = TRUE\n", - " )\n", - ")\n", - "dml_mliivm$fit(store_predictions = TRUE)\n", - "dml_mliivm$summary()\n", - "forest_mliivm <- dml_mliivm$coef\n", - "forest_std_mliivm <- dml_mliivm$se\n", - "\n", - "# predictions\n", - "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(Z=0, X)\n", - "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(Z=1, X)\n", - "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", - "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(Z=0, X)\n", - "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(Z=1, X)\n", - "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", - "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o\n", - "\n", - "# cross-fitted RMSE: outcome\n", - "forest_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", - "forest_y_mliivm\n", - "\n", - "# cross-fitted RMSE: treatment\n", - "forest_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", - "forest_d_mliivm\n", - "\n", - "# cross-fitted RMSE: instrument\n", - "forest_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", - "forest_z_mliivm\n", - "\n", - "### trees ###\n", - "\n", - "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", - " ml_g = trees,\n", - " ml_m = trees_class, ml_r = trees_class, n_folds = 3, subgroups = list(\n", - " always_takers = FALSE,\n", - " never_takers = TRUE\n", - " )\n", - ")\n", - "dml_mliivm$fit(store_predictions = TRUE)\n", - "dml_mliivm$summary()\n", - "tree_mliivm <- dml_mliivm$coef\n", - "tree_std_mliivm <- dml_mliivm$se\n", - "\n", - "# predictions\n", - "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(Z=0, X)\n", - "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(Z=1, X)\n", - "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", - "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(Z=0, X)\n", - "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(Z=1, X)\n", - "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", - "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o\n", - "\n", - "# cross-fitted RMSE: outcome\n", - "tree_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", - "tree_y_mliivm\n", - "\n", - "# cross-fitted RMSE: treatment\n", - "tree_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", - "tree_d_mliivm\n", - "\n", - "# cross-fitted RMSE: instrument\n", - "tree_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", - "tree_z_mliivm\n", - "\n", - "\n", - "### boosting ###\n", - "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", - " ml_g = boost,\n", - " ml_m = boost_class, ml_r = boost_class, n_folds = 3, subgroups = list(\n", - " always_takers = FALSE,\n", - " never_takers = TRUE\n", - " )\n", - ")\n", - "dml_mliivm$fit(store_predictions = TRUE)\n", - "dml_mliivm$summary()\n", - "boost_mliivm <- dml_mliivm$coef\n", - "boost_std_mliivm <- dml_mliivm$se\n", - "\n", - "# predictions\n", - "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(Z=0, X)\n", - "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(Z=1, X)\n", - "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", - "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(Z=0, X)\n", - "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(Z=1, X)\n", - "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", - "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o\n", - "\n", - "# cross-fitted RMSE: outcome\n", - "boost_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", - "boost_y_mliivm\n", - "\n", - "# cross-fitted RMSE: treatment\n", - "boost_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", - "boost_d_mliivm\n", - "\n", - "# cross-fitted RMSE: instrument\n", - "boost_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", - "boost_z_mliivm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86", - "metadata": { - "id": "7187fc74", - "papermill": { - "duration": 0.061872, - "end_time": "2022-04-19T09:13:31.824148", - "exception": false, - "start_time": "2022-04-19T09:13:31.762276", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": { + "id": "0", + "papermill": { + "duration": 0.012988, + "end_time": "2022-04-19T09:06:48.772902", + "exception": false, + "start_time": "2022-04-19T09:06:48.759914", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Inference on Predictive and Causal Effects in High-Dimensional Nonlinear Models" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": { + "id": "1", + "papermill": { + "duration": 0.009437, + "end_time": "2022-04-19T09:06:48.791895", + "exception": false, + "start_time": "2022-04-19T09:06:48.782458", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Impact of 401(k) on Financial Wealth\n", + "\n", + "We consider estimation of the effect of 401(k) participation\n", + "on accumulated assets. 401(k) plans are pension accounts sponsored by employers. The key problem in determining the effect of participation in 401(k) plans on accumulated assets is saver heterogeneity coupled with the fact that the decision to enroll in a 401(k) is non-random. It is generally recognized that some people have a higher preference for saving than others. It also seems likely that those individuals with high unobserved preference for saving would be most likely to choose to participate in tax-advantaged retirement savings plans and would tend to have otherwise high amounts of accumulated assets. The presence of unobserved savings preferences with these properties then implies that conventional estimates that do not account for saver heterogeneity and endogeneity of participation will be biased upward, tending to overstate the savings effects of 401(k) participation.\n", + "\n", + "One can argue that eligibility for enrolling in a 401(k) plan in this data can be taken as exogenous after conditioning on a few observables of which the most important for their argument is income. The basic idea is that, at least around the time 401(k)’s initially became available, people were unlikely to be basing their employment decisions on whether an employer offered a 401(k) but would instead focus on income and other aspects of the job." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": { + "id": "2", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "install.packages(\"xtable\")\n", + "install.packages(\"hdm\")\n", + "install.packages(\"sandwich\")\n", + "install.packages(\"ggplot2\")\n", + "install.packages(\"randomForest\")\n", + "install.packages(\"glmnet\")\n", + "install.packages(\"rpart\")\n", + "install.packages(\"data.table\")\n", + "install.packages(\"gbm\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": { + "vscode": { + "languageId": "r" + }, + "id": "3" + }, + "outputs": [], + "source": [ + "library(xtable)\n", + "library(hdm)\n", + "library(sandwich)\n", + "library(ggplot2)\n", + "library(randomForest)\n", + "library(data.table)\n", + "library(glmnet)\n", + "library(rpart)\n", + "library(gbm)\n", + "\n", + "set.seed(123)" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": { + "id": "4", + "papermill": { + "duration": 0.009588, + "end_time": "2022-04-19T09:06:48.810853", + "exception": false, + "start_time": "2022-04-19T09:06:48.801265", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Data\n", + "\n", + "The raw dataset can be found [here](https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/401k.csv).\n", + "The data set can be loaded from the `hdm` package for R directly by typing:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": { + "id": "5", + "papermill": { + "duration": 0.46397, + "end_time": "2022-04-19T09:06:49.283933", + "exception": false, + "start_time": "2022-04-19T09:06:48.819963", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "data(pension)\n", + "data <- pension\n", + "dim(data)" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": { + "id": "6", + "papermill": { + "duration": 0.009462, + "end_time": "2022-04-19T09:06:49.302928", + "exception": false, + "start_time": "2022-04-19T09:06:49.293466", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "See the \"Details\" section on the description of the data set, which can be accessed by\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": { + "id": "7", + "papermill": { + "duration": 0.35227, + "end_time": "2022-04-19T09:06:49.664810", + "exception": false, + "start_time": "2022-04-19T09:06:49.312540", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "help(pension)" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": { + "id": "8", + "papermill": { + "duration": 0.009357, + "end_time": "2022-04-19T09:06:49.683784", + "exception": false, + "start_time": "2022-04-19T09:06:49.674427", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The data consist of 9,915 observations at the household level drawn from the 1991 Survey of Income and Program Participation (SIPP). All the variables are referred to 1990. We use net financial assets (*net\\_tfa*) as the outcome variable, $Y$, in our analysis. The net financial assets are computed as the sum of IRA balances, 401(k) balances, checking accounts, saving bonds, other interest-earning accounts, other interest-earning assets, stocks, and mutual funds less non mortgage debts." + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": { + "id": "9", + "papermill": { + "duration": 0.009242, + "end_time": "2022-04-19T09:06:49.702401", + "exception": false, + "start_time": "2022-04-19T09:06:49.693159", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Among the $9915$ individuals, $3682$ are eligible to participate in the program. The variable *e401* indicates eligibility and *p401* indicates participation, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": { + "id": "10", + "papermill": { + "duration": 0.618528, + "end_time": "2022-04-19T09:06:50.330218", + "exception": false, + "start_time": "2022-04-19T09:06:49.711690", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "hist_e401 <- ggplot(data, aes(x = e401, fill = factor(e401))) +\n", + " geom_bar()\n", + "hist_e401" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": { + "id": "11", + "papermill": { + "duration": 0.009686, + "end_time": "2022-04-19T09:06:50.349766", + "exception": false, + "start_time": "2022-04-19T09:06:50.340080", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Eligibility is highly associated with financial wealth:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": { + "id": "12", + "papermill": { + "duration": 0.554613, + "end_time": "2022-04-19T09:06:50.914133", + "exception": false, + "start_time": "2022-04-19T09:06:50.359520", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "dens_net_tfa <- ggplot(data, aes(x = net_tfa, color = factor(e401), fill = factor(e401))) +\n", + " geom_density() +\n", + " xlim(c(-20000, 150000)) +\n", + " facet_wrap(. ~ e401)\n", + "\n", + "dens_net_tfa" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": { + "id": "13", + "papermill": { + "duration": 0.010335, + "end_time": "2022-04-19T09:06:50.935024", + "exception": false, + "start_time": "2022-04-19T09:06:50.924689", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The unconditional APE of e401 is about $19559$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": { + "id": "14", + "papermill": { + "duration": 0.038096, + "end_time": "2022-04-19T09:06:50.983602", + "exception": false, + "start_time": "2022-04-19T09:06:50.945506", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "e1 <- data[data$e401 == 1, ]\n", + "e0 <- data[data$e401 == 0, ]\n", + "round(mean(e1$net_tfa) - mean(e0$net_tfa), 0)" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": { + "id": "15", + "papermill": { + "duration": 0.01047, + "end_time": "2022-04-19T09:06:51.004618", + "exception": false, + "start_time": "2022-04-19T09:06:50.994148", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Among the $3682$ individuals that are eligible, $2594$ decided to participate in the program. The unconditional APE of p401 is about $27372$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": { + "id": "16", + "papermill": { + "duration": 0.039305, + "end_time": "2022-04-19T09:06:51.054616", + "exception": false, + "start_time": "2022-04-19T09:06:51.015311", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "p1 <- data[data$p401 == 1, ]\n", + "p0 <- data[data$p401 == 0, ]\n", + "round(mean(p1$net_tfa) - mean(p0$net_tfa), 0)" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": { + "id": "17", + "papermill": { + "duration": 0.010831, + "end_time": "2022-04-19T09:06:51.076114", + "exception": false, + "start_time": "2022-04-19T09:06:51.065283", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As discussed, these estimates are biased since they do not account for saver heterogeneity and endogeneity of participation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": { + "id": "18", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# instrument variable\n", + "Z <- data[, \"e401\"]\n", + "# treatment variable\n", + "D <- data[, \"p401\"]\n", + "# outcome variable\n", + "y <- data[, \"net_tfa\"]" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": { + "id": "19" + }, + "source": [ + "### We construct the engineered features for controls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": { + "id": "20", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Constructing the controls\n", + "x_formula <- paste(\"~ poly(age, 6, raw=TRUE) + poly(inc, 8, raw=TRUE) + poly(educ, 4, raw=TRUE) + \",\n", + " \"poly(fsize, 2, raw=TRUE) + male + marr + twoearn + db + pira + hown\")\n", + "X <- as.data.table(model.frame(x_formula, pension))\n", + "head(X)" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": { + "id": "21" + }, + "source": [ + "# Instrumental Variables: Effect of 401k Participation on Financial Assets" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": { + "id": "22" + }, + "source": [ + "## Double ML IV under Partial Linearity" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": { + "id": "23" + }, + "source": [ + "Now, we consider estimation of average treatment effects of participation in 401k, i.e. `p401`, with the binary instrument being eligibility in 401k, i.e. `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. We consider a partially linear structural equation model:\n", + "\\begin{align}\n", + "Y :=~& g_Y(\\epsilon_Y) D + f_Y(A, X, \\epsilon_Y), \\\\\n", + "D :=~& f_D(Z, X, A, \\epsilon_D), \\\\\n", + "Z :=~& f_Z(X, \\epsilon_Z),\\\\\n", + "A :=~& f_A(X, \\epsilon_A), \\\\\n", + "X :=~& \\epsilon_X,\n", + "\\end{align}\n", + "where $A$ is a vector of un-observed confounders.\n", + "\n", + "Under this structural equation model, the average treatment effect:\n", + "\\begin{align}\n", + "\\alpha = E[Y(1) - Y(0)]\n", + "\\end{align}\n", + "can be identified by the moment restriction:\n", + "\\begin{align}\n", + "E[(\\tilde{Y} - \\alpha \\tilde{D}) \\tilde{Z}] = 0\n", + "\\end{align}\n", + "where for any variable $V$, we denote with $\\tilde{V} = V - E[V|X]$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": { + "id": "24", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "set.seed(1)\n", + "# family gaussian means that we'll be using square loss\n", + "yfit_lasso_cv <- cv.glmnet(as.matrix(X), y, family = \"gaussian\", alpha = 1)\n", + "# family gaussian means that we'll be using square loss\n", + "dfit_lasso_cv <- cv.glmnet(as.matrix(X), D, family = \"gaussian\", alpha = 1)\n", + "# family gaussian means that we'll be using square loss\n", + "zfit_lasso_cv <- cv.glmnet(as.matrix(X), Z, family = \"gaussian\", alpha = 1)\n", + "\n", + "\n", + "yhat_lasso_cv <- predict(yfit_lasso_cv, newx = as.matrix(X)) # predictions\n", + "dhat_lasso_cv <- predict(dfit_lasso_cv, newx = as.matrix(X)) # predictions\n", + "zhat_lasso_cv <- predict(zfit_lasso_cv, newx = as.matrix(X)) # predictions\n", + "\n", + "resy <- y - yhat_lasso_cv\n", + "resD <- D - dhat_lasso_cv\n", + "resZ <- Z - zhat_lasso_cv\n", + "\n", + "# Estimate\n", + "mean(resy * resZ) / mean(resZ * resD)" + ] + }, + { + "cell_type": "markdown", + "id": "25", + "metadata": { + "id": "25" + }, + "source": [ + "Recall if we want to do inference, we need to either use the theoretically driven penalty paramter for Lasso or perform cross-fitting." + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": { + "id": "26" + }, + "source": [ + "### DML with Non-Linear ML Models and Cross-fitting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27", + "metadata": { + "id": "27", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML for PLIVM with D and Z as classifiers or regressors\n", + "dml2_for_plivm <- function(x, d, z, y, dreg, yreg, zreg, nfold = 5, method = \"regression\") {\n", + " nobs <- nrow(x)\n", + " foldid <- rep.int(1:nfold, times = ceiling(nobs / nfold))[sample.int(nobs)]\n", + " I <- split(1:nobs, foldid)\n", + " # create residualized objects to fill\n", + " ytil <- dtil <- ztil <- rep(NA, nobs)\n", + " # obtain cross-fitted residuals\n", + " cat(\"fold: \")\n", + " for (b in seq_along(I)) {\n", + " if (method == \"randomforest\") {\n", + " # take a fold out\n", + " dfit <- dreg(x[-I[[b]], ], d[-I[[b]]])\n", + " zfit <- zreg(x[-I[[b]], ], z[-I[[b]]])\n", + " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]])\n", + " # predict the fold out\n", + " dhat <- predict(dfit, x[I[[b]], ], type = \"prob\")[, 2] # type = \"prob\" is like predict_proba in scikitlearn\n", + " zhat <- predict(zfit, x[I[[b]], ], type = \"prob\")[, 2]\n", + " # default type = \"response\" for regression for RF, type = \"vector\" for regression for Decision Trees\n", + " yhat <- predict(yfit, x[I[[b]], ])\n", + " # record residual\n", + " # as.numeric will turn d = as.factor(d) from 0,1 to 1,2 so subtract 1!\n", + " dtil[I[[b]]] <- (as.numeric(d[I[[b]]]) - 1 - dhat)\n", + " ztil[I[[b]]] <- (as.numeric(z[I[[b]]]) - 1 - zhat)\n", + " ytil[I[[b]]] <- (y[I[[b]]] - yhat)\n", + " } else if (method == \"regression\") { # works for both boosted trees and glmnet\n", + " # take a fold out\n", + " dfit <- dreg(x[-I[[b]], ], d[-I[[b]]])\n", + " zfit <- zreg(x[-I[[b]], ], z[-I[[b]]])\n", + " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]])\n", + " # predict the fold out\n", + " dhat <- predict(dfit, x[I[[b]], ], type = \"response\")\n", + " zhat <- predict(zfit, x[I[[b]], ], type = \"response\")\n", + " yhat <- predict(yfit, x[I[[b]], ], type = \"response\")\n", + " # record residual\n", + " dtil[I[[b]]] <- (d[I[[b]]] - dhat)\n", + " ztil[I[[b]]] <- (z[I[[b]]] - zhat)\n", + " ytil[I[[b]]] <- (y[I[[b]]] - yhat)\n", + " } else if (method == \"decisiontrees\") {\n", + " # take a fold out\n", + " dfit <- dreg(x[-I[[b]], ], as.factor(d)[-I[[b]]])\n", + " zfit <- zreg(x[-I[[b]], ], as.factor(z)[-I[[b]]])\n", + " yfit <- yreg(x[-I[[b]], ], y[-I[[b]]])\n", + " # predict the fold out\n", + " dhat <- predict(dfit, x[I[[b]], ])[, 2]\n", + " zhat <- predict(zfit, x[I[[b]], ])[, 2]\n", + " yhat <- predict(yfit, x[I[[b]], ])\n", + " # record residual\n", + " dtil[I[[b]]] <- (d[I[[b]]] - dhat)\n", + " ztil[I[[b]]] <- (z[I[[b]]] - zhat)\n", + " ytil[I[[b]]] <- (y[I[[b]]] - yhat)\n", + " }\n", + "\n", + " cat(b, \" \")\n", + " }\n", + " ivfit <- tsls(y = ytil, d = dtil, x = NULL, z = ztil, intercept = FALSE)\n", + " coef_est <- ivfit$coef # extract coefficient\n", + " se <- ivfit$se # record standard error\n", + " cat(sprintf(\"\\ncoef (se) = %g (%g)\\n\", coef_est, se))\n", + "\n", + " return(list(coef_est = coef_est, se = se, dtil = dtil, ytil = ytil, ztil = ztil))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28", + "metadata": { + "id": "28", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "summary_for_plivm <- function(point, stderr, resy, resD, resZ, name) {\n", + " data <- data.frame(\n", + " estimate = point, # point estimate\n", + " stderr = stderr, # standard error\n", + " lower = point - 1.96 * stderr, # lower end of 95% confidence interval\n", + " upper = point + 1.96 * stderr, # upper end of 95% confidence interval\n", + " `rmse y` = sqrt(mean(resy^2)), # RMSE of model that predicts outcome y\n", + " `rmse D` = sqrt(mean(resD^2)), # RMSE of model that predicts treatment D\n", + " `rmse Z` = sqrt(mean(resZ^2)), # RMSE of model that predicts treatment D\n", + " `accuracy D` = mean(abs(resD) < 0.5), # binary classification accuracy of model for D\n", + " `accuracy Z` = mean(abs(resZ) < 0.5) # binary classification accuracy of model for Z\n", + " )\n", + " rownames(data) <- name\n", + " return(data)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "29", + "metadata": { + "id": "29" + }, + "source": [ + "#### Double Lasso with Cross-Fitting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "metadata": { + "id": "30", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with LassoCV\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Lasso CV \\n\"))\n", + "\n", + "dreg_lasso_cv <- function(x, d) {\n", + " cv.glmnet(x, d, family = \"gaussian\", alpha = 1, nfolds = 5)\n", + "}\n", + "yreg_lasso_cv <- function(x, y) {\n", + " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", + "}\n", + "zreg_lasso_cv <- function(x, z) {\n", + " cv.glmnet(x, z, family = \"gaussian\", alpha = 1, nfolds = 5)\n", + "}\n", + "\n", + "dml2_results <- dml2_for_plivm(as.matrix(X), D, Z, y, dreg_lasso_cv, yreg_lasso_cv, zreg_lasso_cv,\n", + " nfold = 5, method = \"regression\")\n", + "sum_lasso_cv <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", + " dml2_results$ztil, name = \"LassoCV\")\n", + "tableplr <- data.frame()\n", + "tableplr <- rbind(sum_lasso_cv)\n", + "tableplr\n", + "\n", + "ytil_lasso <- dml2_results$ytil\n", + "dtil_lasso <- dml2_results$dtil\n", + "ztil_lasso <- dml2_results$ztil" + ] + }, + { + "cell_type": "markdown", + "id": "31", + "metadata": { + "id": "31" + }, + "source": [ + "#### Using a $\\ell_2$ Penalized Logistic Regression for D and Z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32", + "metadata": { + "id": "32", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Lasso/Ridge\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Lasso/Logistic \\n\"))\n", + "\n", + "dreg_ridge_cv <- function(x, d) {\n", + " cv.glmnet(x, d, family = \"binomial\", alpha = 0, nfolds = 5)\n", + "}\n", + "yreg_ridge_cv <- function(x, y) {\n", + " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", + "}\n", + "zreg_ridge_cv <- function(x, z) {\n", + " cv.glmnet(x, z, family = \"binomial\", alpha = 0, nfolds = 5)\n", + "}\n", + "\n", + "dml2_results <- dml2_for_plivm(as.matrix(X), D, Z, y, dreg_ridge_cv, yreg_ridge_cv, zreg_ridge_cv,\n", + " nfold = 5, method = \"regression\")\n", + "sum_lasso_ridge_cv <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", + " dml2_results$ztil, name = \"LassoCV/LogisticCV\")\n", + "tableplr <- rbind(tableplr, sum_lasso_ridge_cv)\n", + "tableplr\n", + "\n", + "ytil_ridge <- dml2_results$ytil\n", + "dtil_ridge <- dml2_results$dtil\n", + "ztil_ridge <- dml2_results$ztil" + ] + }, + { + "cell_type": "markdown", + "id": "33", + "metadata": { + "id": "33" + }, + "source": [ + "### Random Forests" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34", + "metadata": { + "id": "34", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Random Forest\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Random Forest \\n\"))\n", + "\n", + "dreg_rf <- function(x, d) {\n", + " randomForest(x, d, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "yreg_rf <- function(x, y) {\n", + " randomForest(x, y, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "zreg_rf <- function(x, z) {\n", + " randomForest(x, z, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "\n", + "dml2_results <- dml2_for_plivm(as.matrix(X), as.factor(D), as.factor(Z), y, dreg_rf, yreg_rf, zreg_rf,\n", + " nfold = 5, method = \"randomforest\")\n", + "sum_rf <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", + " dml2_results$ztil, name = \"RF\")\n", + "tableplr <- rbind(tableplr, sum_rf)\n", + "tableplr\n", + "\n", + "ytil_rf <- dml2_results$ytil\n", + "dtil_rf <- dml2_results$dtil\n", + "ztil_rf <- dml2_results$ztil" + ] + }, + { + "cell_type": "markdown", + "id": "35", + "metadata": { + "id": "35" + }, + "source": [ + "### Decision Trees" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36", + "metadata": { + "id": "36", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Decision Trees\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Decision Trees \\n\"))\n", + "\n", + "# decision tree takes in X as dataframe, not matrix/array\n", + "dreg_tr <- function(x, d) {\n", + " rpart(as.formula(\"D~.\"), cbind(data.frame(D = d), x), method = \"class\", minbucket = 10, cp = 0.001)\n", + "}\n", + "yreg_tr <- function(x, y) {\n", + " rpart(as.formula(\"y~.\"), cbind(data.frame(y = y), x), minbucket = 10, cp = 0.001)\n", + "}\n", + "zreg_tr <- function(x, z) {\n", + " rpart(as.formula(\"Z~.\"), cbind(data.frame(Z = z), x), method = \"class\", minbucket = 10, cp = 0.001)\n", + "}\n", + "\n", + "dml2_results <- dml2_for_plivm(X, D, Z, y, dreg_tr, yreg_tr, zreg_tr,\n", + " nfold = 5, method = \"decisiontrees\")\n", + "sum_tr <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", + " dml2_results$ztil, name = \"Decision Trees\")\n", + "tableplr <- rbind(tableplr, sum_tr)\n", + "tableplr\n", + "\n", + "ytil_tr <- dml2_results$ytil\n", + "dtil_tr <- dml2_results$dtil\n", + "ztil_tr <- dml2_results$ztil" + ] + }, + { + "cell_type": "markdown", + "id": "37", + "metadata": { + "id": "37" + }, + "source": [ + "### Boosted Trees" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38", + "metadata": { + "id": "38", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Boosted Trees\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Boosted Trees \\n\"))\n", + "\n", + "# NB: early stopping cannot easily be implemented with gbm\n", + "## set n.trees = best, where best <- gbm.perf(dreg_boost, plot.it = FALSE)\n", + "dreg_boost <- function(x, d) {\n", + " gbm(as.formula(\"D~.\"), cbind(data.frame(D = d), x), distribution = \"bernoulli\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "yreg_boost <- function(x, y) {\n", + " gbm(as.formula(\"y~.\"), cbind(data.frame(y = y), x), distribution = \"gaussian\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "zreg_boost <- function(x, z) {\n", + " gbm(as.formula(\"Z~.\"), cbind(data.frame(Z = z), x), distribution = \"bernoulli\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "\n", + "# passing these through regression as type=\"response\", and D and Z should not be factors!\n", + "dml2_results <- dml2_for_plivm(X, D, Z, y, dreg_boost, yreg_boost, zreg_boost,\n", + " nfold = 5, method = \"regression\")\n", + "sum_boost <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", + " dml2_results$ztil, name = \"Boosted Trees\")\n", + "tableplr <- rbind(tableplr, sum_boost)\n", + "tableplr\n", + "\n", + "ytil_boost <- dml2_results$ytil\n", + "dtil_boost <- dml2_results$dtil\n", + "ztil_boost <- dml2_results$ztil" + ] + }, + { + "cell_type": "markdown", + "id": "39", + "metadata": { + "id": "39" + }, + "source": [ + "## Ensembles" + ] + }, + { + "cell_type": "markdown", + "id": "40", + "metadata": { + "id": "40" + }, + "source": [ + "Boosted trees give the best RMSE for Y, D, and Z, so the ensemble based on choosing the best performing prediction rule is identical to boosting in this case." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41", + "metadata": { + "id": "41", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Best fit is boosted trees for D, Z, Y\n", + "\n", + "sum_best <- summary_for_plivm(dml2_results$coef_est, dml2_results$se, dml2_results$ytil, dml2_results$dtil,\n", + " dml2_results$ztil, name = \"Best\")\n", + "tableplr <- rbind(tableplr, sum_best)\n", + "tableplr" + ] + }, + { + "cell_type": "markdown", + "id": "42", + "metadata": { + "id": "42" + }, + "source": [ + "We'll form a model average with unconstrained least squares weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43", + "metadata": { + "id": "43", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Least squares model average\n", + "\n", + "dhat_lasso <- D - dtil_lasso\n", + "dhat_ridge <- D - dtil_ridge\n", + "dhat_rf <- D - dtil_rf\n", + "dhat_tr <- D - dtil_tr\n", + "dhat_boost <- D - dtil_boost\n", + "\n", + "yhat_lasso <- y - ytil_lasso\n", + "yhat_ridge <- y - ytil_ridge\n", + "yhat_rf <- y - ytil_rf\n", + "yhat_tr <- y - ytil_tr\n", + "yhat_boost <- y - ytil_boost\n", + "\n", + "zhat_lasso <- Z - ztil_lasso\n", + "zhat_ridge <- Z - ztil_ridge\n", + "zhat_rf <- Z - ztil_rf\n", + "zhat_tr <- Z - ztil_tr\n", + "zhat_boost <- Z - ztil_boost\n", + "\n", + "ma_dtil <- lm(D ~ dhat_lasso + dhat_ridge + dhat_rf + dhat_tr + dhat_boost)$residuals\n", + "ma_ytil <- lm(y ~ yhat_lasso + yhat_ridge + yhat_rf + yhat_tr + yhat_boost)$residuals\n", + "ma_ztil <- lm(Z ~ zhat_lasso + zhat_ridge + zhat_rf + zhat_tr + zhat_boost)$residuals\n", + "\n", + "ivfit <- tsls(y = ma_ytil, d = ma_dtil, x = NULL, z = ma_ztil, intercept = FALSE)\n", + "coef_est <- ivfit$coef # extract coefficient\n", + "se <- ivfit$se # record standard error\n", + "\n", + "sum_ma <- summary_for_plivm(coef_est, se, ma_ytil, ma_dtil, ma_ztil, name = \"Model Average\")\n", + "tableplr <- rbind(tableplr, sum_ma)\n", + "tableplr" + ] + }, + { + "cell_type": "markdown", + "id": "44", + "metadata": { + "id": "44" + }, + "source": [ + "## Inference Robust to Weak Identification\n", + "\n", + "Now we turn toward robustness when the instrument is weak.\n", + "\n", + "Ideally, we would do (semi) cross-fitting with AutoML in order to find good first-stage models and re-run DML with these models. Unfortunately this is not easy to do in R. In the case of semi cross-fitting, we can use R's H20 AutoML trained on the entire training set $y\\sim X$, $D \\sim X$, $Z\\sim X$ to determine the best model (eg ensemble), but H20 does not allow you to extract the best model so we can re-use that in DML.\n", + "\n", + "Thus, in the below analysis of robust inference, we choose Boosted Trees as they perform well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45", + "metadata": { + "id": "45", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "robust_inference <- function(point, stderr, resD, resy, resZ, grid, alpha = 0.05) {\n", + " # Inference in the partially linear IV model that is robust to weak identification.\n", + " # grid: grid of theta values to search over when trying to identify the confidence region\n", + " # alpha: confidence level\n", + "\n", + " n <- dim(X)[1]\n", + " thr <- qchisq(1 - alpha, df = 1)\n", + " accept <- c()\n", + "\n", + " for (theta in grid) {\n", + " moment <- (resy - theta * resD) * resZ\n", + " test <- n * mean(moment)^2 / var(moment)\n", + " if (test <= thr) {\n", + " accept <- c(accept, theta)\n", + " }\n", + " }\n", + "\n", + " return(accept)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46", + "metadata": { + "id": "46", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "grid <- seq(0, 20000, length.out = 10000)\n", + "region <- robust_inference(dml2_results$coef_est, dml2_results$stderr, dml2_results$dtil, dml2_results$ytil,\n", + " dml2_results$ztil, grid = grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47", + "metadata": { + "id": "47", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "grid <- seq(0, 20000, length.out = 10000)\n", + "region <- robust_inference(dml2_results$coef_est, dml2_results$stderr, dml2_results$dtil, dml2_results$ytil,\n", + " dml2_results$ztil, grid = grid)\n", + "# Calculate min and max\n", + "min_region <- min(region)\n", + "max_region <- max(region)\n", + "\n", + "print(min_region)\n", + "print(max_region)" + ] + }, + { + "cell_type": "markdown", + "id": "48", + "metadata": { + "id": "48" + }, + "source": [ + "# Interactive IV Model and LATE" + ] + }, + { + "cell_type": "markdown", + "id": "49", + "metadata": { + "id": "49" + }, + "source": [ + "Now, we consider estimation of local average treatment effects (LATE) of participation `p401`, with the binary instrument `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. Here the structural equation model is:\n", + "\\begin{align}\n", + "Y :=~& f_Y (D, X, A, \\epsilon_Y) \\\\\n", + "D :=~& f_D(Z, X, A, \\epsilon_D) \\in \\{0,1\\}, \\\\\n", + "Z :=~& f_Z(X,\\epsilon_Z) \\in \\{0,1\\}, \\\\\n", + "X :=~& \\epsilon_X, \\quad A = \\epsilon_A,\n", + "\\end{align}\n", + "where $\\epsilon$'s are all exogenous and independent,\n", + "and\n", + "$$\n", + "z \\mapsto f_D(z , A, X, \\epsilon_D) \\text{ is weakly increasing (weakly monotone)}.\n", + "$$\n", + "and $A$ is a vector of unobserved confounders. Note that in our setting monotonicity is satisfied, since participation is only feasible when it is eligible. Thus we have that $D=0$ whenever $Z=0$. Thus it can only be that $f_D(1, A, X, \\epsilon_D) \\geq 0 = f_D(0, A, X, \\epsilon_D)$.\n", + "\n", + "In this case, we can estimate the local average treatment effect (LATE):\n", + "$$\n", + "\\alpha = E[Y(1) - Y(0) | D(1) > D(0)]\n", + "$$\n", + "This can be identified using the Neyman orthogonal moment equation:\n", + "\\begin{align}\n", + "E\\left[g(1, X) - g(0, X) + H(Z) (Y - g(Z, X)) - \\alpha \\cdot (m(1, X) - m(0, X) + H(Z) (D - m(Z, X))\\right] = 0\n", + "\\end{align}\n", + "where\n", + "\\begin{align}\n", + "g(Z,X) =~& E[Y|Z,X],\\\\\n", + "m(Z,X) =~& E[D|Z,X],\\\\\n", + "H(Z) =~& \\frac{Z}{Pr(Z=1|X)} - \\frac{1 - Z}{1 - Pr(Z=1|X)}\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50", + "metadata": { + "id": "50", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "get_dhat0 <- function(XZ0, DZ0, Xb, dreg0, type = NULL, DZ0factor = NULL) {\n", + " # train a treatment model on training data that received Z=0 and predict treatment on all data in test set\n", + " if (mean(DZ0) > 0) { # it could be that D=0, whenever Z=0 deterministically\n", + " dreg0_ <- dreg0\n", + " if (is.null(DZ0factor)) {\n", + " dfit0 <- dreg0_((XZ0), DZ0)\n", + " } else {\n", + " dfit0 <- dreg0_((XZ0), DZ0factor)\n", + " }\n", + " if (is.null(type)) {\n", + " return(predict(dfit0, (Xb))[, 2])\n", + " } else if (type == \"prob\") {\n", + " return(predict(dfit0, (Xb), type = \"prob\")[, 2])\n", + " } else if (type == \"reponse\") {\n", + " return(predict(dfit0, (Xb), type = \"response\"))\n", + " } else {\n", + " stop(\"Invalid argument `type`.\")\n", + " }\n", + " } else {\n", + " return(0)\n", + " }\n", + "}\n", + "\n", + "get_dhat1 <- function(XZ1, DZ1, Xb, dreg1, type = NULL, DZ1factor = NULL) {\n", + " # train a treamtent model on training data that received Z=1 and predict treatment on all data in test set\n", + " if (mean(DZ1) < 1) { # it could be that D=1, whenever Z=1 deterministically\n", + " dreg1_ <- dreg1\n", + " if (is.null(DZ1factor)) {\n", + " dfit1 <- dreg1_((XZ1), DZ1)\n", + " } else {\n", + " dfit1 <- dreg1_((XZ1), DZ1factor)\n", + " }\n", + " if (is.null(type)) {\n", + " return(predict(dfit1, (Xb))[, 2])\n", + " } else if (type == \"prob\") {\n", + " return(predict(dfit1, (Xb), type = \"prob\")[, 2])\n", + " } else if (type == \"response\") {\n", + " return(predict(dfit1, (Xb), type = \"response\"))\n", + " } else {\n", + " stop(\"Invalid argument `type`.\")\n", + " }\n", + " } else {\n", + " return(1)\n", + " }\n", + "}\n", + "\n", + "# DML for IIVM with D and Z as classifiers or regressors\n", + "dml2_for_iivm <- function(x, d, z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", + " trimming = 0.01, nfold = 5, method = \"classification\", dt = 0, bt = 0) {\n", + " # this implements DML2 algorithm, where there moments are estimated via DML, before constructing\n", + " # the pooled estimate of theta randomly split data into folds\n", + "\n", + " ## NB This method has many if statements to accommodate the various estimators we will use.\n", + " ## Unlike Python's sklearn, all methods have idfferent default arguments in their predict functions.\n", + " ## See official R documentation for details.\n", + "\n", + " yhat0 <- rep(0, length(y))\n", + " yhat1 <- rep(0, length(y))\n", + " dhat0 <- rep(0, length(d))\n", + " dhat1 <- rep(0, length(d))\n", + " zhat <- rep(0, length(Z))\n", + "\n", + " nobs <- nrow(X)\n", + " foldid <- rep.int(1:nfold, times = ceiling(nobs / nfold))[sample.int(nobs)]\n", + " I <- split(1:nobs, foldid)\n", + " # create residualized objects to fill\n", + " ytil <- dtil <- ztil <- rep(NA, nobs)\n", + "\n", + " # obtain cross-fitted residuals\n", + " cat(\"fold: \")\n", + " for (b in seq_along(I)) {\n", + " # define helpful variables\n", + " Xb <- X[I[[b]], ]\n", + " Xnotb <- X[-I[[b]], ]\n", + " Znotb <- Z[-I[[b]]]\n", + "\n", + " # training dfs subsetted on the -I[[b]] fold\n", + " XZ0 <- X[-I[[b]], ][Z[-I[[b]]] == 0]\n", + " yZ0 <- y[-I[[b]]][Z[-I[[b]]] == 0]\n", + " XZ1 <- X[-I[[b]], ][Z[-I[[b]]] == 1]\n", + " yZ1 <- y[-I[[b]]][Z[-I[[b]]] == 1]\n", + " DZ0 <- d[-I[[b]]][Z[-I[[b]]] == 0]\n", + " DZ1 <- d[-I[[b]]][Z[-I[[b]]] == 1]\n", + "\n", + "\n", + " if (method == \"regression\") {\n", + " XZ0 <- as.matrix(XZ0)\n", + " XZ1 <- as.matrix(XZ1)\n", + " Xb <- as.matrix(Xb)\n", + " Xnotb <- as.matrix(Xnotb)\n", + "\n", + " # Train an outcome model on training data that received Z=0 and predict outcome on all data in the test set\n", + " yfit0 <- yreg0((XZ0), yZ0)\n", + " yhat0[I[[b]]] <- predict(yfit0, (Xb)) # default type = \"response\"\n", + "\n", + " # train an outcome model on training data that received Z=1 and predict outcome on all data in test set\n", + " yfit1 <- yreg1((XZ1), yZ1)\n", + " yhat1[I[[b]]] <- predict(yfit1, (Xb))\n", + "\n", + " # train a treatment model on training data that received Z=0 and predict treatment on all data in test set\n", + " # default type = \"response\", but for family binomial it's logg odds\n", + " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, type = \"response\")\n", + " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, type = \"response\")\n", + "\n", + " } else if (method == \"randomforest\") {\n", + " DZ0factor <- as.factor(D)[-I[[b]]][Z[-I[[b]]] == 0]\n", + " DZ1factor <- as.factor(D)[-I[[b]]][Z[-I[[b]]] == 1]\n", + " Znotb <- as.factor(Znotb)\n", + "\n", + " yfit0 <- yreg0((XZ0), yZ0)\n", + " yhat0[I[[b]]] <- predict(yfit0, (Xb), type = \"response\")\n", + " yfit1 <- yreg1((XZ1), yZ1)\n", + " yhat1[I[[b]]] <- predict(yfit1, (Xb), type = \"response\")\n", + "\n", + " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, type = \"prob\", DZ0factor = DZ0factor)\n", + " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, type = \"prob\", DZ1factor = DZ1factor)\n", + "\n", + " } else if (method == \"decisiontrees\") {\n", + " XZ0 <- as.data.frame(XZ0)\n", + " XZ1 <- as.data.frame(XZ1)\n", + " Xb <- as.data.frame(Xb)\n", + " Xnotb <- as.data.frame(Xnotb)\n", + "\n", + " yfit0 <- yreg0((XZ0), yZ0)\n", + " # default type = \"response\" for decision trees for continuous response\n", + " yhat0[I[[b]]] <- predict(yfit0, (Xb))\n", + "\n", + " yfit1 <- yreg1((XZ1), yZ1)\n", + " yhat1[I[[b]]] <- predict(yfit1, (Xb))\n", + "\n", + " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, DZ0factor = as.factor(DZ0))\n", + " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, DZ1factor = as.factor(DZ1))\n", + "\n", + " } else if (method == \"boostedtrees\") {\n", + " XZ0 <- as.data.frame(XZ0)\n", + " XZ1 <- as.data.frame(XZ1)\n", + " Xb <- as.data.frame(Xb)\n", + " Xnotb <- as.data.frame(Xnotb)\n", + "\n", + " yfit0 <- yreg0((XZ0), yZ0)\n", + " yhat0[I[[b]]] <- predict(yfit0, (Xb)) # default type = \"response\" for boosted trees\n", + " yfit1 <- yreg1((XZ1), yZ1)\n", + " yhat1[I[[b]]] <- predict(yfit1, (Xb))\n", + "\n", + " dhat0[I[[b]]] <- get_dhat0(XZ0, DZ0, Xb, dreg0, type = \"response\")\n", + " dhat1[I[[b]]] <- get_dhat1(XZ1, DZ1, Xb, dreg1, type = \"response\")\n", + "\n", + " }\n", + "\n", + " # propensity scores:\n", + " if (method == \"regression\") {\n", + " zfit_b <- zreg((Xnotb), Znotb)\n", + " zhat_b <- predict(zfit_b, (Xb), type = \"response\")\n", + " } else if (method == \"randomforest\") {\n", + " zfit_b <- zreg((Xnotb), Znotb)\n", + " zhat_b <- predict(zfit_b, (Xb), type = \"prob\")[, 2]\n", + " } else if (method == \"decisiontrees\") {\n", + " zfit_b <- zreg((Xnotb), as.factor(Znotb))\n", + " zhat_b <- predict(zfit_b, (Xb)) # default is prob, so get second column\n", + " zhat_b <- zhat_b[, 2]\n", + " } else if (method == \"boostedtrees\") {\n", + " zfit_b <- zreg((Xnotb), Znotb)\n", + " zhat_b <- predict(zfit_b, (Xb), type = \"response\")\n", + " }\n", + " zhat_b <- pmax(pmin(zhat_b, 1 - trimming), trimming) # trimming so scores are between [trimming, (1-trimming)]\n", + " zhat[I[[b]]] <- zhat_b\n", + "\n", + " cat(b, \" \")\n", + " }\n", + "\n", + "\n", + " # Prediction of treatment and outcome for observed instrument\n", + " yhat <- yhat0 * (1 - Z) + yhat1 * Z\n", + " dhat <- dhat0 * (1 - Z) + dhat1 * Z\n", + "\n", + " # residuals\n", + " ytil <- y - yhat\n", + " dtil <- D - dhat\n", + " ztil <- Z - zhat\n", + "\n", + " # doubly robust quantity for every sample\n", + " HZ <- Z / zhat - (1 - Z) / (1 - zhat)\n", + " drZ <- yhat1 - yhat0 + (y - yhat) * HZ\n", + " drD <- dhat1 - dhat0 + (D - dhat) * HZ\n", + " coef_est <- mean(drZ) / mean(drD)\n", + " cat(\"point\", coef_est)\n", + " psi <- drZ - coef_est * drD\n", + " Jhat <- mean(drD)\n", + " variance <- mean(psi^2) / Jhat^2\n", + " se <- sqrt(variance / nrow(X))\n", + " cat(\"se\", se)\n", + "\n", + " return(list(coef_est = coef_est, se = se, yhat = yhat, dhat = dhat, zhat = zhat, ytil = ytil,\n", + " dtil = dtil, ztil = ztil, drZ = drZ, drD = drD,\n", + " yhat0 = yhat0, yhat1 = yhat1, dhat0 = dhat0, dhat1 = dhat1))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51", + "metadata": { + "id": "51", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "summary_for_iivm <- function(coef_est, se, yhat, dhat, zhat, ytil, dtil, ztil, drZ, drD, X, Z, D, y, name) {\n", + " summary_data <- data.frame(\n", + " estimate = coef_est, # point estimate\n", + " se = se, # standard error\n", + " lower = coef_est - 1.96 * se, # lower end of 95% confidence interval\n", + " upper = coef_est + 1.96 * se, # upper end of 95% confidence interval\n", + " rmse_y = sqrt(mean(ytil^2)), # res of model that predicts outcome y\n", + " rmse_D = sqrt(mean(dtil^2)), # res of model that predicts treatment D\n", + " rmse_Z = sqrt(mean(ztil^2)), # res of model that predicts instrument Z\n", + " accuracy_D = mean(abs(dtil) < 0.5), # binary classification accuracy of model for D\n", + " accuracy_Z = mean(abs(ztil) < 0.5) # binary classification accuracy of model for Z\n", + " )\n", + " row.names(summary_data) <- name\n", + " return(summary_data)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52", + "metadata": { + "id": "52", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Lasso/Ridge\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Lasso/Logistic \\n\"))\n", + "# DML with Lasso/Ridge\n", + "dreg0 <- function(x, d) {\n", + " cv.glmnet(x, d, family = \"binomial\", alpha = 0, nfolds = 5)\n", + "}\n", + "dreg1 <- function(x, d) {\n", + " cv.glmnet(x, d, family = \"binomial\", alpha = 0, nfolds = 5)\n", + "}\n", + "yreg0 <- function(x, y) {\n", + " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", + "}\n", + "yreg1 <- function(x, y) {\n", + " cv.glmnet(x, y, family = \"gaussian\", alpha = 1, nfolds = 5)\n", + "}\n", + "zreg <- function(x, z) {\n", + " cv.glmnet(x, z, family = \"binomial\", alpha = 0, nfolds = 5)\n", + "}\n", + "\n", + "dml2_results <- dml2_for_iivm(as.matrix(X), D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", + " trimming = 0.01, nfold = 5, method = \"regression\")\n", + "sum_lasso_ridge_cv <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", + " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", + " dml2_results$drZ, dml2_results$drD, name = \"LassoCV/LogisticCV\")\n", + "table <- data.frame()\n", + "table <- rbind(table, sum_lasso_ridge_cv)\n", + "table\n", + "\n", + "yhat_lasso <- dml2_results$yhat\n", + "dhat_lasso <- dml2_results$dhat\n", + "yhat0_lasso <- dml2_results$yhat0\n", + "yhat1_lasso <- dml2_results$yhat1\n", + "dhat0_lasso <- dml2_results$dhat0\n", + "dhat1_lasso <- dml2_results$dhat1\n", + "zhat_lasso <- dml2_results$zhat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53", + "metadata": { + "id": "53", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Random Forest\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Random Forest \\n\"))\n", + "\n", + "dreg0 <- function(x, d) {\n", + " randomForest(x, d, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "dreg1 <- function(x, d) {\n", + " randomForest(x, d, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "yreg0 <- function(x, y) {\n", + " randomForest(x, y, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "yreg1 <- function(x, y) {\n", + " randomForest(x, y, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "zreg <- function(x, z) {\n", + " randomForest(x, z, ntree = 1000, nodesize = 10)\n", + "} # ML method=Forest\n", + "\n", + "dml2_results <- dml2_for_iivm(X, D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", + " trimming = 0.01, nfold = 5, method = \"randomforest\")\n", + "sum_rf <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", + " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", + " dml2_results$drZ, dml2_results$drD, name = \"RF\")\n", + "table <- rbind(table, sum_rf)\n", + "table\n", + "\n", + "yhat_rf <- dml2_results$yhat\n", + "dhat_rf <- dml2_results$dhat\n", + "yhat0_rf <- dml2_results$yhat0\n", + "yhat1_rf <- dml2_results$yhat1\n", + "dhat0_rf <- dml2_results$dhat0\n", + "dhat1_rf <- dml2_results$dhat1\n", + "zhat_rf <- dml2_results$zhat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54", + "metadata": { + "id": "54", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Decision Trees\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Decision Trees \\n\"))\n", + "\n", + "dreg0 <- function(x, d) {\n", + " rpart(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), method = \"class\", minbucket = 10, cp = 0.001)\n", + "}\n", + "dreg1 <- function(x, d) {\n", + " rpart(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), method = \"class\", minbucket = 10, cp = 0.001)\n", + "}\n", + "yreg0 <- function(x, y) {\n", + " rpart(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), minbucket = 10, cp = 0.001)\n", + "}\n", + "yreg1 <- function(x, y) {\n", + " rpart(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), minbucket = 10, cp = 0.001)\n", + "}\n", + "zreg <- function(x, z) {\n", + " rpart(as.formula(\"Z ~ .\"), cbind(data.frame(Z = z), x), method = \"class\", minbucket = 10, cp = 0.001)\n", + "}\n", + "\n", + "dml2_results <- dml2_for_iivm(X, D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", + " trimming = 0.01, nfold = 5, method = \"decisiontrees\")\n", + "sum_tr <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", + " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", + " dml2_results$drZ, dml2_results$drD, name = \"Decision Trees\")\n", + "table <- rbind(table, sum_tr)\n", + "table\n", + "\n", + "yhat_tr <- dml2_results$yhat\n", + "dhat_tr <- dml2_results$dhat\n", + "yhat0_tr <- dml2_results$yhat0\n", + "yhat1_tr <- dml2_results$yhat1\n", + "dhat0_tr <- dml2_results$dhat0\n", + "dhat1_tr <- dml2_results$dhat1\n", + "zhat_tr <- dml2_results$zhat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55", + "metadata": { + "id": "55", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DML with Boosted Trees\n", + "set.seed(123)\n", + "cat(sprintf(\"\\nDML with Boosted Trees \\n\"))\n", + "\n", + "# NB: early stopping cannot easily be implemented with gbm\n", + "## set n.trees = best, where best <- gbm.perf(dreg_boost, plot.it = FALSE)\n", + "dreg0 <- function(x, d) {\n", + " gbm(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), distribution = \"bernoulli\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "dreg1 <- function(x, d) {\n", + " gbm(as.formula(\"D ~ .\"), cbind(data.frame(D = d), x), distribution = \"bernoulli\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "yreg0 <- function(x, y) {\n", + " gbm(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), distribution = \"gaussian\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "yreg1 <- function(x, y) {\n", + " gbm(as.formula(\"y ~ .\"), cbind(data.frame(y = y), x), distribution = \"gaussian\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "zreg <- function(x, z) {\n", + " gbm(as.formula(\"Z ~ .\"), cbind(data.frame(Z = z), x), distribution = \"bernoulli\",\n", + " interaction.depth = 2, n.trees = 100, shrinkage = .1)\n", + "}\n", + "\n", + "# passing these through regression as type=\"response\", and D and Z should not be factors!\n", + "dml2_results <- dml2_for_iivm(X, D, Z, y, dreg0, dreg1, yreg0, yreg1, zreg,\n", + " trimming = 0.01, nfold = 5, method = \"boostedtrees\")\n", + "sum_boost <- summary_for_iivm(dml2_results$coef_est, dml2_results$se, dml2_results$yhat, dml2_results$dhat,\n", + " dml2_results$zhat, dml2_results$ytil, dml2_results$dtil, dml2_results$ztil,\n", + " dml2_results$drZ, dml2_results$drD, name = \"Boosted Trees\")\n", + "table <- rbind(table, sum_boost)\n", + "table\n", + "\n", + "yhat_boost <- dml2_results$yhat\n", + "dhat_boost <- dml2_results$dhat\n", + "yhat0_boost <- dml2_results$yhat0\n", + "yhat1_boost <- dml2_results$yhat1\n", + "dhat0_boost <- dml2_results$dhat0\n", + "dhat1_boost <- dml2_results$dhat1\n", + "zhat_boost <- dml2_results$zhat" + ] + }, + { + "cell_type": "markdown", + "id": "56", + "metadata": { + "id": "56" + }, + "source": [ + "## Ensembles" + ] + }, + { + "cell_type": "markdown", + "id": "57", + "metadata": { + "id": "57" + }, + "source": [ + "Boosted trees give the best RMSE for D and Z and random forests give the best RMSE for Y." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58", + "metadata": { + "id": "58", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Best fit is boosted trees for D, Z and random forests for Y\n", + "\n", + "best_yhat0 <- yhat0_rf\n", + "best_yhat1 <- yhat1_rf\n", + "best_yhat <- yhat_rf\n", + "\n", + "best_dhat0 <- dhat0_boost\n", + "best_dhat1 <- dhat1_boost\n", + "best_dhat <- dhat_boost\n", + "\n", + "best_zhat <- zhat_boost\n", + "\n", + "ytil_best <- y - best_yhat\n", + "dtil_best <- D - best_dhat\n", + "ztil_best <- Z - best_zhat\n", + "\n", + "# doubly robust quantity for every sample\n", + "HZ <- Z / best_zhat - (1 - Z) / (1 - best_zhat)\n", + "drZ <- best_yhat1 - best_yhat0 + (y - best_yhat) * HZ\n", + "drD <- best_dhat1 - best_dhat0 + (D - best_dhat) * HZ\n", + "coef_est <- mean(drZ) / mean(drD)\n", + "psi <- drZ - coef_est * drD\n", + "Jhat <- mean(drD)\n", + "variance <- mean(psi^2) / Jhat^2\n", + "se <- sqrt(variance / nrow(X))\n", + "\n", + "sum_best <- summary_for_iivm(coef_est, se, best_yhat, best_dhat, best_zhat,\n", + " ytil_best, dtil_best, ztil_best, drZ, drD, name = \"Best\")\n", + "table <- rbind(table, sum_best)\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "59", + "metadata": { + "id": "59" + }, + "source": [ + "We'll form a model average with unconstrained least squares weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60", + "metadata": { + "id": "60", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Least squares model average\n", + "ma_dcoef <- lm(D ~ dhat_lasso + dhat_rf + dhat_tr + dhat_boost - 1)$coef\n", + "ma_ycoef <- lm(y ~ yhat_lasso + yhat_rf + yhat_tr + yhat_boost - 1)$coef\n", + "ma_zcoef <- lm(Z ~ zhat_lasso + zhat_rf + zhat_tr + zhat_boost - 1)$coef\n", + "\n", + "ma_yhat0 <- cbind(yhat0_lasso, yhat0_rf, yhat0_tr, yhat0_boost) %*% as.matrix(ma_ycoef)\n", + "ma_yhat1 <- cbind(yhat1_lasso, yhat1_rf, yhat1_tr, yhat1_boost) %*% as.matrix(ma_ycoef)\n", + "ma_dhat0 <- cbind(dhat0_lasso, dhat0_rf, dhat0_tr, dhat0_boost) %*% as.matrix(ma_dcoef)\n", + "ma_dhat1 <- cbind(dhat1_lasso, dhat1_rf, dhat1_tr, dhat1_boost) %*% as.matrix(ma_dcoef)\n", + "ma_zhat <- cbind(zhat_lasso, zhat_rf, zhat_tr, zhat_boost) %*% as.matrix(ma_zcoef)\n", + "\n", + "# Prediction of treatment and outcome for observed instrument\n", + "ma_yhat <- ma_yhat0 * (1 - Z) + ma_yhat1 * Z\n", + "ma_dhat <- ma_dhat0 * (1 - Z) + ma_dhat1 * Z\n", + "\n", + "# residuals\n", + "ma_ytil <- y - ma_yhat\n", + "ma_dtil <- D - ma_dhat\n", + "ma_ztil <- Z - ma_zhat\n", + "\n", + "# doubly robust quantity for every sample\n", + "HZ <- Z / ma_zhat - (1 - Z) / (1 - ma_zhat)\n", + "drZ <- ma_yhat1 - ma_yhat0 + (y - ma_yhat) * HZ\n", + "drD <- ma_dhat1 - ma_dhat0 + (D - ma_dhat) * HZ\n", + "coef_est <- mean(drZ) / mean(drD)\n", + "psi <- drZ - coef_est * drD\n", + "Jhat <- mean(drD)\n", + "variance <- mean(psi^2) / Jhat^2\n", + "se <- sqrt(variance / nrow(X))\n", + "\n", + "sum_ma <- summary_for_iivm(coef_est, se, ma_yhat, ma_dhat, ma_zhat,\n", + " ma_ytil, ma_dtil, ma_ztil, drZ, drD, name = \"Model Average\")\n", + "table <- rbind(table, sum_ma)\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "61", + "metadata": { + "id": "61" + }, + "source": [ + "Comparing with the PLR model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62", + "metadata": { + "id": "62", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "tableplr" + ] + }, + { + "cell_type": "markdown", + "id": "63", + "metadata": { + "id": "63" + }, + "source": [ + "We find that the PLR model estimates of the effect are generally larger; though both sets of results have overlapping confidence intervals." + ] + }, + { + "cell_type": "markdown", + "id": "64", + "metadata": { + "id": "64" + }, + "source": [ + "\n", + "Again as before, ideally we would do (semi) cross-fitting with AutoML in order to find good first-stage models and re-run DML with these models. Unfortunately this is not easy to do in R.\n", + "\n", + "As before, in the below analysis of robust inference, we choose Boosted Trees as they perform well in RMSE and accuracy on first-stage models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65", + "metadata": { + "id": "65", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "iivm_robust_inference <- function(point, stderr, yhat, Dhat, Zhat, resy, resD, resZ,\n", + " drZ, drD, X, Z, D, y, grid, alpha = 0.05) {\n", + " # Inference in the partially linear IV model that is robust to weak identification.\n", + " # grid: grid of theta values to search over when trying to identify the confidence region\n", + " # alpha: confidence level\n", + "\n", + " n <- dim(X)[1]\n", + " thr <- qchisq(1 - alpha, df = 1)\n", + " accept <- c()\n", + "\n", + " for (theta in grid) {\n", + " moment <- drZ - theta * drD\n", + " test <- n * mean(moment)^2 / var(moment)\n", + " if (test <= thr) {\n", + " accept <- c(accept, theta)\n", + " }\n", + " }\n", + "\n", + " return(accept)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66", + "metadata": { + "id": "66", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "grid <- seq(0, 20000, length.out = 10000)\n", + "region <- iivm_robust_inference(point = dml2_results$coef_est, stderr = dml2_results$se,\n", + " yhat = dml2_results$yhat, Dhat = dml2_results$dhat, Zhat = dml2_results$zhat,\n", + " resy = dml2_results$ytil, resD = dml2_results$dtil, resZ = dml2_results$ztil,\n", + " drZ = dml2_results$drZ, drD = dml2_results$drD,\n", + " X = X, Z = Z, D = D, y = y, grid = grid)\n", + "\n", + "# Calculate min and max\n", + "min_region <- min(region)\n", + "max_region <- max(region)\n", + "\n", + "print(min_region)\n", + "print(max_region)" + ] + }, + { + "cell_type": "markdown", + "id": "67", + "metadata": { + "id": "67" + }, + "source": [ + "We find again that the robust inference confidence region is almost identical to the normal based inference. We are most probably in the strong instrument regime. We can check the t-statistic for the effect of the instrument on the treatment to verify this." + ] + }, + { + "cell_type": "markdown", + "id": "68", + "metadata": { + "id": "68", + "papermill": { + "duration": 0.010725, + "end_time": "2022-04-19T09:06:51.098483", + "exception": false, + "start_time": "2022-04-19T09:06:51.087758", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# DoubleML package\n", + "\n", + "There exist nice packages out there that can help us do our estimation with the simple call of a function. Such packages include `EconML` (Python) and `DoubleML` (Python and R).\n", + "\n", + "We run through IIVM using `DoubleML` below to illustrate. The `DoubleML` package internally builds on `mlr3`. We use the meta package `mlr3` to generate predictions with machine learning methods. A comprehensive introduction and description of the `mlr3` package is provided in the [mlr3book](https://mlr3book.mlr-org.com/). A list of all learners that you can use in `mlr3` can be found [here](https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html). The entry in the columns *mlr3 Package* and *Packages* indicate which packages must be installed/loaded in your R session.\n", + "\n", + "You find additional information about `DoubleML` on the package on the package website https://docs.doubleml.org/ and the R documentation page https://docs.doubleml.org/r/stable/." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69", + "metadata": { + "id": "69", + "papermill": { + "duration": 20.239271, + "end_time": "2022-04-19T09:07:11.369618", + "exception": false, + "start_time": "2022-04-19T09:06:51.130347", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "install.packages(\"DoubleML\")\n", + "install.packages(\"mlr3learners\")\n", + "install.packages(\"mlr3\")\n", + "install.packages(\"data.table\")\n", + "install.packages(\"ranger\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70", + "metadata": { + "vscode": { + "languageId": "r" + }, + "id": "70" + }, + "outputs": [], + "source": [ + "library(DoubleML)\n", + "library(mlr3learners)\n", + "library(mlr3)\n", + "library(data.table)\n", + "library(ranger)" + ] + }, + { + "cell_type": "markdown", + "id": "71", + "metadata": { + "id": "71", + "papermill": { + "duration": 0.015455, + "end_time": "2022-04-19T09:12:00.920079", + "exception": false, + "start_time": "2022-04-19T09:12:00.904624", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Local Average Treatment Effects of 401(k) Participation on Net Financial Assets" + ] + }, + { + "cell_type": "markdown", + "id": "72", + "metadata": { + "id": "72", + "papermill": { + "duration": 0.015158, + "end_time": "2022-04-19T09:12:00.950542", + "exception": false, + "start_time": "2022-04-19T09:12:00.935384", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Interactive IV Model (IIVM)" + ] + }, + { + "cell_type": "markdown", + "id": "73", + "metadata": { + "id": "73", + "papermill": { + "duration": 0.015304, + "end_time": "2022-04-19T09:12:00.981285", + "exception": false, + "start_time": "2022-04-19T09:12:00.965981", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now, we consider estimation of local average treatment effects (LATE) of participation with the binary instrument `e401`. As before, $Y$ denotes the outcome `net_tfa`, and $X$ is the vector of covariates. Here the structural equation model is:\n", + "\n", + "\\begin{align}\n", + "Y :=~& g_0(Z,X) + U, &\\quad E[U\\mid Z,X] = 0,\\\\\n", + "D :=~& r_0(Z,X) + V, &\\quad E[V\\mid Z, X] = 0,\\\\\n", + "Z :=~& m_0(X) + \\zeta, &\\quad E[\\zeta \\mid X] = 0.\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74", + "metadata": { + "id": "74", + "papermill": { + "duration": 0.06823, + "end_time": "2022-04-19T09:12:01.064957", + "exception": false, + "start_time": "2022-04-19T09:12:00.996727", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Constructing the data (as DoubleMLData)\n", + "formula_flex2 <- paste(\"net_tfa ~ p401+ e401 + poly(age, 6, raw=TRUE) + poly(inc, 8, raw=TRUE) + \",\n", + " \"poly(educ, 4, raw=TRUE) + poly(fsize, 2, raw=TRUE) + male + marr + twoearn + db + pira + hown\")\n", + "model_flex2 <- as.data.table(model.frame(formula_flex2, data))\n", + "x_cols <- colnames(model_flex2)[-c(1, 2, 3)]\n", + "data_iv <- DoubleMLData$new(model_flex2, y_col = \"net_tfa\", d_cols = \"p401\", z_cols = \"e401\", x_cols = x_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75", + "metadata": { + "id": "75", + "papermill": { + "duration": 20.934595, + "end_time": "2022-04-19T09:12:22.014866", + "exception": false, + "start_time": "2022-04-19T09:12:01.080271", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "lgr::get_logger(\"mlr3\")$set_threshold(\"warn\")\n", + "lasso <- lrn(\"regr.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", + "lasso_class <- lrn(\"classif.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", + "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", + " ml_g = lasso,\n", + " ml_m = lasso_class, ml_r = lasso_class, n_folds = 5, subgroups = list(\n", + " always_takers = FALSE,\n", + " never_takers = TRUE\n", + " )\n", + ")\n", + "dml_mliivm$fit(store_predictions = TRUE)\n", + "dml_mliivm$summary()\n", + "lasso_mliivm <- dml_mliivm$coef\n", + "lasso_std_mliivm <- dml_mliivm$se" + ] + }, + { + "cell_type": "markdown", + "id": "76", + "metadata": { + "id": "76", + "papermill": { + "duration": 0.015382, + "end_time": "2022-04-19T09:12:22.045989", + "exception": false, + "start_time": "2022-04-19T09:12:22.030607", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The confidence interval for the local average treatment effect of participation is given by" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77", + "metadata": { + "id": "77", + "papermill": { + "duration": 0.042067, + "end_time": "2022-04-19T09:12:22.103953", + "exception": false, + "start_time": "2022-04-19T09:12:22.061886", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "dml_mliivm$confint(level = 0.95)" + ] + }, + { + "cell_type": "markdown", + "id": "78", + "metadata": { + "id": "78", + "papermill": { + "duration": 0.015374, + "end_time": "2022-04-19T09:12:22.134875", + "exception": false, + "start_time": "2022-04-19T09:12:22.119501", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Here we can also check the accuracy of the model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79", + "metadata": { + "id": "79", + "papermill": { + "duration": 0.056054, + "end_time": "2022-04-19T09:12:22.206157", + "exception": false, + "start_time": "2022-04-19T09:12:22.150103", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# variables\n", + "y <- as.matrix(pension$net_tfa) # true observations\n", + "d <- as.matrix(pension$p401)\n", + "z <- as.matrix(pension$e401)\n", + "\n", + "# predictions\n", + "dml_mliivm$params_names()\n", + "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(z=0, X)\n", + "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(z=1, X)\n", + "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", + "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(z=0, X)\n", + "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(z=1, X)\n", + "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", + "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80", + "metadata": { + "id": "80", + "papermill": { + "duration": 0.056945, + "end_time": "2022-04-19T09:12:22.278593", + "exception": false, + "start_time": "2022-04-19T09:12:22.221648", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# cross-fitted RMSE: outcome\n", + "lasso_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", + "lasso_y_mliivm\n", + "\n", + "# cross-fitted RMSE: treatment\n", + "lasso_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", + "lasso_d_mliivm\n", + "\n", + "# cross-fitted RMSE: instrument\n", + "lasso_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", + "lasso_z_mliivm" + ] + }, + { + "cell_type": "markdown", + "id": "81", + "metadata": { + "id": "81", + "papermill": { + "duration": 0.016468, + "end_time": "2022-04-19T09:12:22.311250", + "exception": false, + "start_time": "2022-04-19T09:12:22.294782", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Again, we repeat the procedure for the other machine learning methods:" + ] + }, + { + "cell_type": "code", + "source": [ + "install.packages(\"remotes\")\n", + "library(remotes)" + ], + "metadata": { + "id": "1XXIPOR6fGKO" + }, + "id": "1XXIPOR6fGKO", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82", + "metadata": { + "id": "82", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# needed to run boosting\n", + "remotes::install_github(\"mlr-org/mlr3extralearners\")\n", + "install.packages(\"mlr3extralearners\")\n", + "install.packages(\"mboost\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83", + "metadata": { + "vscode": { + "languageId": "r" + }, + "id": "83" + }, + "outputs": [], + "source": [ + "library(mlr3extralearners)\n", + "library(mboost)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84", + "metadata": { + "id": "84", + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Forest\n", + "randomForest <- lrn(\"regr.ranger\")\n", + "random_forest_class <- lrn(\"classif.ranger\")\n", + "\n", + "# Trees\n", + "trees <- lrn(\"regr.rpart\")\n", + "trees_class <- lrn(\"classif.rpart\")\n", + "\n", + "# Boosting\n", + "boost <- lrn(\"regr.glmboost\")\n", + "boost_class <- lrn(\"classif.glmboost\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85", + "metadata": { + "id": "85", + "papermill": { + "duration": 69.414354, + "end_time": "2022-04-19T09:13:31.742249", + "exception": false, + "start_time": "2022-04-19T09:12:22.327895", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "### random forest ###\n", + "\n", + "lgr::get_logger(\"mlr3\")$set_threshold(\"warn\")\n", + "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", + " ml_g = randomForest,\n", + " ml_m = random_forest_class, ml_r = random_forest_class, n_folds = 3, subgroups = list(\n", + " always_takers = FALSE,\n", + " never_takers = TRUE\n", + " )\n", + ")\n", + "dml_mliivm$fit(store_predictions = TRUE)\n", + "dml_mliivm$summary()\n", + "forest_mliivm <- dml_mliivm$coef\n", + "forest_std_mliivm <- dml_mliivm$se\n", + "\n", + "# predictions\n", + "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(Z=0, X)\n", + "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(Z=1, X)\n", + "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", + "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(Z=0, X)\n", + "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(Z=1, X)\n", + "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", + "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o\n", + "\n", + "# cross-fitted RMSE: outcome\n", + "forest_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", + "forest_y_mliivm\n", + "\n", + "# cross-fitted RMSE: treatment\n", + "forest_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", + "forest_d_mliivm\n", + "\n", + "# cross-fitted RMSE: instrument\n", + "forest_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", + "forest_z_mliivm\n", + "\n", + "### trees ###\n", + "\n", + "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", + " ml_g = trees,\n", + " ml_m = trees_class, ml_r = trees_class, n_folds = 3, subgroups = list(\n", + " always_takers = FALSE,\n", + " never_takers = TRUE\n", + " )\n", + ")\n", + "dml_mliivm$fit(store_predictions = TRUE)\n", + "dml_mliivm$summary()\n", + "tree_mliivm <- dml_mliivm$coef\n", + "tree_std_mliivm <- dml_mliivm$se\n", + "\n", + "# predictions\n", + "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(Z=0, X)\n", + "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(Z=1, X)\n", + "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", + "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(Z=0, X)\n", + "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(Z=1, X)\n", + "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", + "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o\n", + "\n", + "# cross-fitted RMSE: outcome\n", + "tree_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", + "tree_y_mliivm\n", + "\n", + "# cross-fitted RMSE: treatment\n", + "tree_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", + "tree_d_mliivm\n", + "\n", + "# cross-fitted RMSE: instrument\n", + "tree_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", + "tree_z_mliivm\n", + "\n", + "\n", + "### boosting ###\n", + "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", + " ml_g = boost,\n", + " ml_m = boost_class, ml_r = boost_class, n_folds = 3, subgroups = list(\n", + " always_takers = FALSE,\n", + " never_takers = TRUE\n", + " )\n", + ")\n", + "dml_mliivm$fit(store_predictions = TRUE)\n", + "dml_mliivm$summary()\n", + "boost_mliivm <- dml_mliivm$coef\n", + "boost_std_mliivm <- dml_mliivm$se\n", + "\n", + "# predictions\n", + "g0_hat <- as.matrix(dml_mliivm$predictions$ml_g0) # predictions of g_0(Z=0, X)\n", + "g1_hat <- as.matrix(dml_mliivm$predictions$ml_g1) # predictions of g_0(Z=1, X)\n", + "g_hat <- z * g1_hat + (1 - z) * g0_hat # predictions of g_0\n", + "r0_hat <- as.matrix(dml_mliivm$predictions$ml_r0) # predictions of r_0(Z=0, X)\n", + "r1_hat <- as.matrix(dml_mliivm$predictions$ml_r1) # predictions of r_0(Z=1, X)\n", + "r_hat <- z * r1_hat + (1 - z) * r0_hat # predictions of r_0\n", + "m_hat <- as.matrix(dml_mliivm$predictions$ml_m) # predictions of m_o\n", + "\n", + "# cross-fitted RMSE: outcome\n", + "boost_y_mliivm <- sqrt(mean((y - g_hat)^2))\n", + "boost_y_mliivm\n", + "\n", + "# cross-fitted RMSE: treatment\n", + "boost_d_mliivm <- sqrt(mean((d - r_hat)^2))\n", + "boost_d_mliivm\n", + "\n", + "# cross-fitted RMSE: instrument\n", + "boost_z_mliivm <- sqrt(mean((z - m_hat)^2))\n", + "boost_z_mliivm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86", + "metadata": { + "id": "86", + "papermill": { + "duration": 0.061872, + "end_time": "2022-04-19T09:13:31.824148", + "exception": false, + "start_time": "2022-04-19T09:13:31.762276", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "table <- matrix(0, 5, 4)\n", + "table[1, 1:4] <- c(lasso_mliivm, forest_mliivm, tree_mliivm, boost_mliivm)\n", + "table[2, 1:4] <- c(lasso_std_mliivm, forest_std_mliivm, tree_std_mliivm, boost_std_mliivm)\n", + "table[3, 1:4] <- c(lasso_y_mliivm, forest_y_mliivm, tree_y_mliivm, boost_y_mliivm)\n", + "table[4, 1:4] <- c(lasso_d_mliivm, forest_d_mliivm, tree_d_mliivm, boost_d_mliivm)\n", + "table[5, 1:4] <- c(lasso_z_mliivm, forest_z_mliivm, tree_z_mliivm, boost_z_mliivm)\n", + "rownames(table) <- c(\"Estimate\", \"Std.Error\", \"RMSE Y\", \"RMSE D\", \"RMSE Z\")\n", + "colnames(table) <- c(\"Lasso\", \"Random Forest\", \"Trees\", \"Boosting\")\n", + "tab <- xtable(table, digits = 2)\n", + "tab" + ] + }, + { + "cell_type": "markdown", + "id": "87", + "metadata": { + "id": "87", + "papermill": { + "duration": 0.017437, + "end_time": "2022-04-19T09:13:31.859052", + "exception": false, + "start_time": "2022-04-19T09:13:31.841615", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We report results based on four ML methods for estimating the nuisance functions used in forming the orthogonal estimating equations. All four point estimates are positive, but their magnitudes and precision vary. Lasso and trees yield precise estimates near $12,000; forest and boosting yield smaller, less precise estimates. The forest estimate is not statistically distinguishable from zero at the 5% level." + ] + }, + { + "cell_type": "markdown", + "id": "88", + "metadata": { + "id": "88", + "papermill": { + "duration": 0.017163, + "end_time": "2022-04-19T09:13:31.893361", + "exception": false, + "start_time": "2022-04-19T09:13:31.876198", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We might rerun the model using the best ML method for each equation to get a final estimate for the treatment effect of participation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89", + "metadata": { + "id": "89", + "papermill": { + "duration": 20.780029, + "end_time": "2022-04-19T09:13:52.690594", + "exception": false, + "start_time": "2022-04-19T09:13:31.910565", + "status": "completed" + }, + "tags": [], + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "lgr::get_logger(\"mlr3\")$set_threshold(\"warn\")\n", + "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", + " ml_g = randomForest,\n", + " ml_m = boost_class, ml_r = lasso_class, n_folds = 5, subgroups = list(\n", + " always_takers = FALSE,\n", + " never_takers = TRUE\n", + " )\n", + ")\n", + "dml_mliivm$fit(store_predictions = TRUE)\n", + "dml_mliivm$summary()\n", + "best_mliivm <- dml_mliivm$coef\n", + "best_std_mliivm <- dml_mliivm$se" + ] } - }, - "outputs": [], - "source": [ - "table <- matrix(0, 5, 4)\n", - "table[1, 1:4] <- c(lasso_mliivm, forest_mliivm, tree_mliivm, boost_mliivm)\n", - "table[2, 1:4] <- c(lasso_std_mliivm, forest_std_mliivm, tree_std_mliivm, boost_std_mliivm)\n", - "table[3, 1:4] <- c(lasso_y_mliivm, forest_y_mliivm, tree_y_mliivm, boost_y_mliivm)\n", - "table[4, 1:4] <- c(lasso_d_mliivm, forest_d_mliivm, tree_d_mliivm, boost_d_mliivm)\n", - "table[5, 1:4] <- c(lasso_z_mliivm, forest_z_mliivm, tree_z_mliivm, boost_z_mliivm)\n", - "rownames(table) <- c(\"Estimate\", \"Std.Error\", \"RMSE Y\", \"RMSE D\", \"RMSE Z\")\n", - "colnames(table) <- c(\"Lasso\", \"Random Forest\", \"Trees\", \"Boosting\")\n", - "tab <- xtable(table, digits = 2)\n", - "tab" - ] - }, - { - "cell_type": "markdown", - "id": "87", - "metadata": { - "id": "f4ce7be1", - "papermill": { - "duration": 0.017437, - "end_time": "2022-04-19T09:13:31.859052", - "exception": false, - "start_time": "2022-04-19T09:13:31.841615", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We report results based on four ML methods for estimating the nuisance functions used in\n", - "forming the orthogonal estimating equations. We find again that the estimates of the treatment effect are stable across ML methods. The estimates are highly significant, hence we would reject the hypothesis\n", - "that the effect of 401(k) participation has no effect on financial health." - ] - }, - { - "cell_type": "markdown", - "id": "88", - "metadata": { - "id": "4939cd9c", - "papermill": { - "duration": 0.017163, - "end_time": "2022-04-19T09:13:31.893361", - "exception": false, - "start_time": "2022-04-19T09:13:31.876198", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "We might rerun the model using the best ML method for each equation to get a final estimate for the treatment effect of participation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89", - "metadata": { - "id": "ca612b71", + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.0.5" + }, "papermill": { - "duration": 20.780029, - "end_time": "2022-04-19T09:13:52.690594", - "exception": false, - "start_time": "2022-04-19T09:13:31.910565", - "status": "completed" - }, - "tags": [], - "vscode": { - "languageId": "r" + "default_parameters": {}, + "duration": 427.936706, + "end_time": "2022-04-19T09:13:53.230849", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2022-04-19T09:06:45.294143", + "version": "2.3.4" } - }, - "outputs": [], - "source": [ - "lgr::get_logger(\"mlr3\")$set_threshold(\"warn\")\n", - "dml_mliivm <- DoubleMLIIVM$new(data_iv,\n", - " ml_g = randomForest,\n", - " ml_m = lasso_class, ml_r = lasso_class, n_folds = 5, subgroups = list(\n", - " always_takers = FALSE,\n", - " never_takers = TRUE\n", - " )\n", - ")\n", - "dml_mliivm$fit(store_predictions = TRUE)\n", - "dml_mliivm$summary()\n", - "best_mliivm <- dml_mliivm$coef\n", - "best_std_mliivm <- dml_mliivm$se" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.0.5" }, - "papermill": { - "default_parameters": {}, - "duration": 427.936706, - "end_time": "2022-04-19T09:13:53.230849", - "environment_variables": {}, - "exception": null, - "input_path": "__notebook__.ipynb", - "output_path": "__notebook__.ipynb", - "parameters": {}, - "start_time": "2022-04-19T09:06:45.294143", - "version": "2.3.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 538bbaf4771bd90d62f89f78ba01303bcc7e028f Mon Sep 17 00:00:00 2001 From: chansen776 Date: Wed, 30 Sep 2026 17:50:04 -0500 Subject: [PATCH 20/32] ADML 401k notebook Update to follow naming convention of other books. Mild figure edits. --- T-ADML/python_ADML_401k.ipynb | 1152 --------------------------------- T-ADML/r-ADML-401k.irnb | 1146 ++++++++++++++++++++++++++++++++ 2 files changed, 1146 insertions(+), 1152 deletions(-) delete mode 100644 T-ADML/python_ADML_401k.ipynb create mode 100644 T-ADML/r-ADML-401k.irnb diff --git a/T-ADML/python_ADML_401k.ipynb b/T-ADML/python_ADML_401k.ipynb deleted file mode 100644 index c2a75ca9..00000000 --- a/T-ADML/python_ADML_401k.ipynb +++ /dev/null @@ -1,1152 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ec70e3f1", - "metadata": {}, - "source": [ - "# Empirical Example for Sensitivity Analysis: 401(k) Eligibility and Financial Wealth\n", - "\n", - "This is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n", - "*Long Story Short: Omitted Variable Bias in Causal Machine Learning* \n", - "\n", - "The original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "170f2037", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:54:10.878737Z", - "iopub.status.busy": "2026-09-11T14:54:10.877672Z", - "iopub.status.idle": "2026-09-11T14:54:11.736717Z", - "shell.execute_reply": "2026-09-11T14:54:11.736203Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# Install packages if needed\n", - "repos <- c(CRAN = \"https://cloud.r-project.org\")\n", - "\n", - "if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\", repos = repos)\n", - "if (!requireNamespace(\"sensemakr\", quietly = TRUE)) install.packages(\"sensemakr\", repos = repos)\n", - "if (!requireNamespace(\"ggplot2\", quietly = TRUE)) install.packages(\"ggplot2\", repos = repos)\n", - "if (!requireNamespace(\"ranger\", quietly = TRUE)) install.packages(\"ranger\", repos = repos)\n", - "if (!requireNamespace(\"dml.sensemakr\", quietly = TRUE)) {\n", - " remotes::install_github(\"carloscinelli/dml.sensemakr\", upgrade = \"never\")\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "267a6538", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:54:11.745657Z", - "iopub.status.busy": "2026-09-11T14:54:11.738176Z", - "iopub.status.idle": "2026-09-11T14:54:11.759105Z", - "shell.execute_reply": "2026-09-11T14:54:11.758692Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dml.sensemakr 0.2.2 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sensemakr 0.1.6 \n" - ] - } - ], - "source": [ - "suppressMessages({\n", - " library(sensemakr) # attached first: we only need add_bound_to_contour()\n", - " library(dml.sensemakr) # attached last: keeps its methods for class \"dml\" unmasked\n", - " library(ggplot2)\n", - "})\n", - "\n", - "options(scipen = 99) \n", - "\n", - "# figure sizing inside the notebook\n", - "options(repr.plot.width = 7, repr.plot.height = 6, repr.plot.res = 150)\n", - "\n", - "dir.create(\"figures\", showWarnings = FALSE)\n", - "\n", - "cat(\"dml.sensemakr\", as.character(packageVersion(\"dml.sensemakr\")), \"\\n\")\n", - "cat(\"sensemakr \", as.character(packageVersion(\"sensemakr\")), \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "a7079423", - "metadata": {}, - "source": [ - "## Setting\n", - "\n", - "The outcome $Y$ is net financial assets and the treatment $D$ is eligibility to enroll in a 401(k) plan. The observed covariates $X^o$ include income, age, education, family size, marital status, two-earner status, IRA participation, and home ownership. The identifying argument used in earlier work is that, after conditioning on these worker characteristics, eligibility is approximately as good as randomly assigned.\n", - "\n", - "However, the threat is that unobserved firm characteristics $A$ may affect both eligibility and financial assets, for example through employer matching policies. Conditional ignorability then holds given the full $X = (X^o, A)$, while the data identify only functions of the short regressor vector $W_s = (D, X^o)$. What we can estimate is the short parameter $\\theta_s$. The task is to bound its\n", - "distance from the causal target $\\theta$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "641048b9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:54:11.760854Z", - "iopub.status.busy": "2026-09-11T14:54:11.760463Z", - "iopub.status.idle": "2026-09-11T14:54:11.771931Z", - "shell.execute_reply": "2026-09-11T14:54:11.771465Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "households: 9915 covariates: 8 eligible: 37.1% \n" - ] - } - ], - "source": [ - "data(\"pension\")\n", - "\n", - "y <- pension$net_tfa # Y: net total financial assets\n", - "d <- pension$e401 # D: 401(k) eligibility\n", - "\n", - "# X^o: the observed worker characteristics\n", - "x <- model.matrix(~ -1 + age + inc + educ + fsize + marr + twoearn + pira + hown,\n", - " data = pension)\n", - "\n", - "cat(\"households:\", nrow(x), \" covariates:\", ncol(x),\n", - " \" eligible:\", sprintf(\"%.1f%%\", 100 * mean(d)), \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "5be9f408", - "metadata": {}, - "source": [ - "## Short Estimates Under Conditional Ignorability\n", - "\n", - "### Partially Linear Model\n", - "\n", - "The first specification is the partially linear model,\n", - "\n", - "$$Y = \\theta_s D + f_s(X^o) + \\varepsilon, \\qquad \\mathrm{E}[\\varepsilon \\mid D, X^o] = 0,$$\n", - "\n", - "with the nuisance functions modeled by random forests, cross-fitted over five folds and repeated five times." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f4dad1cf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:54:11.773658Z", - "iopub.status.busy": "2026-09-11T14:54:11.773277Z", - "iopub.status.idle": "2026-09-11T14:54:38.547333Z", - "shell.execute_reply": "2026-09-11T14:54:38.546902Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Debiased Machine Learning\n", - "\n", - " Model: Partially Linear \n", - " Cross-Fitting: 5 folds, 5 reps \n", - " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 26.663%), treatment (ranger, R2 = 11.588%)\n", - " Tuning: dirty \n", - "\n", - "Average Treatment Effect : \n", - "\n", - " Estimate Std. Error t value P(>|t|) \n", - "ate 9038 1326 6.815 0.00000000000944 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", - "Note: DML estimates combined using the median method.\n", - "\n", - "Verbal interpretation of DML procedure:\n", - "\n", - "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "set.seed(1)\n", - "dml.401k.plm <- dml(y, d, x, model = \"plm\", cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", - "\n", - "summary(dml.401k.plm)" - ] - }, - { - "cell_type": "markdown", - "id": "994cf0bc", - "metadata": {}, - "source": [ - "### Nonparametric Model, with Income Groups\n", - "\n", - "The second specification drops the additive structure. The short regression\n", - "$g_s(W_s) = \\mathrm{E}[Y \\mid D, X^o]$ is left unrestricted and the target is the average contrast,\n", - "\n", - "$$\\theta_s = \\mathrm{E}\\big\\{g_s(1, X^o) - g_s(0, X^o)\\big\\},$$\n", - "\n", - "estimated with the same nuisance learners. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c886bdf1", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:54:38.550249Z", - "iopub.status.busy": "2026-09-11T14:54:38.549906Z", - "iopub.status.idle": "2026-09-11T14:54:38.554742Z", - "shell.execute_reply": "2026-09-11T14:54:38.554353Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# income quartiles\n", - "g1 <- cut(x[, \"inc\"],\n", - " quantile(x[, \"inc\"], c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE),\n", - " labels = c(\"q1\", \"q2\", \"q3\", \"q4\"), include.lowest = TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e281ccde", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:54:38.556283Z", - "iopub.status.busy": "2026-09-11T14:54:38.555936Z", - "iopub.status.idle": "2026-09-11T14:55:06.503086Z", - "shell.execute_reply": "2026-09-11T14:55:06.502651Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Debiased Machine Learning\n", - "\n", - " Model: Nonparametric \n", - " Cross-Fitting: 5 folds, 5 reps \n", - " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 25.821%), treatment (ranger, R2 = 11.499%)\n", - " Tuning: dirty \n", - "\n", - "Average Treatment Effect : \n", - "\n", - " Estimate Std. Error t value P(>|t|) \n", - "ate 8252 1187 6.95 0.00000000000365 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", - "\n", - "Group Average Treatment Effect: \n", - "\n", - " Estimate Std. Error t value P(>|t|) \n", - "g.ate.q1 4560.8 806.8 5.653 0.0000000158 ***\n", - "g.ate.q2 2801.4 1310.0 2.139 0.032474 * \n", - "g.ate.q3 6806.1 1835.0 3.709 0.000208 ***\n", - "g.ate.q4 18597.4 4071.7 4.567 0.0000049363 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", - "\n", - "Note: DML estimates combined using the median method.\n", - "\n", - "Verbal interpretation of DML procedure:\n", - "\n", - "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "set.seed(1)\n", - "dml.401k.npm <- dml(y, d, x, groups = g1, model = \"npm\",\n", - " cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", - "\n", - "summary(dml.401k.npm)" - ] - }, - { - "cell_type": "markdown", - "id": "ef3e8146", - "metadata": {}, - "source": [ - "## Group Effects by Income Quartile\n", - "\n", - "Plotted against income quartile, with sampling confidence bounds under conditional ignorability,\n", - "the pattern in the summary above is easier to read.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5c41f860", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:06.505709Z", - "iopub.status.busy": "2026-09-11T14:55:06.505362Z", - "iopub.status.idle": "2026-09-11T14:55:06.510354Z", - "shell.execute_reply": "2026-09-11T14:55:06.509927Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "group.names <- setdiff(names(coef(dml.401k.npm)), \"ate\")\n", - "\n", - "df <- data.frame(groups = 1:4, estimate = coef(dml.401k.npm)[group.names])\n", - "cis <- setNames(as.data.frame(confint(dml.401k.npm)[group.names, ]), c(\"lwr.ci\", \"upr.ci\"))\n", - "df <- cbind(df, cis)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "522bd397", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:06.511809Z", - "iopub.status.busy": "2026-09-11T14:55:06.511512Z", - "iopub.status.idle": "2026-09-11T14:55:06.689836Z", - "shell.execute_reply": "2026-09-11T14:55:06.689403Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "image/png": 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QebROTSMa6uPQEH/zqMvnpyHOlUc0WPssL/vMB5t9voPRPe6xhSqf2XyMaAjKsdE7ga8LQgW7vZ/2mbdjVn71312WwEbbtG/f3kszZsyYKufyBgEEEEAAAQQQQKB+AqrfaZxVaIFCBhrc8/kZq29BBPuD3G6sK29XX321t999e5hy305WPpR+7b5F9NLY+ZUDDX/5y1/S+21IdKbNbjjdfA1eOnu8ofJW+UbJfZtc+VCdX7tvWL287RGITJv7xjzVtm1bL409QmHb8OHD0/V2oz0ynZay89w37V46N9qhSprg5so8cvUOHk/p3LlzlTyDN5Vv4AsdaGiIQ+V62rWVaXPP16edKwdVLBjk5u/wAlj26EdNW3Dd7bLLLlWS5DPQYHVxc3FUuT6qFObeuFEB6aBHtkBDrg4N8bc61vb5aahz5UCDBcAybfY5MRP7LASfL0tXrECDBamCIKoFRatvwSMv9vvNHvFiQwABBBBAAAEEEGi4QCP3xx9bggTcDXV6Yr7qzbYh9bbZJG6VN5tQzjb3Db5s0rtMm/sWPtNub8i2HbCJ/2ziwUybTQRpjwfY5m48MyXxHpewYdC5bvbYQzBR3AUXXJDxdHsExI2U8CZMvPHGG700NgmjbVam+9bde139HzuvR48e3m6bSC7Tlqu3G6UhexzAtuBRler52mMt9uhHMbZ8OdhjOpm24JqzYwsXLkwnsSHx9siNDb8fOnRoen/1F8EKG4Vc3tAmobR62OYCYdWr4L23iU1PO+20jMcq78zVIV/+9rhRps9PPp1renzIPiennnqqx2CTuRZ7s989Bx98sFds5Uejgnr8+c9/9l7aZ2rjjTcOdvMTAQQQQAABBBBAoAECzNHQALxCnmoz9ee62U11bZvN1l/TZjdLttkz8JW3INBgs/zXtNmz95m2YJ4BW8ou2/PZwczwNl9Dps1uSG02/1w398hD+iaxpiCJ5Vm9/jYHgG02J0O2LThu8yrYaghuCHaV5Ll6B9aWSW3eNjdGobdCOwTXnLWj+nVn+yrPIWFzelh97Jl/u/m3uQvct+KWzJtHw3tRgH/eeustL1e7Kbeb1pq2HXbYoaZD6f01XQ81OeTLv7bPTz6cbd6Vmrbgs2croFg/2++DYm5nnXWWt1qNXTMWzLO5aWyzoKrNe2KbpWFDAAEEEEAAAQQQyI9Acf/ay0+dE5GLTX4XbLYMXF02m8wx2CqfH+yzn9kmEcwU3HBDq72lKO3cbN/22RKDttRj8M2vpbctCDTYzeGIESO8fdn+sbbaBHvVl3Cs6QYtW152zA3fTyex5THrstm343YzYltwQ1LTeZXrZUGS6oGGXL2D0Rd245dpScCgHnVtS5C+Pj+L4ZDpmqtcV+s/m2zTrYay2kgbS2ejYWyp1kJuwTW0/vrreyNraiqrLn1S0/WQySGf/pWv00z1z4dzts9KYGOTuH777beqrT6Z6tiQfW4eGLlVa7wJX6dNm6ZLLrnEy84mIrXrp02bNt4kkg0pg3MRQAABBBBAAAEE/idAoOF/FqF61a5du3R9qj/KkD5Q7YXdoAebzbqeact0Q5MpXbDPRhFYAGH58uWqbXh6phEHQXluQki558WDbLP+dJMrrnbcTeS22r5cd1gb6rLZt63WFhshUts5tlJFsGWqd9D+IE1tP4MAkX3ra0GeTKaWR037a8s/l+OldLB6Pvzww97NnznYZsEsNxeDtyJK8PiIraASPO7iJSrAP3b922aPtLin1VRTn9qoltq2ms7NdF4+/bN9fvLlbEGEmrbKwSB7lKLYm5sjQhZsuOOOO1Q50BA8NuEmSPWCVsWuF+UhgAACCCCAAAJxFSDQENKeDZ49t+oFw6drq6p9U2ib3ZBluumt7fyajtvyiLY0ZPAtf6Z0dhNW+Rn7II0NmbblCa1O2Z61D9Ln++fWW2+dztJ8Ks8JkD7gXlgdy8vLZUPY7QbP0tljF1999VXlZKu9rmxi33g3dHMT6nlZWKDBlh20pfkybbZcX6G3UjrY6By7+bMgg/WFLT94wAEHrNbkwL8ujw2tdnIddwSPsFigza6Tmvo5qEsds601WTH88+n8008/eZ/zTA0LbGzEj1vBJlOSgu+zZTAt0PDhhx/q/fff90YMvfTSS165NS2rW/BKUQACCCCAAAIIIBBTASaDDGnH2jDk4CbTho3X9s26zXFgzx/bduCBB+a1VcHNr1t9wftGN1PmwfDy6seCmzSb1M5u5Gva7rvvPrml5fR///d/NZZR07nZ9lcONATP2mdKb/NH2KMhwZwLQb1tkkj7FrumLbhRscccKj9nX1P62va7VSTSoxVee+21GpPX5F3jCfU8UCoHm1wz+BZ88uTJGYMM1qQgCBeMeqhnM7Oe5la/SB93KxSkX1d/8dhjj1Xf1eD3hfbPp3O26/WVV17xLCp/HhuMk2MGNhltUP6DDz7ojZixz7YZZwpi5Zg9yRFAAAEEEEAAAQQqCRBoqIQRtpdHHXWUVyWb8M4mKqvphtdustxa8enJ9I477ri8NmW//fbz8rNvAqdPn54xbwsSZNrsW2nbbKLECRMmZEoiG3J+7rnnyi1jqNtvv73GoekZT65lpz1CEsy275bpVKbh3fb4g9142BY83hHU2775dEuNZizFjtkz3rbZagL2DXRDNxumv+uuu3rZXH755RknObTgR02rXGQrPxjlUtdHcSyvUjkE34BbHWwehkybBaeCESeZJpLMdE599tnjGkcccYR3qvVJ5cdlgvwsQJYtkBWky/Vnof3z6WyPsGQKJtr1aiOibOvWrVuuBHVKH1zbljjb9W2/R22zz/vf/vY37zWjGTwG/kEAAQQQQAABBPIqQKAhr5z5zWz48OEK5lq4++67vSUg7Y95mzDQbs7t57333it7tMGeO7bNvhEP/pjOV206duyYXrrPlnOs/M2t3eDZspFPPfVUxuLsBs3Ot23EiBFesKFywMTaYMdtSLo9vz5w4MCM+TRkpwUYbE4Du6k68cQTNXfu3HR29vr000/3HlOw57i7d+/uHevcubNsXgnbrM3Vgw2vv/66LBBkQ/btG9HLLrvMS5uPf2677TZZXSyQYc+VV76xnTFjRnqpwFzLsgnvbLMbYvvPHiWp7Qa9VA6VRxFceeWVVSYZtcCaBbxsycTgWqrL/Ai5elVObwEGW3Xiiy++0F577eUt22rXjj1eY31v11AhtkL759PZRjxZYKTyhLD2OTn55JM9GvvddMYZZxSCyZvMMcjYgn/22JH9Tqm+WVDB+vHNN9/0fmfZ60L1XfWyeY8AAggggAACCCRKwP2hzhZigSeffDLl5jewsfu1/uduGlJuCcCMrXHfuHvnH3bYYRmP2043osBL41Z8WC2Nez495UY2pOvg5l5IWV5B3dxIgJR79to77paLq3K+u8FPucn70ue6mfdTv/3tb1Nu5vn0Pmvf+PHjq5xnbyxfO3bMMcesdiyXHWPHjk25YIOXl/uGPOWCCKlDDjkk1bZt23QdXDCnSpZuBEfKLWeYPr7RRhulXOAk5QILKRcU8fZvueWWGc0b6u2+cU25myCvDHO1urqRDt4+q795mIu7eatSZzf6JV1fN+qhyjE3oiR9LLie3A2Xl+akk07yjlk51bf6OrjHO9LluaHz1bP13rsRJuk0N998czqNC+CkDj/88PQxtwpH6uijj0654E7KBUy8/e5Rl5RbSSCdxt1cps93o1i8/S6wlN5XlxfZHNykiemyA7/gpxuSnwrOtc9E5a0hDpZPff3t3No+Pw11doGvtL8Linmv3eiC1MEHH5xyjyGlPydu1YnU22+/bVVKbzVdqy+++GI6TxeITKe3F4Fx9evUjaRI/y4K+uTYY4+tcm7wpvJ1deihhwa7+YkAAggggAACCCCQRwFGNLi/SsO8uZt579tne7SgpuUO3c2wbPZ9m6Mh2xKUDWmnDU229eZPO+00b3SAfZNroxhsOUr7ltKGItf06IDNNWFzNNjjHTYppH3TaEOpg2HvNiLDhjEPGDCgIVXMeq6NlDAfW63AJvWz4dzPPfecNzLEyrdnyINh6kFGO+ywgzdq5LzzzpOtBmHfkj7++OPekp1Nmzb1RhtYHoUwt8dfpk6dKhfI8EY0WDk2+sOWCbQRJS5QE1Szzj9t5EmXLl2qTMYXzHGQLZNSONjoFhvFc84553jXm/XZo48+qmCOhL59+3rzM9hIg+C6s/SF3FxwR/YNfa9evbT33nt714TNX9KvXz/ZSJNgPgUXvMprNQrpn09ne+zJPmfWHy5Y4I3Isc/JCSecIBdkUOXRE3kFcplZOTaXTTAHg+Vf07Vtk0IGW75HfwX58hMBBBBAAAEEEEi6QJkFLZKOEJX22/wCNunjrFmzvOH/Nvu9+5bdu/m0G4ZibTa7vP0RbzcUdpOebem8THWyZThtvgd79t5upCuvsJEpfb73WXDE5r2wm1e7ibOZ8Gvb7GNij1588sknsqVHbTWN4LGW2s5tyHF7TMDKtMccdtxxx7wENexxjy+//FJ2Q+xGl+RUvVI42GMRn3/+uWyFhO222042UWoYNwtA2KNNdkNtN9aF2Arpny9nexzHJiu1a9csKs+fUAiT6nnaIy1z5szxPiuZyn7ggQe8R6jsUSL7XRQsX1o9H94jgAACCCCAAAII1F+AQEP97TgTAQQSIGBzZLgh+15wyYIJFpyqvtkkiDZa5uOPP5bNAzBlypTqSXgfEgEbLWQjqGy0jAWG2BBAAAEEEEAAAQTyL9DwafLzXydyRAABBEIj4ObI8EbAuPlSvMdm7PGZ6ps9wmFBBtuC1WKqp+F96QVstRZ7xMI2exyNDQEEEEAAAQQQQKAwAgQaCuNKrgggECMBe67flnB94okn5CY6lJtQ0Ft9wlbvePbZZ735Pqy5Np/HKaecEqOWR78pNrrE5oyYPXu2nnnmGW+lGFvNJVhGNvotpAUIIIAAAggggED4BHh0Inx9Qo0QQCBkAjavhU0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", - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 450, - "width": 525 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "p1 <- ggplot(df, aes(x = groups, y = estimate)) +\n", - " geom_line() +\n", - " geom_ribbon(aes(ymin = lwr.ci, ymax = upr.ci), alpha = 0.1, col = \"blue\", fill = \"blue\") +\n", - " theme_bw() +\n", - " xlab(\"Income Groups by Quartiles\") +\n", - " ylab(\"ATE\") +\n", - " ggtitle(\"Under conditional ignorability\")\n", - "\n", - "ggsave(\"figures/401K-groups-noconfounding.pdf\", p1, width = 4.5, height = 4.5)\n", - "p1" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "90b555b3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:06.692037Z", - "iopub.status.busy": "2026-09-11T14:55:06.691679Z", - "iopub.status.idle": "2026-09-11T14:55:07.062469Z", - "shell.execute_reply": "2026-09-11T14:55:07.061975Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RV_alpha (alpha = 0.05, threshold theta = 0)\n", - " partially linear : 5.5%\n", - " nonparametric : 4.7%\n" - ] - } - ], - "source": [ - "rv.plm <- robustness_value(dml.401k.plm)\n", - "rv.npm <- robustness_value(dml.401k.npm)\n", - "\n", - "cat(sprintf(\"RV_alpha (alpha = 0.05, threshold theta = 0)\\n partially linear : %.1f%%\\n nonparametric : %.1f%%\\n\",\n", - " 100 * rv.plm[\"ate\"], 100 * rv.npm[\"ate\"]))\n" - ] - }, - { - "cell_type": "markdown", - "id": "99f710a6", - "metadata": {}, - "source": [ - "## The Main Confounding Scenario\n", - "\n", - "The main scenario assumes that omitted firm characteristics explain as much outcome variation as the\n", - "maximum employer match accumulated over three years, which implies approximately $C_Y^2 = 0.04$. On\n", - "the treatment side it allows an additional 2.5 percentage points of eligibility variation beyond the\n", - "observed controls, corresponding to $q_\\alpha \\approx 0.03$. We take $|\\rho| = 1$, the conservative\n", - "case in which the two induced errors are perfectly aligned.\n", - "\n", - "What $q_\\alpha$ measures depends on the target: for the partially linear model it is the\n", - "nonparametric partial $R^2$ of the treatment with the omitted covariates, and for the\n", - "binary-treatment ATE a gain in average inverse-variance precision.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8a03f2af", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:07.064135Z", - "iopub.status.busy": "2026-09-11T14:55:07.063812Z", - "iopub.status.idle": "2026-09-11T14:55:07.069566Z", - "shell.execute_reply": "2026-09-11T14:55:07.069163Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "cf.y <- 0.04 # C_Y^2\n", - "cf.d <- 0.03 # q_alpha\n", - "rho2 <- 1 # |rho| = 1\n", - "\n", - "bounds.plm <- dml_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "eb62473e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:07.071060Z", - "iopub.status.busy": "2026-09-11T14:55:07.070750Z", - "iopub.status.idle": "2026-09-11T14:55:07.077621Z", - "shell.execute_reply": "2026-09-11T14:55:07.077164Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "bounds.npm <- dml_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "b6c06ec7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:07.079155Z", - "iopub.status.busy": "2026-09-11T14:55:07.078850Z", - "iopub.status.idle": "2026-09-11T14:55:07.090590Z", - "shell.execute_reply": "2026-09-11T14:55:07.090101Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "cb.plm <- confidence_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)\n", - "cb.npm <- confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6a24c23a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:07.092294Z", - "iopub.status.busy": "2026-09-11T14:55:07.091969Z", - "iopub.status.idle": "2026-09-11T14:55:07.102416Z", - "shell.execute_reply": "2026-09-11T14:55:07.102009Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 5
ModelShort ATEBias boundATE bounds95% conf. bounds
<chr><chr><chr><chr><chr>
Partially linear9,0384,195[4,845, 13,231][2,636, 15,514]
Nonparametric 8,2534,533[3,709, 12,796][1,695, 14,851]
\n" - ], - "text/latex": [ - "A data.frame: 2 × 5\n", - "\\begin{tabular}{lllll}\n", - " Model & Short ATE & Bias bound & ATE bounds & 95\\% conf. bounds\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t Partially linear & 9,038 & 4,195 & {[}4,845, 13,231{]} & {[}2,636, 15,514{]}\\\\\n", - "\t Nonparametric & 8,253 & 4,533 & {[}3,709, 12,796{]} & {[}1,695, 14,851{]}\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 5\n", - "\n", - "| Model <chr> | Short ATE <chr> | Bias bound <chr> | ATE bounds <chr> | 95% conf. bounds <chr> |\n", - "|---|---|---|---|---|\n", - "| Partially linear | 9,038 | 4,195 | [4,845, 13,231] | [2,636, 15,514] |\n", - "| Nonparametric | 8,253 | 4,533 | [3,709, 12,796] | [1,695, 14,851] |\n", - "\n" - ], - "text/plain": [ - " Model Short ATE Bias bound ATE bounds 95% conf. bounds\n", - "1 Partially linear 9,038 4,195 [4,845, 13,231] [2,636, 15,514] \n", - "2 Nonparametric 8,253 4,533 [3,709, 12,796] [1,695, 14,851] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dollars <- function(v) formatC(round(v), format = \"d\", big.mark = \",\")\n", - "\n", - "row <- function(bounds, cb, label) {\n", - " b <- coef(bounds)[, \"ate\"]\n", - " data.frame(\n", - " Model = label,\n", - " `Short ATE` = dollars(b[\"theta.s\"]),\n", - " `Bias bound` = dollars(b[\"bias.bound\"]),\n", - " `ATE bounds` = sprintf(\"[%s, %s]\", dollars(b[\"theta.m\"]), dollars(b[\"theta.p\"])),\n", - " `95% conf. bounds` = sprintf(\"[%s, %s]\", dollars(cb[\"ate\", 1]), dollars(cb[\"ate\", 2])),\n", - " row.names = NULL,\n", - " check.names = FALSE\n", - " )\n", - "}\n", - "\n", - "table2.3 <- rbind(row(bounds.plm, cb.plm, \"Partially linear\"),\n", - " row(bounds.npm, cb.npm, \"Nonparametric\"))\n", - "\n", - "table2.3\n" - ] - }, - { - "cell_type": "markdown", - "id": "89ff8ba9", - "metadata": {}, - "source": [ - "Even granting an adversarially aligned confounder as strong as three years of maximum employer match,\n", - "the lower confidence bound on the ATE remains positive in both specifications." - ] - }, - { - "cell_type": "markdown", - "id": "d6f07816", - "metadata": {}, - "source": [ - "### Group Effects Under the Scenario\n", - "\n", - "The same groups again, now with the sensitivity bounds in red and the wider confidence bounds in\n", - "blue.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b0a3776", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:07.104074Z", - "iopub.status.busy": "2026-09-11T14:55:07.103749Z", - "iopub.status.idle": "2026-09-11T14:55:07.236165Z", - "shell.execute_reply": "2026-09-11T14:55:07.235716Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 450, - "width": 525 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "bds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0)),\n", - " c(\"lwr.bound\", \"upr.bound\"))\n", - "cbds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0.95)),\n", - " c(\"lwr.cbound\", \"upr.cbound\"))\n", - "\n", - "df2 <- cbind(df, bds[-1, ], cbds[-1, ])\n", - "\n", - "p2 <- ggplot(df2, aes(x = groups, y = estimate)) +\n", - " geom_line() +\n", - " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), alpha = 0.1, col = \"red\", fill = \"red\") +\n", - " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), alpha = 0.1, col = \"blue\", fill = \"blue\") +\n", - " theme_bw() +\n", - " xlab(\"Income Groups by Quartiles\") +\n", - " ylab(\"ATE\") +\n", - " ggtitle(\"With confounding\")\n", - "\n", - "ggsave(\"figures/401K-groups-confounding.pdf\", p2, width = 4.5, height = 4.5)\n", - "p2" - ] - }, - { - "cell_type": "markdown", - "id": "0ba2ae32", - "metadata": {}, - "source": [ - "The posited confounding scenario substantially widens the range of plausible effects but leaves most of the adjusted region positive. Only the second quartile has a small excursion below zero." - ] - }, - { - "cell_type": "markdown", - "id": "651c1a8d", - "metadata": {}, - "source": [ - "## Benchmarking\n", - "\n", - "Rather than posit $C_Y$ and $C_D$ in the abstract, we can calibrate them against a covariate we do observe. The procedure is as follows: drop the covariate from $X^o$, refit, and read off the gains it supplied. Latent confounding is then described relative to the chosen benchmarking variable." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b4a7321f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:55:07.238274Z", - "iopub.status.busy": "2026-09-11T14:55:07.237941Z", - "iopub.status.idle": "2026-09-11T14:56:25.265989Z", - "shell.execute_reply": "2026-09-11T14:56:25.265444Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "set.seed(2)\n", - "invisible(capture.output(\n", - " bench.plm <- dml_benchmark(dml.401k.plm,\n", - " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", - "))\n" - ] - }, - { - "cell_type": "markdown", - "id": "085c0865", - "metadata": {}, - "source": [ - "`gain.Y` is the implied $C_Y^2$ and `gain.D` the implied $q_\\alpha$. Income is a strong predictor of both eligibility and financial assets; IRA participation and two-earner status are considerably weaker.\n", - "\n", - "The `rho` column records the alignment each covariate actually exhibits. None is anywhere near $\\pm 1$, which is a first indication of how conservative the worst-case analysis is.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9837c2cf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:56:25.271430Z", - "iopub.status.busy": "2026-09-11T14:56:25.271070Z", - "iopub.status.idle": "2026-09-11T14:56:25.284321Z", - "shell.execute_reply": "2026-09-11T14:56:25.283894Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.14100.0503-0.3266
pira0.04760.0075-0.1980
twoearn0.04810.0106 0.2931
\n" - ], - "text/latex": [ - "A matrix: 3 × 3 of type dbl\n", - "\\begin{tabular}{r|lll}\n", - " & gain.Y & gain.D & rho\\\\\n", - "\\hline\n", - "\tinc & 0.1410 & 0.0503 & -0.3266\\\\\n", - "\tpira & 0.0476 & 0.0075 & -0.1980\\\\\n", - "\ttwoearn & 0.0481 & 0.0106 & 0.2931\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 3 × 3 of type dbl\n", - "\n", - "| | gain.Y | gain.D | rho |\n", - "|---|---|---|---|\n", - "| inc | 0.1410 | 0.0503 | -0.3266 |\n", - "| pira | 0.0476 | 0.0075 | -0.1980 |\n", - "| twoearn | 0.0481 | 0.0106 | 0.2931 |\n", - "\n" - ], - "text/plain": [ - " gain.Y gain.D rho \n", - "inc 0.1410 0.0503 -0.3266\n", - "pira 0.0476 0.0075 -0.1980\n", - "twoearn 0.0481 0.0106 0.2931" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "out <- summary(bench.plm)$benchmarks\n", - "round(out[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" - ] - }, - { - "cell_type": "markdown", - "id": "70083858", - "metadata": {}, - "source": [ - "## Contour Plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e65ef369", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:56:25.285890Z", - "iopub.status.busy": "2026-09-11T14:56:25.285593Z", - "iopub.status.idle": "2026-09-11T14:56:25.289940Z", - "shell.execute_reply": "2026-09-11T14:56:25.289562Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "bumps.plm <- list(pira = c(0.010, 0.009),\n", - " twoearn = c(0.020, -0.002),\n", - " inc = c(0.010, 0.009),\n", - " inc4 = c(0.015, -0.005))\n", - "\n", - "bumps.npm <- list(pira = c(0.005, 0.009),\n", - " twoearn = c(0.020, 0.005),\n", - " inc = c(-0.010, 0.009),\n", - " inc4 = c(0.015, 0.000))\n", - "\n", - "contour_panel <- function(fit, bench, rho2, title, bumps = bumps.plm) {\n", - " ovb_contour_plot(fit,\n", - " which.bound = \"lwr\",\n", - " cf.y = NULL,\n", - " rho2 = rho2,\n", - " col.contour = \"blue\",\n", - " label.bump.x = 0.015,\n", - " label.bump.y = 0.003,\n", - " lim.x = 0.16)\n", - " title(main = title)\n", - "\n", - " add_bound <- function(cfd, cfy, label, bump.x, bump.y, ...) {\n", - " sensemakr::add_bound_to_contour(\n", - " r2dz.x = cfd, r2yz.dx = cfy,\n", - " bound_label = label,\n", - " bound_value = confidence_bounds(fit, cf.y = cfy, cf.d = cfd, rho2 = rho2),\n", - " label.bump.x = bump.x, label.bump.y = bump.y, ...)\n", - " }\n", - "\n", - " add_bound(0.03, 0.04, \"Max Match\", 0.015, 0.003, point.pch = 25, point.bg = \"red\")\n", - "\n", - " # one-times each observed benchmark\n", - " add_bound(bench[\"pira\", \"gain.D\"], bench[\"pira\", \"gain.Y\"], \"1 x Part. in IRA\",\n", - " bumps$pira[1], bumps$pira[2])\n", - " add_bound(bench[\"twoearn\", \"gain.D\"], bench[\"twoearn\", \"gain.Y\"], \"1 x Two Earners\",\n", - " bumps$twoearn[1], bumps$twoearn[2])\n", - " add_bound(bench[\"inc\", \"gain.D\"], bench[\"inc\", \"gain.Y\"], \"1 x Income\",\n", - " bumps$inc[1], bumps$inc[2])\n", - "\n", - " # a quarter as strong as income\n", - " add_bound(bench[\"inc\", \"gain.D\"] * 0.25, bench[\"inc\", \"gain.Y\"] * 0.25, \"1/4 x Income\",\n", - " bumps$inc4[1], bumps$inc4[2])\n", - "\n", - " invisible(NULL)\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "2522b94d", - "metadata": {}, - "source": [ - "### Partially Linear Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "445dba1a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:56:25.291459Z", - "iopub.status.busy": "2026-09-11T14:56:25.291151Z", - "iopub.status.idle": "2026-09-11T14:56:25.693510Z", - "shell.execute_reply": "2026-09-11T14:56:25.693103Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "image/png": 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0+Yme770fewMNj/QeGPDzM8p/VwYbvf5kyE4WCEAAAhCAQH0IEGioT1vgCQQgAAEIQKAqAj6KwQMNPn3C3z7xZ6mvdeDSpLdNzPR41l8PNPhIgX6SJE0/G93jv9WHFbpf2EIAAhCAAASGkQCLQQ5jq1NnCEAAAhCAwOwErtHX/8a7fMrE26R+j3C1NM+HcJkrVTaboLRl4mMztO3WfYLkHIIABCAAAQhAICkBAg1JSZEOAhCAAAQg0G4C3bUZtlE13xVXtcmjGbwKe8T1GGvjC0C6+CswHw2f+AMBCEAAAhCAQC4EmDqRC0aMQAACEIAABEolcIRKm56gxL8pzRkJ0nkSDzR8Rrqg9FVSfytGN/igj4nleqVcO079Em39e1XyIhXsozPOHOVAR993k/p6Cl5nBAIQgAAEIACBHAkQaMgRJqYgAAEIQAACJRHw11EmkR8pUdJAw3VK+y9pN0jwZ32+Q9pU8QUe/dWhJ0pvk14l7crR+rCs1F87emd3J1sIQAACEIAABPIhQKAhH45YgQAEIAABCLSBgI9gOCyuSNOnTfjbJl4t/Zr0EqkHGnwtho2kPlpjE6mvQYFAAAIQgAAEIAABCEAAAhCAAAQgUHMCPuLCF1lctCA/L5RdDxYcM4b90+NjPpqhK5P1waeD+PoTS3Z3TrDdTMfcvutCE6TjEAQgAAEIQAACYxBgRMMYUNgFAQhAAAIQgMBABPyh/kZpXRZZnCZfLh+oRmSGAAQgAAEIQCAxAd46kRgVCSEAAQhAAAIQSEDgHUrzFun5CdKSBAIQgAAEIACBFhIg0NDCRqVKEIAABCAAgQoJHKiyfRHKj1XoA0VDAAIQgAAEIFAhAaZOVAifoiEAAQhAAAItJLC96vR4C+tFlSAAAQhAAAIQSEiAEQ0JQZEMAhCAAAQgAIFEBAgyJMJEIghAAAIQgEB7CTCiob1tS80gAAEIQAACbSXwWVVsKelNY1TwMu3zt0U8MMaxpLuuV8Id4sTPJM1EOghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAFBDotqANVgEDeBD4pgzvkbRR7EIAABCAAAQhAAAIQgEDjCPxSHh/VOK8rdniuisuneAjUkYAHGdapo2P4BAEIQAACEIAABCAAAQiURsCfCeiAzIB7UoY8ZIHAMBC4XpXcaBgqSh0hAAEIQAACEIAABCAAgTEJ/HXMvezsS4ARDX0RkQACEIAABCAAAQhAAAIQgAAEIACBpAQINCQlRToIQAACEIAABCAAAQhAAAIQgAAE+hIg0NAXEQkgAAEIQAACEIAABCAAAQhAAAIQSEqAQENSUqSDAAQgAAEIQAACEIAABCAAAQhAoC8BAg19EZEAAhCAAAQgAAEIQAACEIAABCAAgaQECDQkJUU6CEAAAhCAAAQgAAEIQAACEIAABPoSINDQFxEJIAABCEAAAhCAAAQgAAEIQAACEEhKgEBDUlKkgwAEIAABCEAAAhCAAAQgAAEIQKAvAQINfRGRAAIQgAAEIAABCEAAAhCAAAQgAIGkBAg0JCVFOghAAAIQgAAEIAABCEAAAhCAAAT6EiDQ0BcRCSAAAQhAAAIQgAAEIAABCEAAAhBISoBAQ1JSpIMABCAAAQhAAAIQgAAEIAABCECgLwECDX0RkQACEIAABCAAAQhAAAIQgAAEIACBpAQINCQlRToIQAACEIAABCAAAQhAAAIQgAAE+hIg0NAXEQkgAAEIQAACEIAABCAAAQhAAAIQSEqAQENSUqSDAAQgAAEIQAACEIAABCAAAQhAoC8BAg19EZEAAhCAAAQgAAEIQAACEIAABCAAgaQECDQkJUU6CEAAAhCAAAQgAAEIQAACEIAABPoSINDQFxEJIAABCEAAAhCAAAQgAAEIQAACEEhKgEBDUlKkgwAEIAABCEAAAhCAAAQgAAEIQKAvAQINfRGRAAIQgAAEIAABCEAAAhCAAAQgAIGkBAg0JCVFOghAAAIQgAAEIAABCEAAAhCAAAT6EiDQ0BcRCSAAAQhAAAIQgAAEIAABCEAAAhBISoBAQ1JSpIMABCAAAQhAAAIQgAAEIAABCECgLwECDX0RkQACEIAABCAAAQhAAAIQgAAEIACBpAQINCQlRToIlErAFk5XnE2JIpucLs+gqW0elTnvoFbInweBKto/D7+LsGEL6nfZKcJye23aQu2tW1U143+yfPJpr5vlezgcJXIOrm87+7me62N926d9nhFoaF+bUqPGE7CXqwqP6GLwyhRV+YXSnpYifR5JfyAjF+RhCBsDEzhLFs4Y2Eo7DNyqahzSjqqUUYsQoLxH55uPl1HaEJVxuurK+bG0BrfXqSi/bm5aWpEUNAaB8BB7uw4cO8ZBdlVKwOZW8XdKv1OpGxQ+VAQINAxVc1PZhhBYXH76/+ZSKfxdTGmXT5E+j6Q+mmHVPAxhY2ACj8oCN9gzMfpN7g4DEx0aA51pqup/pfsNTZXLqehlKmYTPfhuWU5xQ1/KNSLwkPRbYu4PVEglBDqmYj3wfaDaYZtKXKDQcQh0nteBn0l1rjfO9+NQYne+BAg05MsTaxCoioA/LGgqQ6niD7eLlloihY1H4KqZbWGrjZdgiPafp7quqxupsgNvTUb8Izn/IjHbrMmVqJnvx8kfP0d+umZ+tdSdTpe1/vej/VtayaZU60Ny1EeW6bxiSzTF6SHx84OqpwdBv6m22XhI6kw1KyRAoKFC+BQNgXEIeI+AS5p55tOVvuQ1GnyYaqRRDT4XGamYgAcaXHzazbCLBxpctp654W8CAqcozbPSvROkJUkiAp3HlUw385GG9HNDnwjZ4Il+KBN+Lvw8D7iDw8xuofOE8u4hXVp6fHY75MyfQBjB9nbZfVB6pv5PCMjnDxmLPQQINPTA4CMEakIgS6BhmnyvYkSDI/NpG7WWqdGkN06Pptw9LZpy8QNRtFCtnc3m3NXK5r8bAg1R9BdxeEzKsF1BSCadh5XuV1LdgLIwZDJmiVJ9Q6melH4mUWoSDUigM0MGPiD1kXZfGNAY2Qci0Pmzsh8t3VHnlHcPZIrMORPo3CODb5MuLv2F2ofOopwJYw4CEIDARAT+qoOuFYnP6TU9NNqbkztgZyn9bcnT55HS9o/9XDMPa0XaUJDhXKm5PhtNfmeRZVVn2zTP3v5QXfl1Ktm0MKb5wnDM1U7cLLZ1/P/8nsRZSJiAgOlhK5zPN0iQmCS5ELATxVzz0e1luZjDSEYCvtCsae0MU+DXVslohGyFETCd68O5yRf2RiYm4M8ErkhKAoxoSAmM5BAogUDTRjR471GtxSJ7vOugTnptXcfAhwzzMDOzoc/Vxn+XG838yt8EBM5XmjuleyVIS5LkBI5R0qlS1mpIzmzQlJ+Ugaekxw5qiPyDEAjD9HeTBZ/WeZIeannmGARn7nk7HmD4rnQftc0BuZvHIAREgH96fgYQqB+BLIGGqtZocHoNCDRE/+s2s0WdlbufW7b1QMNiumFYtWX1ylKd38WZmD6RmF4Ydq6e4Ghj/YZenDgbCfsQ6NynBH5Dv4O4rt0nMYdzIdC5V2YOl75GzN+Vi0mMZCTQuV4ZPyX1tyJ9PKMRshVHwBeHvFT6Df2vbFJcMVgeVgIEGoa15al32wiwRsMELdqJbCTQ0ImiNgcanALrNEQd75m/TqrpAEgKAicorQc6906Rh6T9Cfhc9eekB/dPSoqcCOjBKbpR6lNXFszJJmayEfC2uED6ebXFetlMkKsYAh3vpPL1Gu6X+pTDFxRTDlaHlQCBhmFteepdZwJ+o++iZ+LE4heLst868WjsXe1HNMyI5hoJNGgaRVsDDSwIOfu/i0+f0FQS85XPkUQEOjcr2Z+kWjHeJiXKQqIEBDp3KNGJ0l3EdbUEGUgyMIHwAPUhmVle+tmBzWFgAAIdv6fZU/q09GT9D7D4oEDUR8Koqx3lj9/LsThkfRqmFZ4QaGhFM1KJlhHIEmiockRD7QMNo0Y0rKTfS5ogTkN+XuE98rfIWUY0zGyx8+J2fmNDGrAubv5IjiwrZdpJvi1yZGzOh5EjpRDo+BSqs6Qf0cPtmqUUSSHjEAijzN6ngy+RfmmcROyujEDnChXt7bOh1P9nEAhAAAIQKIjAX2XXtSKxzXRTpGCD7ZDcAftqnKfEB+iworT7eXRyP6tJ6a+07L51wrd635w/SLVQ7FS1x8MtrFiGKoXf5xPi8dMMmYc4i80vZo9JudnM/Vdg3puroHBrR1XlTmxwg87a1JNuPnQfqZyAn49thnSryl3BgTEImEYChvvPw8c4OMy7Kn4uGGb01B0C7SNQ8QklU6DhyPjiUPKQRNPK3vb9JvwEpkeTH+oGG6ZF82zUBJ/T+2habCvcJLwofd425rBfisdD0rnbWLvi6mTfFTNNx7KWBuSKIzexZe9VD69dFF+kPAKmtTHCeXGX8sqkpLEJ2CJqC01ltLukS4ydhr3VEbB51C6XSD0Y9Pbq/KhdyRU/F9SOR2KHmDqRGBUJIVAagSxTJ3yNBhddJEqVR1XaoqWWmLEwQdXNzUxp8Zsnroyr+IpuXYd86+s0LC591ZBzSFt9f0uCr9GwZ9qMpJ+IQOcGHf25dC/dxK80UUqO5UrgK7L2H6mP/FsoV8sYS0mgo9FS0e5SD2I2opMiZQUbnjysbfJmVeI66U/0/7JRwyuE+xUTINBQcQNQPATGIJAl0OBrNLiUvSDkIypzsVByzf9oTsltXRd14lul+7ll26tVH//9EGiY2bDnxO37pnjLJhGBzt+U7B/SffRz0r8OkiOBz8uWB3HUy46UQ6Dj18f3S5eTHlZOmZQyPoGOeswjjcIMr3x97/jpOFINgc7jKndbqd/f/VrXgFWr8YNS20CAQEMbWpE6tI1A0wINDRnRYLd1fyga0bBK93O7th2tSRBe6UagITRsWIDMH5i9hwZJR8BHNfgUnNemy0bqiQl0/q3jp0n30g38ihOn5Wh+BDq/ly3n/gFxXzc/u1jKSOAQ5btC+jW1h6YUIfUiEN6U49fN+aW/VRs1okOpXgzxxgkQaOB3AIF2EKhy6kRDLkBz3dZtanXRrtL93MKt90ZvoBsDzu8zG/c32qwnHjzUpfuxn6LkU6XvSZeN1AkIHK40jGpIACrnJB+VPf9Nf1vnA10GkOoIdJ5T2btKtRZA5AtElj0as7qqN6bkjo+QfKd0Dekv1EZzN8Z1HK0NAW5Ea9MUOAKBEQJNG9HQiECDhQWoZjLW5zbPj/ZAg89D9psDJIo80ODiQ0GRxAQ6Pmz2TOnbdIPZiP/xxFWrPGEY1XC63NhbbAmAldYenbtU1KHSTaTvliKVEujcrOLfL32ZlFdeVtoW4xXeOVtHPij1UZJLjpeK/RCAAAQgkJxAxavL2qt186lgg+2U3GXbL86zVvI8eaS0b6rc56W17x16JopW0psnpsdvnrgoj9rX04a9Jv4t7FZP/8r2ykd22P3SbsChbAcaXJ5tHv+W/GEAyZWArS22fu48LlezGOtDwDSSxK6V+jmBAFofWuUctp+pLfwtB28opzxKSU+g/vd46euUKkfFzwWpfK1VYkY01Ko5cAYCmQl0F4Ms+60T3uvp55GFM3teUsb5ouj256IZW+tm5nPTo2fb/BB+jZDqASZ6ZUloa15Mx4fm+qKQW6jt9TNAUhD4o9LeJGX6RApoyZJ2/qV0PqrBF9xcIVkeUg1OIAzZP1B2lpJ+YXB7WMiBwP6ycbv0RP0veLsgtSPQ6Y60rZ1nOFRvAgQa6t0+eDecBLon9DSjBLqBhrLnOXqgwaURPUPzRc9dME807fNa3ciH0LZUOk+rYr7gnA91RGYS+K02HmTYEiBpCISbyx8px3p6AOD3lAZdsrTdN1B8KllyUuVDILz14Cey5SMB+V3nA3UAK+GVl++SAQ8y+PkGgQAEWkKAQENLGpJqtIoAgYZWNWcllblSpWreqw8TRkTgd1JfMHU7aKQm8GPleE66b+qcZOhDoHO9Epwh1YiRVq8b04dDJYc/oVIfl35X7LkXrqQJegvtXKpvR0jfrPbwNQGQWhIwX7NngVq6hlO1JMDJtZbNglNDToBAw5D/AHKo/hWy4T346+RgqwUmwnvBfRqA38SmGSnUgroPWoXOPbLg61to9XFbcFBr5J+DwKHa4wHBz8xxhB0FEujcJ+MHS18ufV+BBWE6OQEf4fMn6dE616yfPBspyyFgi6icb0tPVfvMXU6ZlNJ0AgQamt6C+A+BmQS6r7dk6gS/CCfgIxpcNpy54a8I+OrZy0v16k8kJYHjld7fZKJgA5IvgfAGCi2GF+2pm/cX5msba30IfE/HPSirtRpsuT5pOVw4gY6vLeRTKHz6nz/M0nNeOPM0BYQpLtsrx5bSr6XJSdrhJUCgYXjbnprXl8AgIxrmKblajVqjoWQ2VRb3TxX+rPSVVTpRs7I90ODC9ImZHNL8/Z0S3yb1RduQ/AkcJpN+P/a5/E1jcXwCYaFYrdMQ+QMtD07jgyrxSOcOFbaP9MXSY0ssmKISEeh4YG436XsVCFprVhbbQ98fkv5c+oFYfbQQAgEIQAACowj8Vd9dKxJfnCq83tIj+wnFNovz7JAwQ07J7AVxuT7fFakVAdNv2K6plUuVOxNea3dV5W400gHTMPNwXiJ4VUj72Qniq7UwbPVCzGN0AgKmIEP4bfN6xQkolXvINEQ/tMk7yy2X0pIR8Hu/Xgmvjd1RbfZ9qZ/HZkjbtNZGxc8Fvaz5DAEINJ1AxScUUxQ4XGA9apxQbKM4z84JM+SUzOaPy/1iTgYxkxsBU2+QTZf6Wg1IIGD6nYb/rVE3SeDpT8CWFbtp0h/0T0uK9ARsVbH1/9efpM9LjsEI2ELifqf0Jinny8Fg5pTb5lVbaGSePSbV/wZSfwL+v2NnSp+R7lR/f1N5WPFzQSpfa5WYqRO1ao7WO+PDE703zOd3rdD62pZbQd2gBplcbrHhVYp6+GjG6y3LZVN5ab5Ogy8yp7dPIDGBs+PtdhBJS6Bzr3L8SqoexrAoWFoDpJ+QQOcWHdaohmhX8V1zwqQczJlA5wkZ/IB0NelnczaOuUwEOlOVbRepX8NO1f9E2dNCM3k9vJlsGdX9Yumm0i2iqHOatggEIACBXAksKWs/lY7uie+uqO0XDl9/oKsX6PO60rpJxZFL02J1odd19+Rg7KVxnr2T58krpekBxHQj0Bx5PIqWfGDm4nbNcTq1p/6wEn5HH0qdtbUZ/DV2dr/0t62tYqEVMwWJw2/q/YUWM7TG/RWXprVVmnU+bU9zmQJpYdTOOu2pU9NrYu+JzzlfaXpN2uu/ra02ulV6g3TVltaz4ueCllKlWhBISUA3SSGIMHpRpa/G+x/V9nTpcdILpTOkD0nrttJ2xScUUw90uJnfQ2wSimnhpJBn/4QZckxm/1bZvlhcI+TZaPLbpkeTn5c+qs96GG+r+Gscw7DTk9taw2z1CnPhfWinpv0kFj+3aSpKWLAvcaaUCX3kiaZ2hEXQUmYtK3n4Tf1H7K4tq8ThK8d0fQzzm18yfHWvusa2othrdINdKtX5E6kHAftZ/D/xpnr4gxezCITgs+7t7WLpYrP2t+5Txc8FreNJhSCQiYDfjPtohd5Ag68c/Lz0Oumy0l55jb74kMU/9e6sweeKTyiZAg0K1oRAQwWL79hlKvuKGrRbIhemRZNPnB5N0WRo18mfTpSpsYnsQrWNHgyRWQTMF6zSecreMmvfhJ88IHGN1M9tRQ7f9cCil1Hzm2n7eMzPz99I7gTCArsa/edznZHyCdhH4t/3e8svmxLHJmALq018/QwNRBy9COHYOdhbBgHbS+2hqbN2knRyGSVWWEbFzwUV1nzAojWMFIFAoQQ2knX/nX1Ceu+oktRrEH1e+mqp38wjMwn4w4ZLmh4VneyDVHGyf0QlNyaSLbj3xKz8qe5F3c8t3XoASHVsdU9D2qY7Xxk0PD3aPkFGH0J9sXT9BGmHJckJqqjzO2BYKlxuPTt3qbzvSXfQ/+3Lyi2b0kTgm9KrpUeJ/3IQqQOBjmY7RjtLFXCIfHTD3HXwarh9sAVU/0OlX9CtqoLkne496ARYwtRFreVgUyZIxKGWESDQ0LIGrWF1/MLgMt5QW7+g+0VjTU+EBAJNCzQ8LK8Xb0rbdSK7eZavndVmfW7lJw80eMDKF2FFAoHOk9pcIN1ONzwTXQPfrTR+fvLf9tnSJOKsl5AuMkZit7PoGPsn2uXpu0FYt7uxdKL/NU/jD6fj1cv3e3DNRyOMZae3vKWV5hXSSdKuKJDZeWEUPXmGdrxd/JbqHujZavh5Xz97kvNxDAJf1D5N7/GbeKRcAh0fgbmvdCHpseWWTWnjE+hcpWMaTRUWG/QOKqRSAp2nVPxLdT04bHY3wnph++nacKhUAVP7tfRK6Z1K5wFq73D8shSBAAQgkJrAWFMn/MbYH5y3GcfaMfHxOvUcVDxEytYVMjHzYWlJxVeBD3kOTZojv3T2TZX9nNQfsmovz0STtpg1dWKK9x62WMIwbP8tfbbFlcxQNXtv/P8y0fB/9WhGR0j9gcO3fh7rfejW1zHlXO2dLu3tjd5d3z2/j+waT8aaOqHFtcJUtFO09fxd/dooIx4w86DIjDiN38x9Vdorm+nLv6VdG749VerBia50yztTO7rpntDnTaTvkPrNpfbPrX0f1tY+qe9d8TUF/i7t5vPtydKxgi7ajUxMYORVrJtOnI6jxRAwPQylmmJVjBtYHUUgvD5R5znbetQBvlZOwEdOhnWhbtZW1x/7mHQXqc5htqdU140w1Xapyl1N70DFzwXpHSYHBNpIYCVVym8u/cb0IqlHLXWSif4jvUQ6r7Qr3rPm79l9UOrH6yQVn1CyvEEivL9Y7P3mtGwxRbTDDVkjHijUTbjyrEDD5Bl3RNF8ZRMrtzxTMMXOLrfMupfmQ6LDgntHJ/Q0TaBhWdl8QHqN1AMTL5Q+Lj1POlEwbg8d9/Pnm6RduUEfvBfoHOkbpGtKz5J6ui2lLn7TpvnL0VXS9aW62Yt0gxfS7Kitywukj0gvl64n9eDJztJHpb+XdsXLmyb9pVQBzxAg9mDcQ9JbpHtKPahxolQ+/P5ubfxcvqj0fun1Ure7sHQ7qZ/fT5MiqQmYmJrazP6UOisZciDgi8X66DfvifU1ApB6EAidKt4uOseyXkM92qTXC3u32kXXLHvtrL22q75Plf5c2vscMCtJ/T9V/FxQf0B4CIEyCPjFWCtmR5dJfXiy3wz36t763hVP48eel/beWHePV7mt+ISSKdAwt3CKp32lfHD2obhsf6CqvRyqYeVaBPLZbrBBb55Yp/ZOD+Sg/ULtc99AJlqZ2fTQbTcmrFo30DBPwvRvVTo/v31aeqnU1wXxqQgTyXiBBg9azNeT0R/03fan4n0f1tbPox6E6JUf6ssX4x1+XtaNXrRi/L27OVAf3NaW8Q4PNOjhdmS6hu8+Xupp/s+/xLK2ttr3Haltq8+HzfwegiFxkrD5TLx/3d6dfE5KwNTG4bxet2tk0go0PJ1tFfP/dsMr0jL37eVqF3+YVRDOPJiL1IqAHa52eViqa5LpGhCC+l/SdqJAe61qMIYzFT8XjOERuyAw5AS8l+vFUu/d0gkm9OZtqm1XvPftWunruztqtK34hGIaghxuLvdJxySczDWNoWwxPSAFfzcou+Ss5enNEzd2Aw1ToynbZ7XTjHx2UNw+qzTD37K8tINjLqMf0MdyIG2gwW18X+oP6B4ESHKeGy/Q8Efl75XJ+uJ2vxDv/LG2t8Wfx9vohjzy89po8cCD2/K5zy4eaLgyfJr1x495Gg8udMVHUWjfkU9r81t9dn1K+j7p/j16lD57Xq8bkppA6FW/RwivkTb5Jj11zeuTwU4U+xnSjevjE56oPRQkDfcdR0KjjgTsFLWPOhz1cq/I3jO7h7ab9um6FkYL/UFbf1aou/j1c6xraN39rtw/fxhEIFAEAV2YI+8t/LnUe962lvrNblc214eXSnWSQUYR8Btzl7Q3ltOUZ56Qs9w/ilwH8SHbjZBO1Lm566hOgqt2P7d0q577IK9qaf2yVutXccaiAk2nxvb9/8MDq1nFRxj0ynPxl+71ew19f6A3wRifV9K++8bYf3+8b8meY3qwHVN6y4jPUbf9TSl1bp/s/0Puz0ekPm2jqz514ybpglIkNYGOAjlhfZD1tfWgPVI+gY+qyAelChy2/hV+5dPNXGLn28p6hlQjrYwRP5k5FpHRp31Fy0kXkP5LerJUYstLf60PP5D+Xbqv1J8TrtT+FbRFWkige6PSwqpRpZoTeKLm/jXRPQ80eG9n2dJ9EFq87IKzlqenpJFAg2ysltVOQ/L5w6AH/gg0zNZgnev11X8Hb5ltdz5f/MH6e9LrpPNKvyvNKv0CjnfL8NJjGPcAxCuknv8Oqd/4jRbd+AVxP7vSDXR2v0+wvegyHdR9xBp+3pkq9eDx6mPocdqHZCPgI2Nukx6um/FJ2UyQKzuBzkPK69OTfETPp7LbIWcBBPaRzVukJ/GgWgDdTCbDqyv/rKx+X/Vaqd97KFBq/t3vRXz0wka6LH1Ieq70QH0/X3qAFGkhAQINLWxUqtR4At0b/X4PGKMrWnWgoTEjGgRuJNAgyH4BbLF0PKj3b+mGLa5k1qp574puemysB/WsNj3fN6QrSneVflK6k3R3aRFyjYx6WS8ZZVxDV8MbK/x84jd4L5O+SNor74i/uI0McqOPcrgwirZdVlvvxRo9OmR/7btBOto37UKSEQjvpz9EaT2As1eyPKTKl0Dnp7J3nvRgnSvWytc21rIT6DyuvH5uXVD6c7UNgbjsMHPK2XlWho6UqmOjc4m2L5feKb1AertU9yEdH83QK95uHqxGIAABCAwFgb+qlq4Vid/IhLmH703ngN2tfLrYli2mh4zg70Fll5y1PK3LsF13jQZfryGrnebksx+qjZ6WciM2W6P5ytjht+s9YxPJETroD+zzTJQoPrZDnPaz8XcPGF4sfUy6inQ82UMHvIzeYcD+kP6rURm8g8DTfSnev5C290qvlW4qXVN6lNTTbCZ1WVr6qNRHLmwuXVG6v9TXVfCAhPvoMlZ5H9d+t7WUJ4hlSW1938e0eZvWj9Tneb1+d0k9sLKG1B+Kn5SeJkUGIuBv9jCNwLE7pPMOZIrMGQnYymKv37P9Wdr9f8loi2z5ErD3qU10DrKj87WLtXwI2M/UNgpmj/X2FlPgwZ6Xdq9V+RSZv5W/yqQrkpIAN50pgZF8QgLr6OjWE6YY/+Ax4x8a2iNpb2YY0ZDwp9IJry2biVd/V9GT0Nw7zVy0L6GFxiW7Qh7vLfWh7Rl7rxtX5yQO66EhvLrxLdr+MEmGPml8eoIPdXfG3UCAP5B7IOOf0pOkm0tnSPOSJ2TotdITpJfERp/R9rM93+/X502kXseLpC73Sf37R6TuY1ZRIGRRBTkvVxnrebDB632kVPuin0o/I0UGItDR78VXb49+IfWhxlwvB+KZJXPnf2oD/5/6qvQA6XFZrJCnCAIdtUV4naKCov4mis7ZRZSCzSwEfF2GyEfObap28REoPeJvWIt+I9U1JIx+6DnGRwhAAAJzEvCbdb9hzaJzWqtuT8WRy/BKIDG0/dIhsP8oz2/T5ckrtT2lsv0BqxFyq7pf9YrLGd1RDU9H0Qsa4XhmJ209tY//pvwGGZmNgP1YXPRgbgvMtruZXxaR22tKp0zgvqdZbYLjGQ75A1j4fb1CmZ3jizIYIUtfAqZrkz0k9TZESidgc4v95VI9MNlKpRdPgRMQsIXUJhqdaI9IV50gIYdKJTAyQnfUaEDbUu30oPQC6bzStaUKpJpG1dktUg9O1Ekqfi6oEwp8gUC1BPZV8d6zPlW6h9Tn7CZRJauNVHxC8Vf9hJv2/dMRseuU7/fp8uSVOgzpPTMva2XYUZDh9x5omBZN+d9/J34wK8OdgssIN8hP6vfx44ILaqB5e0v8/6YpAEg2AraMGGpuLr+vbPyS5hqZ6vPFpDlIlzcB08jN8Fs/L2/L2BuUQHg1uHd6XCPVwytSPQGfrhmCBzpn2VKxatRbmC5xsrYLSw+W6pnBfifVddg+Lr1DmvIeuNDaVvxcUGjdMA6BxhHQjXsY1XBahZ57RHvHjKpoanR9db7bGsKXoffZrla+P1bjt/1DZV9YTdnZSr07iubXWg1vUtfUktksNC2XXaw2Um8BMjsBm09c/OZUNz1IdgLhvek+MmRI/p+ykxosp49aC+utLD+YHXJnJzAygmev7DbIWQwBe5f+P/z+6YfF2MdqegJhRKXfn06XTpPq9svUARmCEGdp69eNUSN4bWftUx9QbYRAQ22aAkcgMJOA37TrZB+9viIg58Tluw9ZVCfEqsRWl8t+oXxfOg/CsNq/pMuTV+rwEDt6JeG8jGMnFwJ2lH5TM6SL5WKuVUbCkE0fcjtqeGerKllwZezV4ufnrYMKLmjIzdu6Yvy89PghB1Fh9f08EXrN/ZxBwKfClhi7aPu22sXPRfuMfZy95RMIoypfqzZ5kdT/f7Q8VghO+zSkjef0JwSMbptzf2V7CDRUhp6CITA2gSW0W3MZK1swaWmVvWlGVe9mWC1dmyokc6BBC8HZVVV4rHJ/Ib29mrIpNRmBkSkCb0qWfphSmaZ5hRvTrYap1vnX1c8/5ovmaS47UhwBO0mMn5Nqmh1SDQF7mfh7D60WskPqRcAmq118LQ3vKVc7IfUjYDuobXx0gwLUoyWs2fAvHTt29JEKvxNoqBA+RUOgbQSeUIVcK5IQ8fVovK8unkLsD8pzbYoMOSa1H6hsrQGA1JeAKfgWHqaPqK+PVXnmozzCQ8NxVXnQjnJNQ8nDb+yt7ahPXWsRXrXoc5obtS5OXWlm98t0Lg2/912z2yBnMQR8sc6w2OAt2ur8jtSLgE/zta/N6VOYyni+jt0nXX7O45XtIdBQGXoKhkD7CFQdaFhNJ1gPNLw/HVo7R3luTJcnr9T+/urgs3oSkPoSsJvUTlrlGZmTQAjU+dxRDelEshEIPVHxSuLZLJArKQH7anzOfVXSHKTLm0DoOb9e7eC/eQVykXoRsDeoXXya0dlSzuu1apywePmoAJ0tq3byAMRDUk0Rc7Hd4n13auudaVWN4iLQMLNBUv+dK3UOMkAAAkUTUJAhSNoL4zTlqupB/+HY58XjLZt6ErhMbm2oizVD2+dsnzO0aznpxnMeYk8yAh31skffl26h39hLkuUhVUYCX1A+zW+OtPYKUg2Bjl9z95YuKv1WNT5Q6vgEOufr2GHSN0sPHj8dRyog8BuVqQUgw0KRvmaDFn+M/iH1xYR1/Yg8ePdrbTVaNvL1v/aVekfaldq/grYIBCAAgUQE/MTiWiepekTDqjqR+oiGD6SDYnrLh92VLk9eqX3F4ODzWnlZxE4RBOyAuJ3WL8J6s22G3hTv/VJP8ZiyovaeJZ13zKOz7/Q0e0m/FG81jHdMWUR7d5F+UfpR6eh0m2nf7hPomjrWFS/zXVJ/AH2HdGFpUllPCT8mPUjqn8eTfv5qZNMr9xRDn7t+/HhG2J8XAft0/P/Muit5Ic1kx74ct8OOmbKTqUACYdFBH+3p5/YtCywI06kI2IJqjx9KZ0iflj4n1XXQpkh9VO/dUgUWbNS9ip2hfX6NK1v+qgJdEQhAoGEEvPe+24NfF9erDjS8UEg80PDBdED89Xw+hLMKMT3YBJ9fU0XplJmUQOg98N/W+5LmGK509iexuW2cOutmNTpknGO9uxfQl5ukfl5T70vodfYFZreR9spL9MXT+SiAv0j1cB7phivaQNqV0/XB7Yyn3WDkykqjIdzBhtt6VPqs1HuG+okHF3STF3mQ8iqpl3WedPSolyT+emBEN4h3/1Jm/OZxcX1HCiNg84vxPdJ/SucqrBgM9yEQ5pX/R21wr3SJPok5XDoBPw/5ed3ul65QevEUOAGB8BYdBRNsoZmJvH1CW+mh3jywPUpMwX4P7JUuBBpKR06BEMiHwLoy41onqTrQsIpOpLrZtw+lgxKiw4+ny5NXatMDTfB5+7wsYqcIAuEVU/qN+GulkDkJ+P9c+B2/ctSxt+v7Q9L4ZmjU0dm/fkZfp0n9wdxFD4PRRVIPNqi3ZkT8oV7zTqOl4z0v0Pbf0l/F333jDy0+kqJXV9F3T+e6sNTFAwMeLNjYv0jcz+9K3Wf1Do0rnl49SmGkRte3jfTd831d2itJ/PX056vIM2OOn+o1wOciCNi+Met9irCOzaQEbBO1g/ea/zRpDtKVScBeobaZKr1c2j3XlekAZSUiYD9T+1wj7V7benLZhtrv/2Me0C5bCDSUTZzyINBiAlUHGtQ7GR52PpyOsX1H+XQhrUJ8eFvwec8qSh+kzKnRlNWfjSbvqq4o74keAjE9CNqtQ1DRDFUMvSk+lPPoUZn/pu/+4N5PfBTAedLjRiV8t76btHuDtFX8ffSIAw/U6f94wt/iQTruoxXWlrosJXXbatfZRD1DIYjw7dn2zv7Fj3leDzj0yvf1xQMQ3SBIGn93Vr5nFEPxm0VfwGtSr2E+500gBA+vE2cfajwk57C8GeZlz1fRD9fBHfOyiJ08Cdjecft8L0+r2MqLgL9lIkyhePWcFu2lOuYjUn4x57FS9hBoKAUzhUAgPYF5lEUnj2h16apS76EbPSRXu2olVQcaVoovhh9JR8W+oXz+cFCBhFdJ6YHFPlpB4ZmLVNf+ktOjyU9Mj6ZoUvlkDckbBrFD4t+X/18icxCwy8Tn5p7dftPjD+OjgwI9Sfp+/KVS9D6464HEpxiMyCL6lOS8uI7SeTDx8JGcM4ME7t/+Pfu6H/UAGhbS6n4fvT1fO+6Rjl541kdwuM23Sl3S+Lug0j8dRW88Lf6dvTNY4E+BBEzTcsIDrv63keoIjEyhuE/tsWR1flDy+ARMQYbwv8IIoPEhVXTE1orbxp8besTX1ghvdrlA23l7DpT5kUBDmbQpCwJ9CHivyn7SK6XTpH7D2qs+hPgSqd/M1lGaGmj4sjCLs406SZeBOCzs42UfUUZpeZUxLZpnvZlBhhBoeDIvu/W2Y6+PfyfvqLefVXnnwbLwf7RB7IGPLHpWmiQQ0Ou0/x/uJj1F6tMa/k/aFT2Eh3PgZtpeK/UghJ8XfTTD/NLx5M868IC092ZrNX338+vx0l5ZXF983YfegEbvcf/8E6mGokY++qFXjtEXt/neeGdaf/+g2MWvZeIuqYYqI8UTsN+Ltc5htlzxZVHC+ARsY7WBD+/++fhpOFIdgfBKUj00hmkUG1bnByXPScBHv9kN0i9Kl4r1S9r6/9PJUn+2qEoINFRFnnIhMIrAivr+P6nfpGr4bHSN9ALp6VIf8nSx9J9SvwH2NL5vWWmdpOpAgxiGB52PpoPiD/khX0UnY9PDmE/faI48pDnuswINU0xPekNwk+6LLoXhiV9vTkuV6amtHP8f6WYniE8juDn+nGazphJ78MrPc3dKXyPtymX6cLv0YekJ0p2lvjbD89JTpWPJutrptnTjNZvMpW83SR+VqkcoiAdFPif19I+HPWP/2SNO801tp8RJVtbWffO83XNQWn9/oLxiZgdLZccfvpBiCYSFXvX7MWePVErAvhL/7uvamVIpneoLtxeofe6V6rxsy1TvDx7MIhDOY1erXfSMYNOkd0u3n3W8sk8EGipDT8EQmJ3A9fqqYbPhnbcLz35otm+KVoZXqfmN9e9mO1L9l6oDDSvoxOo35x9Lh8L0YBHyLZYuX16pw+rnjevFUaDhvlnBhkmb5UWj3nbCYktX1tvHKr0LC4b9J/bgL9pePMqbVfXdb356delRaTwA4PJiqY8cmCHdReriaz74g/wn/EuPeJDB96/Ts6/78Vh98PPlyt0dPdvX6bOfdz3vxVIPFNwqPU+qG7VxxadMnCP1fPdLz5c+J/WAoe97r9Qlrb+HKI98PdnPZfLLX72LFE/AfiTWHmzQfGakOgI+vDv0zOp/yntmkfoRsE3VNv4g+0fppPr5N8wehXVnXqt2eZG0ghG6Y7In0DAmFnZCoFwCq6s4vzndN0WxR8R56jSqoepAg0fbxdE+noKjYzwozldRhN4UZPLhu82SadHkS7uBBk2l2LtZ3mf11r6ltvIeg/mzWmh3PlMAIPwPrqd63is9Y1R9ff0UP9f16htHpen9uqi+eCDARy24dEcvLD/z68jfHfXJbb5rZM/MD/Np84i0m3/U4fB1Tf39RpxG54IwOudsba+TTiQeENlf6sGA46XbSV8udT+6PbJp/T0wzq8RFmFOtIIXvo4LUiyB0FP7lFh7gAmplIBtpHbw3/2ZlbpB4RMQsPerfXSeMx/RhUBgIgIEGiaiM8Gxbo/LBEk4BIHEBNaNU16UOMesnkI9XCMDElB0PsjkAe1kzf6wMi6eNXOF+f7bLduijgfLhkEuVSW9F2fDYahshjp2Awv+oO0P+KuMsuEP5T76pVev0Hf//ftD+wbSXvFpDTdIPXDhcufMTViXIf4YNt3/4cd6d+rztlIPVnx/1P7erzfqy4ekb5EeKb1X6ufkq6QTiQcUvivdSeojGDw4sb7UpZs3rb+rhNwz2Xnww6dyfCDex6YwAp27ZPoYqYJe9obCisFwAgIdfzD5snRHtcXowGGC/CQpnkBHAffoRKnOTbZH8eVRAgQgAAEIDEJgY2X2m9bdUhj5vNL6cOCFUuQpOmnVIxqWF0aPsqtXNY2MROdXS5Mrv7SmXs/mvTZRb5v4THdEgz6fnh+POlsaeUvIp+vsZbW+2VX6Pf9bPvxB6ueEJLKIEqkXM9Jw3NnE107wc6OPDHDZQurfd/cvPfJjfZ4hHR2w8/Okp19aOpZcrp1njTqwjb57nq1H7e/9up2+TJd2Awt+rCP16SKuXUnrrwcrPGgSd2aYpsfZI9IFugbZFkUgLMyrIJP9U0pnUlGYE9kNCw96OygI76/uQ+pHIExz0dQwe0b68vr5h0c1IeCBQ1cEAhCokID3WvmN+ePS/aTeAzeeLKkD/yf1IMO54yWqaH/VgQYtSBgCDc4nhZh6I0O+tVNkyjFpmB88uic2R/vFmHo2mrxzN9CgaRR/L6YeyWDTAABAAElEQVSUOlq1O/R7OaeOntXDp+5UpDWOlT/+wL5CQr+8l8zTHyn1/0XvKfuHdKq0O+pLH0Mw4kltPdiwhvRL0qelXt5o8REWD4ze2fPdg7seoDhQuor07dKHpMdJe+WX+uI9eF3xB1EfCXGJ1APFL5WeJPVz+OiApQdPkvr7X6W9QBqLbR2fmzRUGSmewMi1YJ/iy6KEiQnYy/TbV9DN6nafM7HbQ3U0BN7vVxv9T7rUUFWdyiYlQKAhKSnSQaBgAj703G9a/Ubbb6wVzY8uknpP8ZnSC6V+0+29aJ7mX9IlpXWSqgMNywqN2Ngn00Gxd8f5ensn05kYKPXIStvzDGSm5Mxal2GDbqBBIxr8QWpIxE7V7+VRqT9sInMQCAtR6f/wLJ9W4OeqbedIMvaOydp9jNR79D2f63XSTaS9spC+nCLtplOPfwgMjNUefp68SDqeeJDXAwQebPDyfKrDt6XzSXvlPn3xYHCvbKEvCjqFfH5e9nO0j4YYLUn99XQ+quPDswxYR+ZVB1MAgt/bLC5FfQqLqek3FxboXbCoUrCblIB9Vm2h/0vbP2kO0pVNwF6n9tH5zy6W+rRCBAK9BAg09NLgMwQqJqCbyugA6eXSZ6V+49tVH8Fwr/Qi6Y7SsW6qtbtSqTrQsIxw+U3JQeko2C5xvlely5dXavtUXL78b46om3ihWYGGKb5E/gua4/0gno5MtentZR/EYAvzjkyfOF+V+23KCs6r9C+RLt0n3xQdf7HUgwWDyqIy4Lb8HJxGvOzVpIslyNTP34/Lhv6tRo9os33j88MOCcogycAEfI2GcB35wsCmMDAggRD40YOKKZDtAUykngTsg/H/zLfq6R9eVUiAQEOF8CkaAhMR8N5tv9H2i6sPPW5CpLjqQIN4hRtEPbinEVPgJuTbNE2u/NKODNetaOpG9poo0HDPrGDDpM2zW2pSzvC+ag9oHdgkr8v11UcV+f/UQf5w7L39BGUmbgAfzXGXdP85k4W50BpRYX+e8xh7iiFgCo6FuecrF2Mfq8kJ2Opqi6ekf5HmEVRMXjQpUxAIU0D9uvieFJlI2n4CBBra38bUEAKlEahLoOHgdDW2N+vi6BfILdPlyyu1vT0uf/QQ8bwKKMzOtGjKJd1Ag6ZS7FtYQbUy7EPY7THpz2rlVq2csVXj3/QRcsunIpxcK/fq58w+cula6TgPUnZIzLOiUVf1A1asR7amePtwcP7HiwWd0LpppGe4Rn8mYQaSlU7ANGIrBIM0Gtc2Lr14CqwrAQINGVumjsPWM1aFbBBoDQEFCzKJz/V20YWyEnkoLnWJSkofqFCfOz5T9IrLNbqf273t+Hz+y6SNCwyV1y6dW1TW36Q7ST8nnSSdX4qMTUC9tmFBSp8iN5Ycp51TpR8b6yD78ibQuUEWvyP1aXWvzts69tIS6Hhb/E6qc4ltkDY36csg0FGAIUzrfVDbM9VOQzKVsgy2lDGMBAg0DGOrU+e6E+gGGtLOs/YLpIsPX65CHo4LbVygQaBv7ALT52W7n4dg+2fVUVOabOUhqGvWKp6mjD7seUVt9cAW3gyR1Vbb8x2kCl4yfiU7vnbDSVKf5rXK+Ok4kiOBQ2XLFxr9upinvabk6AamYgJ7a/uEVKOjfDoRUj8CnXvkk68h5mvW6E09tFP92giPmkKAQENTWgo/h4lA1kBDd0RDVYGG7oiGxZvWWM9F034un+/SDcXTM6IZpzTN/wH89UCDC6MaZnIY6+/p8U4f1YAMTuBrMuH3Hj1vpRjcKBbGI9DxAPDh0g2lu46Xiv1lEejcrZI0hSJaS3pkWaVSTloCncuVY3/pK6U/SJub9BCAAAQgAIHxCHhvg2tFYhoREOZxfjadA/byON/u6fLlldrmi8v/Ul4Wy7Sjbuu5NX+iqmknZVa1p6zQZhoJYz6kFxmXgOmm024a9zAHUhKws8VT51hbJGVGkmciYPOItUZt2R1Spv5kYph3JlNA22ZIt8jbMvbyJGDHqI3U+ZP2deN5+oCtGhBgjYYaNAIuQKAtBHQDXGmgQSMCMgUa9Cq9kM8XZKtIwgrnx1dUOMVmImBap8Guy5R1aDLZx+L/rQ2GpsqFVtQ2j3l+otBiMN5DYGSx4EN7dvKxMgKmV9GGwM/t2hJwq6wd+hUcXk16ntroeem2/VJzvLUECDS0tmmpGATKJ1B1oEHzAkPAwBefSyG2RpzvgBSZck5qd8qHM3I2irlCCdjRajPvWfP5qMiYBGylmNFRYx5mZwYCdpWY+vlCve1IOQTsd+L9tFS/Z6R6AvZ6tYWfe39SvS94MD6BEBTyEUH+lqa1xk/HkRYTINCQsXF9niQCAQjUi8CgazRUOfzf12lYol448aYPAV+nwReJY52GcUF11OsY+Y0G6zSMyyj1gS8rh6/o/q7UOcmQlYCvi+GBna9kNUC+PAl0/iBrx0p30wPsznlaxlaeBDqPytr2Ur83O1sb7nHyxIstCEAAAkNG4AnV17Ui8WGUYUTDoekcsOXifP+XLl+eqe1C+fDPPC1iq2gC5lN1vFdNIxuQ8QnYB+P/r43GT8OR5ATCkORbxPR6KW9DSA5uwJSmxTjD9WXzAQ2RPRcC/kYDn7pmj0hXysUkRgoiYG9UGz0nvVjKSKyCKNfULCMaatowuAWBJhJoaqBBUfZwA5lyEck8m8i0Qr/dladFbJVBwIND5hdSZFwCIZDn83S/Pm4SDqQkYO+Lz1neW4iUQiAEsu8Td/+fn7uUIimkDwFbV20xVfpHKSON+9Cq9rB9QG3ki0N+v1o/KL1kAgQaSgZOcRBoM4GqAw0Lxxeyw9JBtgXjfJ9Ply/P1PZd+aAbJqRZBOzbarfp0gWa5XfZ3oYRO3o9HQ8D+ZAPbz25XzwvzcceVpIRsL3F3B+W9NCE1IOAaVpLaJNP18MfvBifQLjP8f+fj4yfhiMtI0CgoWUNSnUgUCWBqgMNC8U3HIeng2CT43wVDoG3L8Q+8MCarvEqTu3zg8NNrhYnQ8YnYO+NOW0+fhqOpCNgn4mZbpouH6mzE/CpKnaF9GHpktntkDM/AqFNzlN7eMB3w/zsYil/AjZJbXSB1KdRbJO/fSzWkACBhoyNwhCtjODIBoECCfiCQy4p5y13pimP51XAoTLxxSBduHmdyaEpfy+JHeVhb+IWO1OHdXMZ7TJxMo6mIKDRNNGT0k+myEPSgQh0/DrhoxkWlSo4jFRPILTJnvJDazVEp+hSrhGKSD0JdPwa8Hap1piJTlVbrV1PP/EKAhCAAATqSKDqEQ0aDRB6l49ID8eeVd7vpM+XVw7bI/Z9g7wslmlHUZKFp0XzHPNsNPkIrVBXZcCmzGrHZdlNaruLKii4DkUurKjexXLkV9J5J3bIzhGnB6XzTJyOo8kJ2DHiOUP6kuR5SDk4ATtBzH3dkUaerwevfx0t2JvVHgoEedsg9SZgL1Y7+SKeN0vpXKl3Yw3qHSMaBiVIfghAYIRAkwMNj+uC98ORmpT+wbZV+X6TtFXpRedQoAIMh0+Ppmjs6hRTwOH9OZhskAn7kdrtaemQBVgiDzL8RQ2lB90wIkjDlycKNthuSua/cf3WkXwI2AriqRFZdlI+9rCSjIAtI+aPSS+VphxBl6wEUmUhENbM8XPMO7LkJk+ZBPxeJ0x3+aO2w3btLBN01WURaKi6BSgfAi0iUHWgYX5dsPwm4wvpmYae1pPT58srh20U+97IoeUKNBw1K9Aw+YS8qDTDju0Zt93GzfA3Fy9HggzHR5PsyGhu/d/1CzaERVefUrKf5uIBRmICoXfd56evBJIyCdhH4//73csslbImIhAWSdWgurCGxooTpeRYHQjYgfH/0JDdM9SBfWk+EGgoDTUFQaD9BKoONMwXX7S+mB61v1rSTkufL68ctnrsuy68zRONYtinJ9BwafNqMIjHtmrcdgcNYqVBeWcLMnTbPWGw4Wdi5cGGBRpU35q7amuJp0+f+EbNHW2Zez4FyP4tvVu6UMsq1+Dq2Hpqj6nSi6Wsp1b7lrRj1U7eQTQs18/at0jODhJoyBko5iAwzASqDjTMG1+wvpS+EewW5fV55hWJLR77/rmKHBio2OnRpE27D5zaPjCQsUZmDoEqrUHQehkzyNBt+/7BhpF51JpGgeRHwM9dIYDDfOf8oCawZG+Iz9tfTpCYJKUR8NcnhodXXnlZGvOsBdncaqtzpR4s9YUikXYRINDQrvakNhColEDVgYYp8c3FkekphJ6p89LnyytHeEXX8/L/m3lZLNOOGn7p7sOmb7XgxRJlll99WT4dIMzZ1k1Ta2XCIEO3/ScONoReYAWirML/tTa2z8jUq8+3sXb1rpOdqd+zr5OhkSVIPQiE66k/vPqUolfXwye8GJ+ALax2+qfU1zp61fjpONJAAgQaGthouAyBuhKoOtAwWRcpH4J3VHpA9g/luzB9vjxzmD+A6YG1mTI9mvxw92FTIxyGab0CNZjtJ/Xf3iua2Xp9vU4UZOi2f59gw7fE6Tnpsn1LJUEKAuH99L6Su27akfII+NoYYTTJBeWVSUn9CdjSapd7pbdKF+mfnhTVEjCtqRGmId2n7Qur9YXScyRAoCEjTOZ9ZQRHNggUSEAPekGyrAKu11tGGhFRqdyv0peq1IMBChf8G7vZLeq8uPt5SLZaOTvIa1ta35+qfTf6TjSps1fUf9DGx6JJ0ednpnujeHx3FBNfdNWN7DJqP18HI/BFZV9UesBgZsidjkDndqV39lvoAWnndHlJXRyBjl9P95CuLD2+uHKwnA+Bzh2ys510Qelv9b/k5zIEAkNLgEDD0DY9FW8pgToEGnxtA/XCNFZ6Aw1rNrYWmRzv3KBs6omJ2hpo0IifKLpEb7J8Prxcwr+NL9OU5rJohmITQULeWak73sNxk3T3Wfv4NDiBjveoXyH1+enzDW4PCykIfEVp/ys9Ruz9QQmpBYHO+XLD22Yntct7auESTkxAoHOVDr5T6h0VPiVJC64iEIAABCAAgZkEqp46MUkXJj3cWIaFucKwY80RrFL8rRc+dLCZoqkTB3WHzuvzWc2sxSBeh/bzoettDUTrISqyd0ZzaUn3yZr8PGVMfUrHtok6WtgrRCQOHpuoHarD/r+61tjH2ZuNgL0l5vr+bPnJlZ2AbR2zz3D9yV4qOfsRCOvCKAAXpres3S81x+tAwD4c/y/9qA7e4MNABJg6MRA+MkMAAr0Eqg40aDh2eHjxHoyUYuco70iPfMrMOSW3b8sHX7wqy9SPnHzIbubZaPIO3YfPadHkf2e31NScI+8EX7+pNUjg94TBhmRBBi/FXiT1QMMXEpRJksQEwiJ414rr/6T0BibmlldCO0Pc/Rz+krwsYicPAuEVxI+pXa6TMtonD6SF2zBfy8evEZ8uvCgKKJIAgYYi6WIbAkNGoOpAg3qSw4XJH4ZSiqkH3m5LmSnn5HZI7P9iORsuxZwCDWt3Aw0a0TDtokgT9YdKbJ24/TR0vdUyZrAheZChy8b+MvN/rpmBtW4t6re1XePf4Z71863tHtkKYq/roF3S9po2r37m0yf8wZX1GhrReOG1l79Re2l0nLGeTyPabEwnCTSMiYWdEIBAFgJVBxo0EiDcSHw1vfP2c+WteNqCHRD738iFFK+PoskKMDzXDTZMjaaskb4dmp7D7lMb/rrptUjg/2zBhvRBBi9hZATIpgnKI0liAuEGXWtgmNYNae00nsQ0yk9oHxd3f6B9d/llU+LEBOx7cduwaOfEoGpy1Nc7sWukU6WvqYlTuJGOAIGGdLxIDQEITECg4kCDexZu8L42gY/jHLKTlPehcQ6WtNveHvu/SUkF5l7MtGjKf3sCDdvlXkDtDdqpasNHpf1fzVD7uvR1cCTY0H9NhrFs2ZLiNE2qm38kXwL2XnH1h12dU5ByCZivFXSt9H5pI0enlcurzNJ82kRoG59GsWqZJVNWVgL2ArXVnVItlu1T7pCGESDQ0LAGw10I1JlAkwMNP9BF7Mlq4dpm8sEfDnas1o/spWtEw5ndQIOmUgzhQ47tF7fhK7NTbFTOEGyQx/rdRuMs/DhRfexsZX1YOmWiVBxLS8B52l3Sq9PmJH0eBEyjdMK5/Lt5WMNGngRMC0KGhSGv1HZynpaxVRQB07pHYUrSf7VVgBppEAECDQ1qLFyFQN0J1CHQ4PP5vpEeVFiIUb2rVYqtKd890LB/lV4MUraCC2sp0HCeRjYcrzuCIXx4tNXjNvzkIBwblvcg+XtgNp9H5k0PYVAqG7HkuUZWbh/CkUXJKRWX0k7QucCvR68urgwsZyNge8fn6W9my0+u8gmEt7pMV7vpwdXmL798SsxIgEBDRnBkgwAE5iTQ5ECD1nUID/kVvvHBFo99+NycaNnTHAJ2u9rx/Ob4W6WnNq9Y+StBh2Fdi5JBh2Hi94qtem6R8gmEqUEPiv8/pEO2MG75tNOXaCeqXTyw/7b0eclRDQHbK26zX2k7DNMTq8Gcb6kEGvLliTUIDDWBOgQantcFKEMvhX0pvoDpwacqCa+m84j9t6rygHLzIGA/Vhs+LWVYbiKcYYE2/90vlSg5iVIQsI+Jqz9MvSlFJpLmRmDkwegTuZnEUE4EbAH9X/xL6mvqsF5DTlSLN2PqiAnntO8UXxYl5ECAQEMOEDEBAQjMJFCHQMNzuggdm75B7ND44rVI+rx55vA3X9hpeVrEVtkEbPf4t/TasktuZnm+mni4cfxQM/2vs9c+xDgsSnh5nb1sr28hePxHtcFT0pXbW8+m1iy8ktjb5m/SIZzq19h2+77aywOoGdYFamqdG+s3gYbGNh2OQ6B+BOoQaPCe0QwjAuxT8YVr6Wqx2t/lh25MkeYSCKtk+03QYc2tQ9me23/F66qySx2O8uz/4nPb1sNR37rV0tYS/2elv6mbZ/jjBGzP+P8jw30DBKshEF7hq/+nEGzYoxofKDUhAQINCUGRDAIQ6E+gDoEGLejoCzumlZEhxiumzZlvep/bb//O1ybWyidgN6gd/1x+uU0tcWQ47DpNrUF9/Q5DxB/Q7/Gy+vrYds/s8+Lvwce3tb2mzaxfWLjT2+cdzfR/GL0O57Ur1GZ+z7fVMBJoSJ0JNDSkoXATAk0gUIdAg/ccHZcelr1f+fxGY7X0efPMYSfLh4fytIitKgj4bzDcAC1YRenNK9NeKF6+Qv/RzfO9CR6b3gwSzm/ckFfSXGHRUx+1c6d0oUpcoNAJCIQpRtepbR6TVnwPMIGbHBpFwDQC1W6WPi7VKzCRGhIg0FDDRsElCDSVQJMDDfvqQuWBhrWrhW/+9gt/4GKV8mobYsDSvecy/J62GdDQEGX3KUN2l5TVxHNvdX+49QAmo2xyR5vYoPe6hnNChtcvJy6EhJkJhCkuT6qNrpbOm9kMGUsmEF4p7SO2fH2rlUsunOL6EyDQ0J/RmCnmGnMvOyEAgaoJKFgQdTI4oZEQQapeEOp+eeH+LzXTHf42lMDF8tt/i1s21P8q3D5JhS4vfX0Vhbe7zI4HgRXEjF6jn+UW7a5rXWvX+b08+6nUR8+9oq5eDq9fHZ+yeID0ZVKCQY35IXQ0UijaTrqo9Fz9by3WGNdxFAIQgAAEUhGow4iGZ3Sh+W4qr0Ni21n5fETDRunz5pnD9o79WC9Pq9iqgkDoGVPvGJKMgC2s3/7T0lOSpSdVOgKB78Piq5EjSDUEbBnxf0R6lZSRO9U0Qp9S7Xi1jd8L6O1BSHMI2PZqM3/r2CVSRqTUp+EY0VCftsATCDSeQB0CDf6g8r30JO2tyuc3F69NnzfPHLZt7Mcb8rRatq1noklbTo+m3DEtmnKJxmvrAXIYxb6stvRpMEsOY+2z1dnU4xuCDUP6m8lGLXku+2x8fmGkTXJoOae0/eI2+GjOhjGXC4GwnsbVaiN/7eW6uZjESEkEbP/4f+uX2hLIK4l6n2IINPQBxGEIQCA5gToEGvzmQD0SacU0lz4EGip+wPchtcGP3dLWoE7pp0eTf6VAg7lOi+bZq06+leeL6bcU2lKjZZBkBEbmsWvNFCR/AmFUA2s15A82hUXT1LjQ6+rXqhemyEjS0giExWkfVvv4Ap6LlFYsBeVAwA5Vm3mn0fdzMIaJwQkQaMjIcK6M+cgGAQgUS0AXmMav0eCENMS2uWJRx9ea6Mqw9gr9SQB87Q/WHOj+EvpvL1CSO6RDGpzqD2iwFB2tzh59Rfoa3Yi/cTBb5M5GoOPXKA+keY9rhqB4tlLJlYZA51al9mD/atIT9b+i4BDSDAKdQ+WnT599j9rtC83wGS8hAAEIQCAJgTqMaPBVo3+QxNnZ05jfeHsU/O2z7y/7Wxi26X4cVXbJeZanKRMf7o5o0Pb3edpuli27UG3pN61IYgJ+cxj+F1+cOAsJUxAwvXLVfJV272lCKiNgB8e/8z0rc4GC+xCww+I20uthkeYQMHUG2+lx232oOX630lNGNLSyWakUBKohUIdAg3zIFGjoTll4VzXoeksN7/L+ce+epn2eGk3aqifQcHfT/M/P35GHCe8ZQxIRsBfpf9jXtmh0sC1RVStLZJ+Ib8LfVJkLQ1+wzaM2+IfUp7I0egRbe5syPLCep/bxRQa3aG8921gz0xvETCPkwrVk1zbWsCF1ItDQkIbCTQg0gUAdAg0aGmw/TA/LXqp8PpJg7/R5885h/5Ef5+ZttUx7mny8XE+gwR6NoiF95ZS9Mv5d7V8m/+aXZReL273SSc2vSx1rYPOL7X3Sv9XRu+HxKazJ4w+xpw1PnZtWU1tC7XObVNMBbYWmeT/c/tpCarOrpNOkTBWr5sdAoCEjd9ZoyAiObBAomICCBY1eo8Hx6AEgWto/NFUWiKJ7dGHXa9xmygLRpHW6n4dsq5ucyDmwTkO6hvdgoffybpsuG6mTEejo7TzRkdKX6/90+2R5SJU/gY4Her4ufQftkD/dfCx2HpIdn1K5sNSH42skCtIMAh3v/NpG+j/pmWq7VzXDb7yEAAQgAIGxCNRhRIM6z+2EsZybeJ+trHw+ouHDE6cr46j5BfHOMkoqsgyt0/Cn7qgGvXliiHv0Q3vqZtWH4SLJCNh84uX/y79Olp5U6QkExprWZNdIWewuPcCccoTRJTerDXTO97eCIPUkYO9V+/g9wrfq6R9ejU8gvEXEz3UPStccPx1HCiDAiIaMULlhzAiObBAomIBuBBjRUDDjpOavn5VwrmEd0eAI/iBdXLqBf0GSEOg8o1Q/k2oNAVsuSQ7SpCUQGH9JudaX7pA2N+nzIhBGl+wnay+QHp2XVezkTaBzvCz+WHqgzkk1WMsp7/q12V54i8jWqqFPxTtf7ccUmDY3N3WDAARaS6AOIxoe1kXkxPSETWsIhN6Kz6XPm3cOOyT2pdHrGmgUw/u7Ixq01dsXhlXC4obeE/apYSWQrd5h/rpzY8X3bAAT5AoLpnlP+j+ljGpIQKy4JPYjtYEvgrpZcWVgeTACYRSQjwDSMkT2ksFskbt8Av6/ZQpimzpBzIP/SPEEGNFQPGNKgMDQEKhDoMGHqJ+UnngYvuoPNXq1XtVi+8sP92XNqj0ZpPxnokmv6wk0aCGtYZawmNiFw0wgW93t7/o/+K+Uh+BsABPksgPi880uCRKTpDACIdh9r9rCFwPW1CGkngRsVbWP1t0J56VF6+kjXo1PwN6itvMFWPUAbAuMn44jOREQ58gVgQAEIDAwgToEGnwO3k/S18TmVj5/uP9K+rx55zANYw6+bJ635TLt6cewVE+gwfS90QtcDsbONOzWnpXOP5idYctt74//F143bDUvr77hNYu3iLM/4PrQYqQyArZj/HuvwXWoMggNKNh8Stfz0nOkTKVuQIvN7qLtqXabIf29dMrsx/iWMwECDTkDxRwEhplAHQIND+jCcXK2RghR7mOz5c0zl22sOnjQY+c8rVZhS4GGe7vBBo1w2LIKH+pRpmnV8tCmukFFkhMw9Ria3pBgP02eh5TpCdju8e9TC94h1RLwV12Gh1hWyK+2IfqUbgfH/zM1GAXZx1UOj0HAPhK33y+1VUcTUhABAg0FgcUsBIaRQB0CDffronFKNvhh3uX3s+XNM5etFl8AP5in1SpsKcjwh26gwddsqMKHepQZhkX7cM2v18OfJnnhU6FsqnSJJnndLF+9V9auk/p6DfM2y/e2eWtLqQ08YO7zyOltrXXz2hlqI+8UeFut3cS5cQjYoXH7+TWG6XnjUBpwN4GGjAAZKpURHNkgUAKBrBcMPcxEdbixuzdmtEwJrAouwi7oFmBRRw8xwyodzemN/iZ9w7ASGKDeHvzz/0v1uiPFEOhoGHH0GekLpFpVH6mOQEdBhsiDzGtLP1edH5ScgMCeSqOAkL+NgsUhxaFh0jlUDn9D6teWGoxmlRcIBCAAAQiMS6AOIxru0wU/4zBru1t5fz5u7Uo9YE/Klx+UWmQBhWkM8NwaybD71GjK9gWYb5hJO1xt6r1fKzTM8Rq4a/8WN7+hRwolEBZIe1CsFy60GIwnIGC/UjtMl74sQWKSVEYgvFXIF4e8SbpYZW5QcEYCPpIhvPHFr81Mg8lIcYJsjGiYAA6HIACBdATqEGjQaAD7WTq3u6ntVuU9q/ut2q3dLF9+U60PlJ4vAdtEbeo3M3vna3cYrNnHYnZavwQpjoBtEXM+rLgysJyMgC2ntvAH2H9I50mWh1TVELCt1UbPS8+TMuK5mkYYoNSwGPjpaju/Pn9iAENknZMAgYY5mbAHAhDISKAOgYZ7dKE4NZv/doPynpstb9657DL5cmXeVrFXJQFf0d8ey/77rNL3qsu2JcXN39pxQtWetL/8sBK7zuW+VgBSLQF7t9rBH340GgqpNwE7KG6rI+vtJ96NTcAmq/08UOT/b/uNnYa9GQgQaMgAjSwQgMDYBOoQaBhg+kPoObpw7KqVvdd8JeTbyy6V8oom4CNmzIem0+uVGrVPawoLtjKsPzW7NBnsleLsN9tfS5OLtEURsLPVFkyhKApvrnbDG0P8f2enXM1irCQC/vpp+5PUR6e8s6RC214MgYa2tzD1g0CJBOoQaLhLFwgtDZBF7HLl1UiCOoh9R76oBxdpFwF7n9rVb0Rf0a56lVEbe33M7oAyShvuMuwXYj1VuuJwc6hD7W15tcPDUp9CoV5XpL4EbAG10T+lvsbSS+vrJ56NT8AWUdtdLfXg3pvHT8eRhAQINCQERTIIQKA/gToEGvz1bJprl0XsEuW9KkvO/PPYofLFH0hZXCp/uBVaHHl16acrdKKhRYdFu27R/8Q1Da1Ag9y2tcXZe/V+1CCnW+yq7aG28OvBES2uZEuqZquqnTww5OeqJVpSqSGrRnjFrC9A/Ix0iyGrfN7VJdCQN1HsQWCICdQh0HCHLgxnZGuDMDf5umx5885l+6sefmO5Vt6WsVc1gbA6uYJaSHoCI/OgN0yflxzpCISV2J/TOWiddPlIXQyBMO3K24PffjGAc7RqW6mdvK0ulGptHqR5BOwFajsPFvnoFBYhzt6ABBqysyMnBCAwikAdAg2366Jw5ii/En4Nc2FvSpi44GT2VtXDAw2vK7ggzJdOwL6tdvVhmQuXXnTjC7SlxW2a9EeNr0rtK+CvYQ09er+uvatD4aAto/Z4QOqLFs83FFVudCXtw2onv4Z/q9HVGGrn7YVqP++8elT6iqFGkb3yBBqysyMnBCAwikAdAg3/0wXhF6P8SvjVR0L4RaUOYhvJF79J2aUO3uBDngRsu7htd8jT6vDYGlkUctHhqXNVNbWj4t/qplV5QLm9BOxtcXt8vXcvn+tKIIwK8uv4e+vqIX71I2AvVvvdK/XpMOv1S83xOQgQaJgDCTsgAIGsBOoQaLhNF4NfZquAnaK892fLm3euEEn3GxT1irRDno0mrzs9mvSadtRmkFrYgmpXf1Xj8YNYGd68PsonBOE+MLwMyqq5rxETbrBrskhuWfWuczl2stpkhnTzOnuJb07ApkgvlfoorM1h0lQCPn0sjCbS/aGvX4OkIECgIQUskkIAAhMTqEOg4VZdCM6a2M3xjtoPlffx8Y6Wu9+HxoaHqSPLLbeY0hRg2Hx6NPn56dEU3XHNwzuqI/uD2rcmo2eKafNirdqN4leT9VSKrWn11u0T8bmIETjVN4Y8CMEff7vSbdKFauESTkxAIEz38pGWD0q1UCTSTAK2vtrPRzXcI12jmXWoxGsCDRmxz5UxH9kgAIFiCWgUQNTJWIS/TlI9EHWQjlY7jjzosWwdvBnUB4vmXlfNEs6bnWiutw5qrwX5z1EdfA68uCAZCHxPebyXaZMMecmSjsCxSu5BsS+K99zpspI6fwKdR2RzH+nK0q/lbx+L+RLo+CjJ7aXzSs/W/9DC+drHWjkEOn9XOW+Uzi+9QO34wnLKpRQIQAACEOgSqMOIBl8l+Fddh9JtTTdtYRRB1kBFuuL6pg69tuf1TdaABFOjSW/00Qyxqjdu2MXWjH9rBw07iWz199fGhYUKNYwcKZ6A7Rn/XvctvixKSEbAFGwL16ttk6UnVbUETCOCwpSX32hLZ2W1jTFA6abpn+FNFD56dsUBDA1LVkY0DEtLU08IlECgDoGGm3Xyz7hKun1JeTUiwrznoQZif5Qv19TAkYFdeCqKlusJNPj8FD0oDruEV2ddMuwUstfffqL/j6lSfkvZISbM6Q9Gdq3Uh+x7jx5SOYGw1osH1u+WLl65OziQgIB9Wm3l9xhfTpCYJLUlENYJelrt+F/pcrV1sx6OEWioRzvgBQRaQaAOgYabdOI/OxtNO1R5/SZgkWz5885lp8kXzQdsh2iNhgdnBRsmbd6OWg1SC3/tWXjN5aKDWBnevKFnyf9fPz68DMqsub1ZrJ33wWWWSlkTEbBN1R7PS0+dKBXH6kTAfhr/H+1ZJ6/wJS0B0zSKEOj+l7Z67TIyDgECDeOAYTcEIJCeQB0CDR5h1tDELGKfim8A9L7yOoh9U/48J23FMEsFGS7sBhqmRVM+WAfC1fpgb4p/bztV60eTS7d/iKGPYmrF/0j9WyKMsnpMvLmxrk1j2THxeWTn2riEIxMQ8BGTdoX0WSlrzExAqv6Hwquq/Y0i/5QuUX9/K/GQQENG7NzUZARHNggUTEA9bpkXg9Qw7CBT4m3VGx/N4IuvLVW1I3mUb9EMXYxHhEUQo+gi0fBFP5ljPfKzSP3h28qxqnSb1DnJkIXAx5TJ33RwWJbM5CmEgIbjR+pVjb6jh50VCikBozkS6Ph9hi+I/ID0l2ozP38hjSTQ8dGz75SuJb1AbckUpka2I05DAAJNIVCHEQ036mR/TjZgdoDy+tDgNbLlzzuX7RX7s37elquwp9da7jVrRMPkK6vwoX5l+jQfu19K8DpT4/h6AfaI9LxM2cmUgUBYG8NHWq2dITNZCiEQXr3nPeT+sFOTxYwLqWiLjNrL1FZPShUkqst0zRbhLbUqtpPa0M+Jfi1fttSi618YIxrq30Z4CIHGEKhDoOEGnejPzUbM9lZeDzTUpLfdto790bb5okDDBt1Ag9ZrePq0maM1ml+xgWpg+8Vt/OqBzAx15jB0fIY4rj7UGEqrvK+0bk9LMwZ0S3N0yAqyT8TnEtYsaUzL21vUZs9Lz5dOaozbODoGATtEbejXoTPHODjMuwg0DHPrU3cI5EygDoGGf+tEn7F303ZVXg80vDJnLhnNhV4q90cjG5ovt+o94gowPNcNNjwbTV6z+bUatAY+1Dn85o4Y1NLw5rfVxNBv8L4+vAzKrrnp9xp+t28ou2TKG49AeDPIhWoXH9mw3nip2F83AiMBouPq5hn+pCVgm+t/b8O0uVqenkBDyxuY6kGgTAJ1CDT8Syf632WrtO2ovP5gv2m2/HnnsmVifz6Vt+Wq7E2LJl/fE2jYuSo/6lWu/V3tLEWyE7DfiuGj0gWy2yBncgLh1Yr3ircvgubryCC1IBBGm/hUouuk89bCJZxIQMB+oPbye48PJUhMEgg0iQCBhoytxXzajODIBoGCCehinXkxSPUEBanLYpC+WJSGVUbLzXSrFX/1loARoddtJgp/S4pYsJDbyC8j/YdvKcsi0j3SZyVHegKdJ5Xns9KXSjXlDKkHgc4d8mN/6TrSo+vhE14kIKD1ocLiwF/VdWC7BOlJAgEIQAACEBg6AnUY0eA9Ob/PRt62VF7vVdD74usidrf80XIG7RBNnTioO6JBn/0BG4ns1fHvTus1INkI+AJ45q+21f8/Ug4BH8kQRjT4yIYFyymTUpIRsJPUJj6daOtk6UlVPQFbTO3lUz8VxPOFIhEItIIAIxoyNiMjGjKCIxsECibQphENjko38e0Z0fB8NGNkioAaauWCfwtNMX+5HH1QWqMAV1PQdf3s+P+9v+pSPbn2uu5etkUS6Phoq49LNcUrOqjIkrCdmsD7leN/0hP0/7Bk6txkqIBA5xEVuq30aelv1G5avwdpJgF/80SYzrdiM/3HawhAAAL1JFCHEQ0+Z/iCbHjsFcqrBxZ7V7b8ReQKFyv11LZDVJEpWqfhCl8Uclo05SPtqFUetbAT9bvTDabNl4e14bThr4gLvYGs+l3qD8DfPhF+u9xUl8q9X2G2idrlOelZ/VJyvE4EbGO12TNSBeUZKVSnlknuiy2tttM0UbtVuurs+XztFFNQ3D4v3UmqqWe27+xpWvWNEQ2tak4qA4FqCdQh0OAnd628nUVM841DoGGfLLmLyWM/lE9PFmO7Mqs+kZgH6tnwhxsOD3J5jxaSmYB9Vwz94WqlzCbImJKArR0z/0nKjCQvnMDI20Ha/CBTOMXyCzAtlBymvvgit3OXXz4lDk7AFlfbXSG9S9oTbLDl9f0w6R+kD0j9uq83pdk8g5dZSwsEGmrZLDgFgWYSqEOgwXsBLsqGz1aPT/oHZstfRK6RG8WFirCOzboQCL3x0/T704Mykp2ArRP/Dx+V3QY50xOw48Td1wR4Vfq85CiOgE1Sm2hqlj0lXbO4crCcPwE7WG3mD6Hfyd82FsshYAur/b4iHaNjxYMPIQhx8djHy/GwhFIINJQAmSIgMCwE6hBouEYn7YuzAQ+vBvML+0ez5S8ilynoEW421ijCOjbrRMAXMQ03Hp06edU8XwLHh8Ry/ub53lSPfR0A0xxz002lL8yJ1IeAraY2eVzq18a6vFGpPnhq7Yl9X23m9ySfrLWbOJeSQLjXvE3t6ufLtnciEWhI+esgOQQgMD6BOgQartaJ+4/juzjREVtKef2irp6EuojtEPu0eV08wo+iCNgH4rbWWiFIdgL+1pjwf8xbPLJDzJDTPhxz3y1DZrIUSsB2j9vma4UWg/GcCYQRKeeq7Xy00C45G8dcJQTCQpH/UXt64G+xSlwot1ACDeXypjQItJpAHQINV+nkfUk2yh5ZDg8oh2fLX0Qu2yj26Z1FWMdmnQjYKnFba/4mkp2AzSWOWnfUrs9ug5zpCfgcY7tBeqd0gfT5yVEsATtF7eIPrNsUWw7W8yXgC0KGh9Kp2m6Wr22slUvAllAbXiv9l1QdW70SRjmcqf2PSnXtsl17jzb4M4GGBjcerkOgbgTqEGj4m07Qf8oGxiYrr49oODpb/iJy2cqxTzWazlFEPbE5k4D9U+2tng5kMAL2wfj/ZqvB7JA7HQF7U8y9RsHadDVob+owX/wWtc990mXbW8821iwsIHi72u1h6ZptrGH76xTWYfL705uky89Z39DR4Md9ZOOh0gekH5kzXeP2EGhoXJPhMATqS6AOgYYrdXL+c3ZEocfnG9nz553T59SG4MeX87aMvToSGFn8k1cFDtQ8YXTSY/rf+c1AZsicgYD5UO+npStlyEyWQgn4Yp02XXq+lLU0CmWdt3F7idrMe7s9WLRM3taxVyQBXy/ILpV6sEidR0nEdlRa/1/V+jeNFgINjW4+nIdAvQjUIdBwhU7MOqFnlXCD/L2suYvJZw+pTicXYxur9SJgG6qtfVTNgfXyq4ne2NfFcYZ09SZ631yfbS0x9xvknze3Dm323BcWDOeYg9pcy3bWzbZQ202TeofKAu2sYxtrFdba+JbaLMW1KLwe069fWzWcCIGGhjcg7kOgTgTqEGi4XCfmy7JDCUMTT8yev4icdp3qdEERlrH5/+xdBZwlxfGe5e52D+e4QPDD3T3okaDBneCugRAgWJDDNUiQECRocAkWgh7OH7fgcjiHS5C73YP6f9U3b+1W3hut7vn696udt+9NV1d91TM9U11dbQ0BnWWUj0H/sSaZf/K4bPs/Acu/+ie77xI7J486zFb2XZPw5Hf3GI1oUGfQsuHpF7pG7Yk9b4f9sH0pS3gIOMfEcbDvONCsnutHR4PnBqT4RMASAiE4GvQlz9hMnNuuD8mDwio/RgN/0xY139oaDdo5LM3SauO2NBuLfojkpCzpEJBbgOO3IOxnzlIcAjIVMP8U9DxoQHHtsqX6ENDQexkNGgWCrVj8QkAOgd3UkXehX3JT2vEIyEywHfIvyFWga0FXgy4GnQO6BPQOSKMZ/hwAYnQ0JDTiRAnrsRoRIAL5IoDBN0qz9nQM6iMvgqkC50c0vSmJMhBmQDTgHzDVOk1R0/lY0D1jBixDYYGX4wiJSaM1QlGoRD0034o6bHYsUYYKNt2EteTRoaCFQbtVEADjKjd9AgG3BQ0DXWBcWIo3AQJNJ+Crv4F2wsvokRP8zC+sIwCHQnQaaFrQKNBnIC3TgAaDLgctj+cjjWqYGLQ76ErQv0DL4TcWIkAEiEAlEbAQ0fAYbsTqQU1Y5BXUNxa2LidCJjhQdMAJp7RGLW+2RS2I322RsVHzRuFollYT92DxPeytDxssqRFw24m9DTw5s54ay0YYuG1GNYu6Zsr3PaFZI4p7dG772LKnR0JTVIeA3s/ci6c+G+xKUHxCwCWHvBN2+wg0f++Syzz4/VWQJjbW58ADQB+DfHKc6/N4imfy3tHhL0SACFQPAQuOhkdxE348OfRuv+r7k9fPo2b7Vn1z5MG9LJ5YNnFdJ0fDMWXJYbNduQn9WF/QuAY3tYH0ocw56ujMSo1lowzcLgcaAsxZ80ahK+R8txb8EdgHkXyyWCFNspEMEXBO6YdhO13Lv0GGjMkqdwTcjmI3w26IZujp2nO7jOjys+dAM3SII+vh/887/jf/iY4G8yaigETAHwQsOBr0oemJ5JBJyoiI5C33XlM2gU46a7Fi7+f49wscDYfWHA34fJt/GuQpsWwf2/zXebZSDd7ugQ6h4ml2o6kGUvloKRcBe3U2YEcVFnsIyMywDV5c5A3QFPbko0R9IyBDYLeXQD+CVuj7XP5qCwHn6LsadvsKtGyHbDIl/tfrUSfOul2TshS+0+dBXyL06GjoMCw/EQEikBIBC44G9e5j66ekRUaiPjzIlopgrZ4bWDazJFVaWcZEA9fscDS0IISQpQMBGQqb6yzVXzu+46fkCMjh8TXE9a3JQUxYU7Du2EXn4L6syylY7CEga8M26gy6xp5slKh/BFxywfdgP31hXbD/83mGHQTcErN/wG53dMgk1+P/USDcO7sXOQ/fY0LMm0JHgzemoqBEwD4CFhwND+Em/FRyqPRmr2viLBWZDTKpB3tfS1KllQWdZZpOjgb5Poo6hQem5R5CfbkfNn83BE3K18E5btDF5MbyZamiBIIcAO4etlsVtfdDZzk5ttEefshLKbsioGv9nUPvAxxn6fob/7ONgNtydtLxMsqs8XXYQwSrLo9xDsFNbevTRTo6GrrAwX+IABFIg4AFR8ODuBE/nVwJtzb+neT186jpQr/V0YAHwbAKEkK+W3M2jIla1g1Lu7TaqGPJvZxx7XRaKF19ORt4/gSaKxN2ZNIAAm7W7hlg/wUI0Tos9hBgvgZ7NmlUIt2RQLCJk7wMmrrR2jzfAgICR58uheleXL6b7/DbOeN/8WY3Cjoaupuyzv8Z/lcnUDyNCBSMAF7IU29vObhgmftprmksTvgSFNyMf1MnpxA+L9kPEFX7GYmiXMEsBksGCJwW89gvA15k0RACTQjLj/YCYT15dHxDVXlyQQg0YalWtAUIkT/RtXih0W1hWbxCoAlr+qPNQXODkPdIdzdg8QyBTyHvZLBdp0TQsga+uxc0EqQTEPPg+CzoJBCWzERYMhzdgO93xJGFCBABIhAsAhYiGu7HzVZvwAmL6Fq5bxNWzrGa26LvvhwbKIV1t4SQt5cihOlG5Xn0xxdMi+iVcHId8NQZv2m8EjsYYeViYK9RJUsFo1Jwisg6sA8cQ3J1cKpVRiHZCfbTKEh1NnR6Ya0MAB4rKoNgMyz/FUw0yA6gK0CarwkOWhcZtiCOcEZ4sxsFIxo87o0UnQhYQ8CCowEe3zTJHF14tc7sGCuiey4byx2RHiIkhFy9tnQCx9HpOYbGQY6E3fWBcc7QNCtHHxd+qniOKKf9qrcq0wJ7TVj3BIiRoWa7g5wC++h1wnwNZm3Un2Dy59iGl+LY1N/Z/N0SAqIRDQeAngRdAFpzvHRe7kZBR4OlrkVZiIDnCFhwNGDWX2eBkxZBKJp7wGpOyiGfem4m0GCkRTptodDQTo4GnWqeKR3H0GrLQnF/PCg0zcrTRx4Epp+BJi5Phiq3LL+P+/QuVUbBtu5d8jUsYVtWStc7AnJmfK2d2vs5/MUfBLzcjYKOBn86GCUlAuYRsOBouAcD64vJkWqfQR6SnEceNeW4+IEBnu6wChJCjqo5G8ZGzcxHMIF55XXYHjPALNkgIOvF19Ke2fAjl8YQ0P3fXdjv5zgObawuzy4OAbdlojrkRoGmLq5dtpQdAm43g3/Cfhqdcmh2fMmpeARk1tiO/exGYS5JJB0NxXcWtkgEgkXAgqPhbtyM/5scYReupoOysZn19u3hNAlQUAV5Gq7r5Gg4NijlMlHGrc3UPjksE3aVZ+IevjUr+1sgvPSyFI+A/ArYax6AC4tvmy3Wj4CsBhtpTo1/gxh+Xz9whs50a/6R/4hLYQwZJYEo9exGoUkidYmtfAM6EaTLLz4GlZkkko6GBNZmFSJABHpGwIKj4S7cVF/qWbx6vnU3c32p06zNhorbP1nlWsWQUJmI0hoN2qvmaIDT4YhMmAbFRBaH3dX2+wWlVqnKyPYxpppln6UUBNzaY3U2LFdK82y0TgTa1/ofWWcFnmYOATfL/QCuNXUabWlOPApUBwKyMWz3LqhTck/djUK+A90KWhRkMUkkHQ11WJenEAEiUB8CFhwNmjQRs5VJi2yH+vpSt1hSDvnU0yztTq6t8+FfHteRUTRQd5/AsomjP4qiScqTxHLLbvb9UcsS+iWbm+V7H9dUih1q/NLYnrS6bEJ0+cTzoE4Pz/YkrbZELgJIX2T0JXXNamPhs/YyBez3NKgNtI7PmlRT9j53o5gKNn0DhGcEtXPn0v7sWFb0Hh0Nnc3Bz0SACKRCwIKj4T+40b6SXAvZBPXV0bB8ch551JQZY7kOzIM7eVpHQE6G/XX219iSHuu49SWfRoi4ax2zQizlIOC2b9P77Z/KaZ+t1oeA6IuMLjX6AjSsvjo8yx4Cuq2vPh/Jj6CV7clHifpGoNfdKK6HPUeBYN/uRc7D9491/7bA/+loKBBsNkUEQkfAgqPhDtxUsUYtaZG1UF8ffFdPyiGfei6Bms4onZkPf3K1jUD7rMQfbMvpk3Tuoe1LXFP3+SR1WLK62XIN6f4exBdY08Z1odk/wE5PgVpMi0rh+kDAJfnUEHxdx79EHyfyJy8QkFlhR31mXXFCcd2SW52g2HTC3wr7ho6GwqBmQ0QgfAQsOBo0adVryaFWL7+7aW+YnEdeNQUrC3R7I5ZqIuDWZz5UTd3z0lqOia/3pfJqgXz7Q0Dmgw3Ggm7r70z+XjYC7UsLzy9bErafBgGZC9fbJyDdVWTeNJxYt2wE6kkSWaqMdDSUCj8bJwJhIWDB0XA7Bs7Xk8MqS6K+eoe3Ss4jr5ryJOTiOv284DXPV/4C+2tUy/TmRfVGQBdKrLO0dOCVarN2hw+WrrHYRkD+jutFx8gdbMtJ6fpGQBaBDb8Caa6aYX2fy1/tItBvkshBJctOR0PJBmDzRCAkBCw4GjArpklxkhY3u6YPUbsm5ZBfPfkX5ELII0s1EXBbAmrf3Kua+ueltZwDTNWBM3deLZBvfwjIYOCP+7Z8COqWzKy/uvy9WAR02YQ8AdJ1/osV2zZbyxYBzUXlli1hckZ+mS1vcisGgT6TRE5UjAx9tkJHQ5/w8EciQAQaQcCCo+FWDJhvNiJ013PVs+9ma/7Y9XsL/8m5kK0V1GRBGspQNAJuPfsHsP/IolsOuz2ZDZiOAzEcvFRDy6qwgTrSzipVDDZeBwIyC+z0Oeht0JA6KvAUswi4LRJ16dILoKnNiknB+kCgtySR3auoU6LwQkdD4ZCzQSIQLgIWHA23YLB8KznE8gvU14fdPyfnkVfN9v3Mp82rBfK1joAmA3Wz75x9ytRUchVwHQOaIVO2ZNYgAnJF3L+XbrAiTy8cAU2Y7O5FulzRwsxp4QiE06ALv1dnq0aqMKIoHMN208Tl5bgSNi5ysoqOhm5W4L9EgAgkR8CCo+Fm3EQxy5K0yMSor46G45NyyK+ebB/Ltlh+bZCzbQRcqKv2zz1ty+mbdG69suJ6qm+ShyWvwInqtlDU2dUyZt/CgjN3beQw2Emvm6Nzb4oN5IyAbAs7/gx6GDRpzo2RfSkIyLGwrSYAnazA5uloKBBsNkUEQkfAgqNB8xiMSge0m6XBzLG1IqtBN32oW9eaZFnJc20UDWiNBi35URRNkhXPsPi45ROavOuBsPSyoI3ueiC4hzF8uFxrtO9scGi5crD1/hFw96ObcM3oC+qG/Z/PM2wjoLmpnC3vxXGwbVkpnScI0NHgiaEoJhHwAQELjgZ96HknHVjuZePCdDzyqN2eqHL3PLhb4NkWNV/fFrUgEUXzq09FEWc0ezSKnIY+zt0nesQmzZeyHHBVR96INFxYNwsE5G7YQZMNzp0FN/LIEwGZHHZ6CaROugXybIm8i0BA9oEd9T54B6i5iBbZRtAI0NEQtHmpHBEoFgELjoYbMTi+m05tGQ0eWLNtrejaSfcAcIw1ybKSB06Gz9TRMJ4GrpAV37D4yDJxP8ADIUu2CMj9wPZLEF6eWMpDQGaHDX4A3Q8qcj1xeSp73bLMBTvpVolvgIZ4rQqFBwJyIEidDRohOpCQEIEUCNDRkAI8ViUCRKArAhYcDTdgYHyvq1iN/qfJJAVJJS0WN2t0sUXJspAJDob7OhwNLftlwTNMHhq1I4+EqVuZWrXvfHBImVKwbUVA/gTSl51diIcPCMiasJVGWv0HxOSQPpisTxnlSNhRr79rQAP6PJU/EoHeEaCjoXds+AsRIAINImDB0XA9BkWsYU9T5EXwwBpFi0VegWx3WZQsC5nGRs0n1hwNWD6BBxyWnhGQk9EPdF30zD3/zm+TIyCPAldNmMWEaMlBzKCmvtzI0yCdKZ8+A4ZkkTsCcjBspS+nJ+XeFBsoAAE5MbbnpTgysqgAxANsgo6GAI1KlYhAWQhYcDRchwHxg3QACG6MShaL3APZsB42zAJHw0YdjoaWUWFqmYVWsgT6gT7QM+ojCzi78JC1Ymz37/I1/ykBAcEOOzIOBAcyix8IuBlwvTdt4Ye8lLJvBOSM+H74977P469EoEcE6GjoERZ+SQSIQBIELDgasHGBfJhE+I46Gs2gUQ0Wi1wC2b6xKFkWMmFR9ow1R4Me0aGw3R1LzwjIm+gLRh1iPUvsz7eCXKTyMWiwPzKHKqmL3tEX1w1C1TAsvQQ7BslzoO9Bi4alW1W1ETgZnGP7zKoiQL0TI0BHQ2LoWJEIEIHuCFhwNOh6wo+6C9bY/5qfQd5urE5RZ8tx8YA/WVEtFt0OHAwf1pwNY6KWdYpu35/25Pi4L8zqlQx37wAAQABJREFUj8y+SCrrx9ju7YvE4copE8MWb4HgQNaEuCz2EZBZYavPQYhKk1/Yl5cS9o2A28ZUl0+ow4/LYvoGi792RYCOhq541P0fE93UDRVPJAKFIoCBMEp7fWImJrK6PrsWrTFToagW2pg8UWuuKZKla595nAABRO+4stkEv/CLtAhoMtgXQAfhwbolLTPWT4NA04+ovStoBtCpaTixblEINL2DljYHaQ4ZjTLkzgUAwt/SpM9VO4KuBh0Ie2LCg4UIEAEiQASIQLEIWIhouBKD4Cfp1JYLweO7dDzyqi3rQTadVfhNXi2Uzbctaj64FtGAY7CJL7PBWV5FX3gmG17k0hUB2SS+1vbs+j3/KwcBuSC2x6rltM9WG0dA9o1tdk7jdVnDHgLqMBLNg6XPIMfak48SGUSAEQ0GjUKRiICvCFhwNPwTA+Cn6QB0yY80o7/BLMuyeDzIb59OR7u126KBwzscDc1fQ1KDdrCCX/sWZPNYkSgcOVy4MKIadBcbRjWUb1ddNuFs8Q6Ok5cvDyWoDwG5CPbSF9Pd6jufZ9lGwDkbro9tSmeDbWNZkI6OBgtWoAxEIBAELDgarsAA+Fk6PNvzIEySjk8etQXJEd1D22F5cLfAczSWrSCqYVzN2YCdKOa3IJdNGQQOBtcfRtiUz3ep2qMa9vJdkzDklzXj/n5eGPpUQQtphs0eBrWCVq6CxuHr2MXZcEz4+lLDFAjQ0ZACPFYlAkSgKwIWHA2X42EGSajSFDkUPHQGBi/11oqbZR0L2YJ+0G6Nmp+rORpao0G6NpSlVwR06YS81uvP/CEFAoxqSAFeTlXdDLlGnAW7fCwn4Epk6xzk78JmmiBy9hIFYdOZIeCcDTfAnvqsRGdDZrgGx4iOhoQmTZtsLmGzrEYEiEA/CGDQSx1qr8kgtRhMCOmSMmlCyICTQSr0TTo4xWWiZWufeOwRgavw7dx42MOyGpZsEXDX21HgqdfbztnyJreECOyHeh+BNJfOZAl5sFqhCDTpckbs5BINBt0Gu2EZDIvfCDSNg/ya8PNG0GGw6dF+60PpiQARIAJEwDoCFiIaLsWA92U6oGQn8FAv/YLp+ORVWx6EbNinPNyCKIbtahENOI4MV9MsNBNkdhed4T0lC27k0R2B9qiGD4BxS/df+X8ZCMhvYQu9R59bRutsMykCsgFspveqO0ADknJhPUsIyCDYshbZQGeDJdPYkIURDTbsQCmIQBAIWHA0XIJB76t0aMoW4KEPscuk45NXbdGEl1/kxd0CX3iKpsTyiZeQq6EVToftLchkWwZ5CH3iPRATZ+ZiKNkY2Oo9gbkacsE3CVO5GPbQl9ZVktRmnbIQkIPja+nMsiRgu1kj4JwNiGxw90iNAGMhAjUE6GioIcEjESACqRGw4GjQh0/dqSBFkXXjAfM3KZjkWFWOj+ULOmx4RBRNNGp8qG2OWIbCWvaI+8TKoWhkSw8X1fA8MGZUgxnDyFSwB5aRydsgg8vczABlUBDRyEMBcScKg9ZJJhKdDclwC74WHQ3Bm5gKEoHiELDgaPgHHl6+Saey/Dp+CFovHZ+8auvDmXtIWyCvFsjXNwRkKPqEZnW/wDfJ/ZG3Parh9/7IHLqksnZ8Lzw7dE3D0k+XILmdKNpwxHjLEgYCdDaEYcdMtaCjIVM4yYwIVBsBC44G3bP723RmkKXjh9ffpeOTV21ZK5YPRxYiUENAbka/wLIh5hGoIZLtkVEN2eKZFTc3O65LKIZnxZF8ikBApoHNELSmOZVkriJaZBtFIOCcDTfBphqxcmwRLbIN0wjQ0WDaPBSOCPiFgAVHg2Yi/y4dbIJIATdI7pKOT161Zf5Yvt3zaoF8fURANo37xSY+Su+HzLJRjPE+fshbBSllCGyiSyj0pTXo5WThWVMTLuvEgLwOmjo8/aqqkXM2XA+bqrPhlKqiQL0dAnQ0sCMQASKQGQIWHA3nY2CrbU+ZUDEZFg+Q+yZkkHM1XY/sBvATc26I7L1CQAajX2hEwy1eie2VsC6q4Rlg/DFoEq9ED1rY9l0ocP9n8QsBF6GHrRLlAVCzX7JT2t4RkIGw55UgdTacBWrq/Vz+EjACdDQEbFyqRgSKRsCCo+HvGNB+SKe4W++ugyP2hrZa5DPId5VV6ShXWQiIOtpaQQhLZskHAVkH+Or94YB8+JNrMgQ0P4mzC5eUJQOwxFqyZ2y7y0oUgk1njoBMBLteHNtWxyY6GzLH2DxDOhrMm4gCEgF/ELDgaDgPg9mP6SBzM8P6ImE4YkCegnyPptOTtcNDQFZAv9C+u3d4ulnSSPDw5Jx9k1uSqtqyCGzhlk/oMgosp2DxCwE5A3bTe9cRfslNaftGwEWB6XOZ2lZ3GxnQ9/n8NTAE6GgIzKBUhwiUiYAFR8PfMJCNSQ+CaCgnwv2sFtH1jx9ZlY5ylYmAvIW+8WSZEoTftqwGjPlSZM7QmhBSNDEkQrZZ/ELAzX5j2Ze7rowmYvYLUVvSypmxba/GEcsqWCqCAB0NFTE01SQCRSBgwdFwLgaxsemV1S0yNeTPapG/QD59oMY2YSxEoDMCMgL9Ql+C5+v8LT9njYDcB4z1PjE0a87klwYBOS3u/5un4cK6ZSDg8g9pDhRMFmh0FktYCMhJ8bV5I47NYelGbXpBgI6GXoDh10SACDSOgAVHwzkYwLBGPW1xWcyvTcslv/qyTzxgz5VfGzY4I+HGzG1Ryx1tUfONeKtjZvJ+zSJzxH3jhH5P5QkpEJBlY5yZVT0FitlXdUvfXoJtvgTNmD1/cswXAbWZvA/6AjR3vm2Re/EIyFGwqzrCbwcNLr59tlgwAnQ0FAw4myMCISNgwdFwNgYvLHtIW9x2W/9OyyW/+rJePFivml8bNjiPjZpPgaNBxlPzYTaksi6FPIz+oQ/rSMbFkh8CcjMwRk4YvtDmh3ESzrIYbKJJUe8EMQFdEghLrSMLw24aLfQmiIltS7VFHo3LIbCrOhvuBnH3njwgtsOTjoaEtuDDW0LgWI0I5IwAlhNEWTxYfgc+2EbSbHk3lmyYWQkzEgw3W8xM1krTirVPPPaJwGX4dSbQKn2exR/TIqCOLw0BPiItI9bPEoGmZ8FtBGh10F4gFq8QaHoB4m4K0vFNnXmc+fbKfv0J26TRdvuBdKLkDtiXSXX7g4y/EwEiQASIQGQhouFMDFrqbEhZ5EHweTolkxyry5SQT2cEjsmxEROs26KBwztFNHwzIoro6O3XMjIV+oauc76031N5QkoE5Arg3AaaMyUjVs8UAc1uL4+AsPpK5smUNZkVhIDsBNvpOKfJj7OYQChIbjZTHwJuW1M8r7ldfIbUV4dneYYAIxo8MxjFJQKWEbDgaIi3yEoLk6iX/bW0XPKtL19DxsvzbaN87h9F0STIz9BWcza0RoMWKV8qHyQQ5BgRRObIZD5I66+MLieGhulf7a8OoUrubINxyW0HPChULcPWS46D/dTZcHrYelZVO9keth0Heh70y6qiELDedDQEbFyqRgSKRsCCowEPI/pQkrbIdeDzYVou+daXZyHjQ/m2YYN7a9T8eCdHw942pLIuhfx2/LUgO1iX1H/5RHPD6MzcEv7rEpoGsmN8HZwYmmbV0EcjGdSh7pwNGm7PEhwCsgnsi93C5A3QsODUq7ZCdDRU2/7UnghkioAFR8Np8QNJyvB63dpSk1FZLnITZHzfsoRZydYtISScQCz9I+BCxxEQosuAWPJFQKYFzjpzjuRmLPYQcKH3P8E+w+3JRon6R0AQjeISB6ozb/P+z+cZ/iEga8C234PwTCPz+ic/Je4FAToaegGGXxMBItA4AhYcDadikNIwywGNi9+5hpwFHgjns1w0lFT04VmT0QVdxkQt69YiGnAcHbSymSonJ6J/6PUwR6ZsyawHBGREjPVqPfzIr0pFQLAtrnwAeg/EteCl2iJp45ow0EXxae6Z4Um5sJ5lBGR52FaXhH4KWtyypJStbgToaKgbKp5IBIhAfwhYcDScggFKX6wG9ids378LsiI7PhP3fV6Zv8o+sYzBJ6HDk8cQ5Gn4ueZsgOOByd3q6no6M+T68TF1nc6TUiCguTDkE9AzICauS4FkPlXl17CLzohfkw9/cs0fAZke9nsH9BVowfzbYwvFIyCLwrZ6H0VEqaxYfPtsMWME6GhICGjKsOyErbIaESAC/SEAJ4MraR/0dXtLLZOOP5j8+04s1awmpctQqKmi6CsY9sUay4min1eqfeaxLwSaXsWvOtBvh4c2jlt9QZX6tya9Z6hDZzHQFqnZkUHGCDTdB4aIeIs2w7WwfcbMya4QBJo+RjNrgXSc14TNMxXSLBspEIGm59CYju+6dPVO2FjtzUIEiAARIAJEwMT2lidhYMJDSNrlBPKHmM+sdu0qC8cy7mxXxuwka41azq5FNCA55GXZcQ6dk+wa9xOG9OduareW/C3grYR15Sy2ENBxwUWcfIsjlxPZMk4D0sgKsN+PoBdAUzZQkad6g4DMAtti5y/RHX3gHGTxFAFGNCQ0HGeGEgLHakQgZwR0pkNLVhENCIc2W96JJZvNrIQZCiZdkho2MaKhfmyvwal4KI+2r78Kz0yGQFMb6v0ZNDto92Q8WCs/BJrw0hJtCdKldVfgBUaPLN4h0PQwRN4atADoZtixxTsVKHA/CDS9hxN06cTLoKtg4537qcCfiQARIAJEIHAELORoOAEDkkY0DE6HtWa2dnyWTccn79ryBeT8Z96tWOCPuPTpahENesSbM2Y8WOpDQPuI/ADi7F99gKU4y23H9zSw1oRmSGDHYg8BgRPI3d+PticbJaofAdkrtuP1OHICsH7gPDpTsHJSHontfKBHglPU8QgwooE9gQgQgcwQsOBoOD4ekCZOp5WsHfNZNR2fvGuLvtBgEK5GwZKJ12vOBmx5yXDKus0u6MfuxWq3uqvwxBQIyGox3kelYMKquSIgOhM+DsToqFxxzpu5HBtfa+fk3RL5l4WATAIb/ye2M5bHsniEAB0NHhmLohIB6whYcDQcFw9GKZM4ysoxnw1sg+72h//ItozZSQfnwsnjHQ26A8VAbIXFUh8COtsn74J00GcpBAG5G3jjnii/LKQ5NtIgAvIL2OZDEEK0dftLFn8RkItgQ41kPNJfHSh53wi4/DdXx3ZWew/o+3z+agQBOhqMGIJiEIEQELDgaEDWd/fAkTK3giwR89nGtmHkFMj5M2iwbTmzke6pKBoEZ8NWcDIMz4ZjlbgIZtfdtbFAlbQuT1fB7hPu2jyvPBnYct8IyCqw0U+gW/o+j7/aRkBfOl2Eijob9rItK6VLjoBzmCNyxY1juGYlZeRqcklYs24E6GioGyqeSASIQH8IWHA0YM2tG4RSro2WuWM+e/SndLm/y56xnPOWKwdbt4+AzIq+ok6p0+zLGoqEchnw1vD8+UPRKDw9ZATsoy+ofwxPtypppM52uR+kjiNuLxu06eVw2FivWSwblSFBq+q/cnQ0+G9DakAEzCBgwdFQm7WdIh0qMkM8kP0pHZ+8a+se027A/W3eLZF/CAjIXegvn4GaQ9DGvg4yM7DWJJz/ti9rVSV0s+H3w0ZjQUtWFYUw9BaM+277UuwuImuGoRO16BkB2QU2VifuS6CZej6H3xpAgI4GA0agCEQgFAQsOBpGYNBRT3fK7PrugUX5IELCcpF5Yn0ZLmrZTGZka99NhYk0C7NJe7K6NQprkg01iIBzLKsD7i1QSid1g03z9IwRkGlhw9dB34N+lTFzsjOFgCCHlm5A5fKszGdKNApTQ4COhhoSPBIBIpAaAQuOhiMw6KiDIGU4nVsLqHyMh5nr/uEuHP4vqa1HBhVAQCMZXETDvRVQ1oiKgnwx8jHovyAmMDNilQnFcNFhurTomgl/4zd+IeCWiWmizy9BC/olO6VtDAFZETb+CvQFiI6lxsAr4mw6GopAmW0QgYogYMHRcDgGG3UQZJBF3M2InG/fdvIB9L3RvpyU0AYC7QlE57QhTxWkkB3j+9LuVdDWXx3l5NhOu/mrAyUfj4AsAFvqy6c6HGYlKiEjIAvFdtYolrVD1tRD3eho8NBoFJkIWEXAgqPhMAw06mgYmh4k+QR8rkrPJ28O8hDkfDbvVsg/FATaE52eFIpG9vVwEVK4RuVTEEPzzRrMbaH3GGyk4dh4eWHxGwFZFnb8DvQGCEsqWMJFQIbBxq+C2kA7hKund5rR0eCdySgwEbCLgAVHw58xyKijAXukpy1uve6tabnkX99ltv8m/3bYQjgIyH24RtSRNigcnaxr4rZS1HsTHTymTeVeWDQU+xXQpKZFpXB1ICBrwI6aHFIdfVPVUYGneIuAPvcJXmzdMyCeBVkMIEBHgwEjUAQiEAoCFhwNh8aDzDTpQZXnwGtkej55c5ARsc4ZLBfJW1byt4GAbv/mHsaYFLJQg8i/gPsY0GyFNsvGGkRANoyvj0sarMjTTSIguM+5bS8fwXESkyJSqIwQUPsKJojc+PY3HJkXJyNkE7KhoyEhcKxGBIjAhAhYcDQcEg8wv5xQvEa/kYfB66lGaxV/vmwX67xE8W2X1+LoKJq0LWrZf2zUvFV5UvjacntSyHt81cBPuWUuXKs6u3qdn/JXSWo5K76vblslrcPV1W2HqBFFd4K4vW+4hoZmbsvaC+LrV527Ewetrm3l6GiwbR9KRwS8QsCCo+GgeHCZLj1ycgd4vZaeT94cXNZlfYCq1Ox0azToNDgasCCzBVPELevmjXJ4/NuTQs4dnm6WNdKdbNxs23DLUlI2t6PP07CVJphDYkEW/xGQA+JrT18+B/qvDzXoG4H2aE+87EoGUa59t8Zfe0SAjoYeYeGXRIAIJEHAgqPhwPhBYvokCnStI9eCFzJWWy8yY6zzwdYlzVK+1qjl/JqjQT9nybsavGQO9JufQXjxZSkOAZkSmGtSyOdBDOstDvgELblr5GvY6WUQtill8R8BORK2VMc8Ej1rklaWsBHQxJAuQeSbOCKijKVgBOhoKBhwNkcEQkbAgqOhNmOBl++0RS7CwPRtWi7515cmyPkjCKGC1SmIaNimk6MBDxEsjSMg/0G/+RLE0NLGwUtRQ3YG5vqys0cKJqxaCALt+RquLqQ5NlIAAu3bmOoYj/GTJWwEZDXYGc9y8jloubB1NacdHQ3mTEKBiIC/CFhwNOyPgUQf4GdKD6OcCT4/gTx4EHEzbvem19kfDj9E0Yw1R4Me4WmZxR/prUgq68fXy/ZWJKqGHG67Sw3L1wffIdXQ2Wct219M9/FZC8reGQE5J773ndX5W34OFQFZBPb+AKSTMhuHqqVBvehoMGgUikQEfEXAgqNhv/jhYeb0IMqxMS8PtjhzWZZHpdfZLw6tUfNrNWcDIhy290t6C9K6pFnvo58/bkGaaskgK8T3F77omDe8rueXB0CayPNX5sWlgHUg4CIBL46vwRPrqMBTvEdAJ6DkBZBOIP3Re3X8UICOBj/sRCmJgBcIWHA0YPBwEQ0ZzG7LQTGvDBJL5m0/F30xDvIOyrslS/yRm+G8DkdD86WWZPNHFjk87udL+CNzKJLKlcBer1smGzRvUsE4IB+DdFaUieXM26seAV1k0dWwp0ZB4j7IEj4CMgVsfU9s87/iyDwd+RqdjoZ88SV3IlApBCw4GvaNB5Bh6ZGXvWJeHiQQEoT0uoelOdPr7Q8HbG25Wc3RgCNm5lkaR0CQONXN1F7YeF3WSIeAm2HTXQ3w4MtiHwFZCbZqG28vvqDYt1c9ErpolZthU3U2ICKSJXwEdEJGLoltfguOHkStemsVOhq8NR0FJwL2ELDgaKi9cM+WHh7ZNh6IFkvPK28Osk4s6xp5t2SJPzrcNG1R8881ZwO2ufTAKWQJwZosbocVfeGdqvYNj0Uh0B5RsmFRLbKdNAi0b5F4XBourGsJAbeV6V3xGLq7JckoS54IyBGxzZ/CMYOdyvKU1VvedDR4azoKTgTsIWDB0bB3PHDMnh6e9mzjmMWyXmS+WO/KZbFHnoYXa44G5GnY1bqlbMonq8T9h8nuCjeQDAb2o0Bvg1oKb54NJkBAboCtfgbBwcsSBgIyCez5YGzXHcLQiVr0j4BsDZuPBb0LWrD/83lGgwjQ0dAgYDydCBCB3hGw4Gj4PQYLDYGco3cx6/3FbYmkvNaut0Z557mXFX3wPbU8GcppGc6FMzscDc1XlyNFCK3KK+g/IJbiEdAs6O6+9efi22aLjSPg1nm/Dpt9CcrAqd24BKyRBwIyOez5GEiTBeIFlKUaCMjKsLdey9+AVq+GzoVpSUdDYVCzISIQPgIWHA21vAoZ5CqQZTHoqKNhCz9M55KU3eSHrNlJiTwN69ccDTh+Cs5N2XGvEqf2PB+/qZLWdnSVe3Gvwa6tMqsdmShJ7wjIQrCVLjd6DoTZcJYwEJApYc8nQeNAW4ahE7XoHwGZB/Z+E6Q5WCoXGdo/PonPoKMhMXSsSASIQHcELDgaMEA458Dc3YVr/H/NBO947dJ43TJquO3XsHVTtQqmIaZEnoZxNWcDIhywXzZL4wi4WVpcw1I5Z1XjWOVRQ+YF9hrCi8R0LH4gIL+Lx4gr/JCXUtaHgAyBXZ8GqbPBk4mG+jTjWX0hIL+AvR8C6QTTGaABfZ3N3+pCgI6GumDiSUSACNSDgAVHw+7xIAHvdNoiM8e8PMlELRdBXsywVa8gT8P/dTgaWv5YPQSy0ljORR/Sh+thWXEkn0YQkOPje866jdTiuWUiIKfFNvtDmVKw7awRcM6GZ+L74eZZcyc/qwhIM2x+WXxN344jltOwpECAjoYU4LEqESACXRGw4GjYLR4gMDuYtmgGfufZPjItp2LqyyGxvNMX056dVhDRcHjN0YDPB9qRzDdJZP64D53om+RhyOsS0r0DG4wCTRyGTqFr4bZHHAl7acj1SqFrWy39ZGrY9NnYtptWS/eqayvIl+MSviJKVGapOhop9KejIQV4rEoEiEBXBCw4GnbFoCAgvDClLRo253h5kmBRNovlXTGt5r7VHx1FkyJXwymgkz+KIq6XTmVAlyvgc/SlwanYsHJCBGT9+Do+NiEDViscAZkWNnsf9AlopsKbZ4M5IiBDYVPNw6GOpE1ybIiszSEgcC65vDl4xNCcXSwJEKCjIQForEIEiEDPCFhwNOyMAUEdDcivkEVxg8z5WXDKn4csHuvOrbnyBzvgFtq3dWU/Ks3KciuuZc3XMHdpIrDhBhGQpWCvMSBNJEgnXYPo2T7dORueh13V2bCxbVkpXbYIuOsa8xfu2t4qW96V4EZHQyXMTCWJQDEIWHA07IQBQR0NC2ajspuhuiobXnlzcdmyVXfOhOYNddD8XSSP7imOZGgs5SAgswH/H0B3l9M+W02GgGwPm+k9+NJk9VnLLgIuUaCG0auzYUO7clKy7BHQKCWXHFSv7eNATdm3ESxHOhqCNS0VIwLFI2DB0bAjBgEdDBbKRn233dFt2fAqgot8Bt2vLqIlthEyAnJwfB39KmQtbesmh8U2YCI624bqJp2cGdtt324/8F/vEZBpYNsXQa2gDbxXhwo0gIDLn3N9fG3fiOOkDVSu8ql0NFTZ+tSdCGSMgAVHww7xQLBINrq5RFAPZMOrCC6Cm7qG7rIQgTQIuNm7H9GX6LRKA2OqutIC/F8DfQhi5vNUWBZZ2SWHvA82Gwf6dZEts60iEHDOhv/CtupsYGRDEZCbaUMjGeQYkE5maZJQ7EzG0g8CdDT0AxB/JgJEoH4ELDgatosHgUXrF7uvM+VB8MMWV74UwX7u8pUv0lJOywjIhehLGiaMsFGWchCQ1YC/PtRiC0UWfxBwjrp3YDdNqjqbP3JT0voQcMk/a86Gjeurw7PCQUB+h+taHfEaQYrPLH0gQEdDH+DwJyJABBpDwIKjYVvc+PXBfLHGRO/tbMGyCXmjt1/tfS8jYv2H2pONEvmFgC4/ctfSSX7JHZq0cg3soLPjGTlPQ8PHqj5qL/kepOv6J7MqJeVKioCLbKjlbNgiKRfW8xUBWRrXtSZ/1Xvzur5qUYDcdDQUADKbIAJVQcCCo2Eb3PTV0YAdGLIociV4YcsyX4psHeu/rC8SU07LCMhd6E+IkOGLUnlWkumB/9egJ0ATlScHW24cAbc93s+w279ACLtmCQsBtxsFIh7lJxCiKVmqhYDMBbvfDNqgWno3pC0dDQ3BxZOJABHoCwELjoatcNNXR8OSfQla/2/yd/BCiJwvRZaJ9YfDgYUIpEVA1oj70z5pObF+GgRkz9gOe6fhwrplINAeZXZ8Ga2zzbwRkKlwbT4OUofSrnm3Rv5EwDME6GjwzGAUlwhYRsCCo2HL+IEce5pnUeSUmN/ALLjlz0OmjuU9Kv+27LYwJmqZe2zUvPGoKBpsV0pfJBNdi/wWiLPppZlMsXeJXr/FccbSxGDDCRBwCeSug93UAY7xiSU8BDRZqzwEUmcDnYHhGZgaJUeAjobk2LEmESAC3RCw4GjQJD36QIf1c1kUOSLmhxd4X4p8AZmx5KOaBYuiZ2iLmr9ri1qQFry5sjhkZ33ZMb4GNs6OJzk1joAsDDu0gW5ovC5rlIuA2xpPQ+w1gVxGY1O5GrH17gjododyH0ifP/7U/Vf+XwUE3FIaTyalCrMHHQ2FQc2GiEBjCAzC6TOAsAYsmh00FDQAZLlYcDRsHg/0GeUokH1jfsMsA99VNhfG+WTX76rzX2s0aAl1Moyn5jEfRdEk1dE+D03dNoujcR08kgd38mwEAUFiTvcis24jtXiuBQR0KzzR6wi3JNGxnSU4BGRi2PY/IHU2HB6celSoHwRcTqOHe76+Xa4dnQhbFbQm6GjQ8H4YhvAzHQ0hWJE6BIMAPOLRbiB9SWwFYbDqQpisjR4EbQKyWCw4GjYDZDrI/yobgGSnmN9C2fArgku1t7gcGUUDEdHwdc3ZgGUUaxeBethttEf2IAcIS3kIuJnxUbgnvQeavDw52HIyBHRccpnqMcbrSylLeAg4x+wtsK8+hxwXnn7UqHcEZH7Y/BWQOhSHdz1PDsJ374J0+ZsuscEzvizf9Zwg/6OjIUizUikfEcBsR4SbkHMs/Ijjs6B7QdeBbgTdD3oBhNBZd45+Nx3IUrHgaNgU8OgAv1w2wLis4Rnyy0aqvrnIkTEGv+j7vHB/haPhupqjoTVqOStcTYvSTNCX5AfQtUW1yHZ6Q6A9QSf7dW8Qmf5edydwY9Q1OHInCtO2SiqcICJV8Ozm7HxqUi6s5yMCukOT25IYy2h6KrISftclcIiWrUSho6ESZqaSPiDwEoTEg3y0C2iKPgSeBr8dDPoJdGcf55XxkwVHw8bx4L5CNgC4EDd1NKyZDb8iuGjCMfeAk1FURxEyZ9sGlk/s1MnR8Ga23KvKze3AMg59a9aqImBHb7kcdsAYkFXklh3NqiFJ+xKYSiftDdvWgqWu8k+QPj+cC6JTKWyDd9Oupy2hBZOD8jEITsbKFDoaKmNqKmoZgbkgHAYj52SoV85j4zqWohosOBo2ApQ6sK9YL5B9n6eREY4flmT4UjTZmJN5W18kzlpOeOxmrDka9IjlE3qNsaRCQOZFv/oZdHoqNqycAQIuwuQz2AIOamnOgCFZFIqA24niJthOxyo4hlnCRMDtFnNhbOdLcbSeZytMM5jQSpAkUrD02S2tQNRD5yK/xPd7g9QhNRw0UedfPf9MR4PnBqT4YSCAWXjnaJizAXWQUMbVWaKBOnmfasHRsCFg0Yc3hKdlUWTBmB9yNfhSZEgs8zG+SJyHnNhx4vmaswHLJ/bJo43q8ZTb0Ld0jSn2jmcpF4H2yKWjy5WDrSdDwO1S8AyupTGgKqzVTgaT97WcU+lM2FifS64H0THovU2TKCCnwvbfgZDHoXORrfGd3gP0XnAS6A3Q+Z3P8PwzHQ2eG5Dih4EAZs2d0wA3nLqLvkTq8glLCcEsOBrWB5Q6oA+vG8k+T5RZYn779XmauR9FZzurFJ43gQXGRs0n1BwNOFpbZjSBvH58ISvH18MhfsgbupTO8dMKmywSuqZh6iczwnYfgPR+3chEQ5hwBK2VHAsb67PJXaBJg1aVynVDQDCZ6Gy/RdcfBJGyLkrwZBzjaJf2iFREpgZR6GgIwoxUwncE9AaDTLURZgrdrhN9zRZqgr8DQepkuANkqVhwNKwX39BXyQYYnbl1A8SIbPgVxUW3IpRni2rNYjtt0cAVOhwNzWNGRxEf7jIxlNs+VdeZtmTCjkxSIOBeVL+GLZ4GDUzBiFVLQ0AWhe0wdsrroKlLE4MNF4CAYMLCPU88iiMiD1nCR0Dmhq2/Af21q64yD75D4ne5oOv3+p98CNpmwu+9/IaOBi/NRqFDREDXkGP9lotsQBiV22FiJI7XgW4A3Qd6HtQGglc8ehmkTgdLxYKjYR3Ao7MGv8kGGJfQSfmdlg2/orjIxZAZYXrVLdgeYQB2n/iy5mxAhAOiXVjSIyAbxdfYrul5kUN6BNq34D00PS9yKAcBWQvXlCZa1TXcdOCVY4SCWpUdY1u/gON0BTXKZkpDQP4NO6tjaVBXEQTP9PIUqJuDWPAu4J5hQ4lSo6Ohq+H5HxEoFQHNSrwH6HHQWJA6FGqkEQyYlI1GgvCgH00EslYsOBrWjm/Sq2YHjltXh4ROPhVBaLsbrBCaW92CPA1X1hwNyNNwfnWRyFJzl+BMZ19fA1m8D2WprCe85G7YQtf5dlv/64n4FBMICMZ+d8/+J47coSDoPuFC6fGMJ2+CZg1a1cor5xL3ztAVBlkXdm8FLdT1e/1PbgX9FxTKPYCOhgmNzG+IgAkE1Ps5LWhO0Eygbl5PfGOvWHA0/BY3aDhnZLXs4JGPwA8T5D6V9jWBGS0h8Un3DlkRxbBVzdGA4wcdv/BTOgRkl/g6U6cnS+kIyDDYA/dfwUMdnT+lmyOxAC5hnI5fmoOJJWgEZHXYGVGHLkcHHYRB27q7cnIW7A6HQvfiol1+xm/Ljf9FE4cKIjE1OaRsB5q8ew0P/qejwQMjUUQi4AsCFhwNa+JmrA9qa2QHmrwKfv/Jjl8RnNRT7nDYrYjWrLbxbRQNxfKJn2rOhtZoUCjhiCVDruHdbj9wfYhgMYGA7BVf8/ubEIdCJEDARQvdENtxhwQMWMUrBORXsPWXoM9BS3klOoVNgYDcDHtf1JWBYFJINMolXqbrcju8jv+/Ap0Bug30Fmho13rm/6OjwbyJKCAR8AcBC44GOBjcCzYcDlkVeQI8scbOpyITQ2b1jP/FJ6nzkBVLJh6oORqQIHKlPNqoJs/25TkrV1N/a1q7bfQewDX/A2gua9JRnnoRcPduPJy70OqMcg3V2zbPKx4BWRi2/hgEv7i+bLKEj4DsCVtrNEuco0O2wGdNGKkOCCSHb9+N5kl8rp2DZYoyCoRtMr0qdDR4ZS4KSwRsI2DB0aDhiBrRsFZ2UMk94Ic1c74VeRdy3+qb1FnLi+UTCyCq4XrQEVnzrjY/tyOLPhzfXm0cLGmvWyQ6R8ODODZZkoyyNIKAYNmkvA3SHUUYVt8IdF6e665bfYkcA+JyNC9t2IjQzil8Nmyt0QqfgNTJAAeCJoZ0UU2P4fgGaJquXOV0fHdL1+/M/0dHQ0IT+bBePqFqrFYCAgugzaQz8JWfse5mL8ziu5Jlkjq8TEVzx3x9OiDszku5M8W4JWp9CQw3yZQpmQGBJn0JwtrRaH8csVSn6UXCUjYCTZpc7jBIoeMCllJEeJhl8Q+Bpk9hx7Uht0bSadZ6hNg3YdabJUwE3HW7PHS7E3Qt7L077H1hmLpSK9gWk2HR7+N7tS6ZeQjfwcmkRaMdosVBS+K7z/SbTmU4PuN+wEIEiAARaAyB9XG63niSUGMt5Xu2hYgGhJq6iIZ1slNVLgFPvFT5VuQcyN0KomPUN9N5I68L8dQ+drk3IgcvaPuM2Hewy2zBqxu0gjIcNtR128+AJg9aVSoHBGQI6BEQngXlUEJSNQRkMOyuDnx1Fncrsi2+HwfCUhst8kvQ3qBzQcNBWU6uuRYy+sOIhoyAJBsikBYBZHGP8MAeqVcTN5RovToJp5kpFhwNWOPoBmnFL6MiZ4LnTyDPQpFlnxgLH6MxMrId2eSPgCa1cg9Ac+TfFluoDwGZDzb5ETQS5Nl9qz4Nq3OWW7+NSD23hK+5OnpXVVOZBLa+DaTOBkQkmX2BrKqBctRbVoK91bE4ZddGZGZ8p8ssNFINRbYG6TKbZ0AngXSZxfnjfzP3l46GhCbhDGFC4FitVwQuwC8Il4z+BdLZ+M1AZRQ8oGrIVqKi1wVeyEstGhWiJcuHa106od7iyUDqTPGlvBwLOj+Or/siNOX0DoETIPF2oENAO3snfZACN72CB8/DodopoH1BWNvL4icCTVfDltNBdrXhpfi8ZRx67ac6lLofBJp+gI03wEn64qjLn2B72Qo2xwsoS+AIzAL99BkTTuJa0VxI0R0g5HKIjkBf0HeDy0CngjDmNuGZW27A58dxxHKbpifwmYUIEAEi0CsCV+AXfVletdcz8v3h9rh9lSEJteUrXn/cZWWIDbndQN3fyXX+LgfEPGeqs4KR02SGWO5DjQhEMYJFQJdOuGU6+qDEYgIBt4TiQdhFIxvU2cjiNQJycnw/P8NrNSh8AwjIcbHN78ex2yx3A2x4qicIuMgFvV/vOF5gmRefnwTpriSzg+YB6e86MdmtyIf4fptuX1r4lxENFqxAGYhAJwSG4jM8k9G5nb4r8qN6TzURTRL6HvVKnvGXFXGzVUfDRtmBJrvGPD18WHfr/dR5xUIEckTAherrzEpZ960cdfOZteZocNvmPY3jIJ81oewuUz1mMt34dhDxqAoCgqgGt3TzBRxnrIrW1dVTn13lI5A6DnQZxd2gYePxkPvw+SnQwK746HbG7r6wSNfvTfxHR4MJM1AIIhAGAupkKNvRsEJ8w904O0hl85jnr7LjWRQneRSyP1tUa2ynygjItehrum4UkTQsdhCQneL719F2ZKIkyRBQZ5HcAfoZtG0yHqzlHwKC5xl3b30XR13eyhI0AjIAdtbo3E5jqayL/1tBC02oum5jrluw1/LxCCYKBcvm5AhQ2bmT6GiY0GD8hggQgYQIqJNBqcQiy+PGqhENm2YnhKwV81wjO55FcdI1e6JrPicqqkW2U1UENBu2ewHSteQsphDQvdddws5lTIlFYRIgIJPClk+A2kAYm1iqgYBLFPgVbP4FaLlq6EwtOxCQs2B3OBS6F11m4cZd9AlpAekyRnVEXgNChKHgmVzW7F6rwP/paCgQbDZFBEJHQJ0MSiUW3W/cORo2y04IHdSz5pmddH1zkv1i2cv2avctZoG/Ysp9jrFR85afRdHkBTZbkabkX+hv6tiatiIKe6Km2wrtU9jlNdAknghNMXtFQKaBHV8HfQdautfT+ENgCOhstnwA0nvshoEpR3X6REBuhs0v6nqKrILvdHnFaaAmkDoXsIRZftNxniBppCASprRCR0Np0LNhIpAOgedRXclSseBoWBY3VY1owHKHrIosGPPcOSuOxfFRT7bDY93i2rTbEqaDpmqLmr9ui1owHdhyp11JfZVMsGON628n+apBuHLri4mzzdnh6lglzVz+DU0SB5+pzF0lzautq0sYqGHymhPn99XGokray56wtzoWsQuJFrft7Tc43gwaADoApFFOvx3/e+1v+0TZ4No3BR/paCgYcDZHBLJCAC/TbleIrPhlwceCowGzO+5h+ndZKDSehxvY1Xmxf3Y8i+Iks8R4HFRUi5bb+TGKZoudDBiRm3+G6396y/L6KZv8G30O9wKZ2k/5Q5ZaLoFdNKx29ZC1rI5usihsqS8bo0C8l1XG8LrlodwP0ueSk0GYzWYJGwEXsQAnsejymU9Aet1rtAISQ2qySBfJcNiEGMih+A3RT6UVOhpKg54NE4F0CGA9dKRkqVhwNCyFm6oOvltmB4xuK+V4HpUdz6I4ucFJX/ouLapF6+20Ri1v1ZwNrdGgXa3L65987TMox/gne+gSyxS4FyCM1oVfDwld22roJ7+GPceCngPhBZSlGgi49fhXweb6vHMlCOvzWcJHwDmZVoO9O0UouLwMT+O77rtRzIDv1DExokRc6GgoEXw2TQRCQ8CCo2EJ3FR14N06O3DdfvQ6C3hGdjyL5CSPQ/animzRcltwLpxeczQgquF2y7L6K5vcgz6nMy58mTVnRLeuV+9n15oTjQIlREA2gz01lP4R0CQJmbCadwi4iQTdXUCfeR4EMYrMOxtmIbCMhu236crJ9Q0837hcLp2cEl3PKuA/OhoKAJlNEIEkCGAbqwjeyGgu0OygoSCswzJdLDgaFseNVQfdbjfdtLi5l6aL03Ipp74mEHJhdROV076tVtuigcM7ORrGfBpFk9mSMARp2nd/OTYEbcLTwYVb631y+/B0q6pGghxCbuy7C8fmqqJQTb1lD9h8HOg10BzVxKCqWsvEsLk6GdfsioBOjLntMFfq+n3h/9HRUDjkbJAI9I4Atq2KdgM9CWoF4UGwC2FJeQSvdbQJyGKx4GhYFJDpA/R22QIk74HnjdnyLIobd57ojDSmcQcgkuHzmrMBO1Bs2vl3fs4KAbkT18y3IHWSsphCQF9E5ZnYPurIZgkCAc0j5MY/jFWaII6lOghoEkCXG0eTgy5XHb2pKex9P+g2EK55t/3tWThq1NoOBtCho8GAESgCEVAEZgZh7axzLCBnXfQs6F7QdSB9wb0f9AIIWWXdOfpdnH0Wn2wUC46GRQBPDjN18iL4qj08LJr4zWGyvofC5yJya9R8Sc3RgM9Y58qSPQLtW82ekD1vckyPgMyL+4Juk/cYqNva3vTcyaEsBOTo+H6PvDwaPs1SHQRcctAPYXc8Q+pyGpZqICAzwt54oZfPQd+B3gRtaER3OhqMGIJiEIGXAAEe+qJdQFP0Acc0+O1gEEKlIswYmioWHA0L4warjoaMPbnyMHh6mufADUKKyaGmekuJwoyJWtarORoQ3fDtG1HUUqI4ATctd6Df4b4gvwhYSY9VcyHXem84ymMlKPoECMiZsKna9awJfuIXgSMgM8Huz4N0RvugwJWlel0QkHlg86W7fFX+P3Q0lG8DSkAEXB4GPBQ4J0O9cOjaZ61jKarBgqNhQcCiD1g71QtkfefJ7eCJ91Ffi8s8fKWv0mct96goGgwHw/9qzgZ1PGTdBvkpAvrQ467Hk4iHVQTkVthI13cvb1VCytUoAi4R3MXxtafPCiyVQkAmh+3VyavPQheCNOcXCxEoAwE6GspAnW0SgW4IbIz/MSBEc3b7vq9/V43rLNHXSQX/ZsHRsEA8uO6cre5u+yjkDfS1uIgMzHKw1BDQJRM1RwM+I8yYJR8E3NpRDeecNh/+5JoOAUGUnHwMegfE7RHTgWmotluvfT1sqi+bBxgSjKIUgoCz/7mx/bHskzsAFQI7G+mOAB0N3RHh/0SgBAQ0cY86GrZuoO1jcK4un4Dn2kyx4GiYLx5Yd80WFdEBe2y2PIvkJueNl59rsWuoIwnkJjVHA6IbvsK6GM761MDJ9ChLxtfkqZmyJbMMERA4rl2oteYEYgkGAZf0U5OyqrMh4zExGJACV0T2he3xrCivguYIXFmqZw8BOhrs2YQSVRABZIqNXgEhQ7vbdaKvWSVd63wgSJ0MCI0zVSw4GubFYKoPVbtli4ycEPMdnC3forjJ3rH8wIdFEfgoiiaBg+GHmrNhTDRwTSKTFwJyC/rf96Bf5tUC+aZFQE6M7xGaJ4glGARkEtj1YZC+bG4RjFpUpAEEZB3YHs9nLlngig1U5KlEIC0C/wcGSixEgAiUjMBcaF+3rtTIhjEg3WFiJEhnmG4A3QfS0Pc2kJ7zMkidDpaKBUfD3IBHHQ3YVzrLIgfHfD19UZJVYvk3yRIV33nB0XBDzdHQGrX8zXd97Movi8f973S7MlZdMl3HLY+D1CGEyDCWcBCQKWHTZ0DYNltfOlmqh4DojlzvgxCZKdtUT39qXBICdDSUBDybJQI9IdCEL/UFGQ97kYbpq0OhRhrBMBo0ErQRaCKQtWLB0QCHjXM07JktOO3Z2ZHV18fi1mGrA2aEj9LnJTOWT2zcydHw97zaIV9FQG4C/QhCVnQWmwjI7LDPN6AXQRPblJFSJUPAjQGvwK56Da6WjAdr+Y2ATA/bY5Wge0bSKE195mQhAnkiQEdDnuiSNxFIgQBml6JpQXOC9MF8IMh6seBowBpEN4j+Pluw5Hcx32Wy5VskN4GjSjQ6hqUDgabWaNDecDic9G0UDe34mp+yR0A0UauGbyMLOotdBGTz+F5HO9k1UkLJ3FbHb8K+P4B+nZAJq3mNgFtKc218jf8Lx8m8VofCW0eAjgbrFqJ8RMAjBCw4GnRGTmfukZMgyyK/jfmuniXXYnnJ3dDh9WLbZGtEoDMCcgn64DjQvJ2/5WdrCMjf4vvdVtYkozxpEZCZYdu3QbpEZqW03FjfRwTc9qdHxdc4luTKLD5qQZm9QICOBi/MRCGJgB8IWHA0zBoPnn/IFjLBziDOgbFZtnyL5CanQQedUUZyMBYiUAYCMgz9DzloGFlTBvr1tymDYaPnQLinC/LesISFgLsO343tu3xYulGb+hFw0Usa3fIJiP2gfuB4Zv0I0NFQP1ZdzrS4Pr6LgPyHCFQUAUQzuJL12sOvY75IquVteRGS670LIewsRKAMBJrwchNhtlzzzMjSZUjANutBoEkTEtecqkhIrI4HlnAQcNfhKtBHx7U7YN9lw9GNmtSPQNM1OFejWtpA96Ef7Fx/XZ5JBIgAESACRKBYBCxENGhYqC6d2C9b1d3aVuX7p2z5FslNloyx2bHIVtkWEeiKgGC3HEFKDLm36/f8zx4CsmV8zzjPnmyUKD0CghxQ8iEIDgdZKj0/cvATAZck8rH4Wv8rjj7kBPMT6upJzYiG6tmcGhOB3BCw4GiYKR4s989WS5k05ntstnyL5KZZ5N3SidOLbJVtEYEJEZAj4uvJ45wnE2oV5jdyQWyrLcLUr+paCXZSko9BX4EWrzoa1dVfWmD/S0A6oXIPaOrqYkHNM0SAjoYMwSQrIlB1BCw4GmaIB8ocIg8E4YVytt9GltfGP0T4rQWl9x0BzXTu1gU/jWPWy5x8B8eY/M5B+QLs9C0IM+As4SEg88XX4xc4LhqeftSofgQ0GtQl7H0LRy6zrB84ntkzAnQ09IwLvyUCRCABAhYcDbpPtHrkD0wgfz9V5HPwvaKfk4z/LNdDByR+YiECZSMg+8TXKrZTZLGNgO4SIt+BngFh5pMlPARkQdj2s5gWCk8/alQ/AoJIMxfhos7F9eqvxzOJwAQI0NEwAST8gggQgaQIWHA0/BIDozoaDk6qRO/1RD38t/b+uw+/yJExPtP6IC1lDBkBaUZfHAV6HcQ1weZNLdvE945zzYtKARMiIIvAxhrV8Clo/oRMWC0IBGQu9IFXQD+D/hyESlSiDAToaCgDdbZJBAJFwIKjAS/QztFwSPYYi4Z5P5Q93yI5ykYxPqsW2apHbTW1RS0HtEaDzkBnnsYjuT0Vtf3ldQ9PFaiY2HJRfP/YumKKV0hdWQw2/hI0GsTQ+QpZfkJVZQr0gdtBOnlzHWjSCc/hN0SgTwToaOgTHv5IBIhAIwhYcDRoRnsdFHPwwGuWfHmxEUDsnStzxPjsb0+28iUaGzVvAEcDknG0SGvUfHn5EoUugWC7VXkWhOU8Mnno2vqvn25z6ZZP/IAjZr9ZwkRAloB91dmgkQ0Lh6kjtaoPAXePPgH9QJ+r8PzDPC314cazYgToaGBXIAJEIDME/gdOSiUWzZTsBsTDsxdCbgDv97PnWyRHTbwnsJFcWmSrvrQ1Jhq4Zs3R0BY1f4+nbCQtZMkXAUF0jbtmj823HXLPBgGZFfbS8Po3QVNlw5Nc7CHgIhs0L5HaenF78lGiYhGQTdAPNE+L7k6ydrFtszWPEaCjwWPjUXQiYA0BC46GIRgE1fOO7fOyLnIh+JbsSMlCJ3kEejyXBafQeDwVRYPgYPiy5mxAhMNWoeloUx+5A33ye9CMNuWjVF0RkDVhq59At4G4a0hXcAL6zyWI1GgjfblcJiDFqEoiBHQpjcupo3kbRoB47SfCsVKV6GiolLmpLBHIFwELjoYpMfipo2FE9qrKqTFvzxPXyd+gx1jQoOwx8p9ja9Ryfs3RAKcDXqRY8kdAFkJ/1BfXf+TfFlvIBgE5DPbSe+2R2fAjF5sIuK0vP4KddQeC5W3KSKmKQ0D0GetmkF77mr+BUU3Fge9jS3Q0+Gg1ykwEjCJgwdGgyYt0ADwqe4zaH6x/kT3vIjnK7jFGXHvbA+w/RgNX6eRoaMXT9dAeTuNXmSPgIobU2cB+mTm2eTB0y7Bugb3UZmvl0QJ5WkHA7UCAZYMudH64FakoR1kIuGtfHY167esSKt6zyzKF/XbpaLBvI0pIBLxBwIKjAWvqnaPhmOxRk9/HvOfMnneRHOVXsR7bFNmqL22NiKKJ4Gj4sOZswA4Uu/kiu99yygzol7p84k6/9aiS9G528w3Y7EvQ7FXSvHq6ymyw8TsgTQS6WvX0p8YTIuCWUGkOD71vbznh7/yGCER0NLATEAEikBkCFhwN2H7JORpySCwnW8e8l8wMsVIYiTpjdI3lKaU070GjcC6c1uFoaLnfA5EDEVGOjq+x1QNRqAJquGUv+qLxLGjiCihcYRVlFthYZ7B/BDGKpcI9oUN154DSa18jSc8Aeb60tEMzfsoEAToaMoGRTIgAEVAELDga8KDrBrzjszeJZlp2vFfNnnfRHEVnIe8qulVf2oOjYamaowF5Gn7GFN6Mvsjut5zOCTYaffN5ELa+ZPEDAZ3NdPfGS/2Ql1ImR0ATtsprIM3zs35yPqwZDgLqYJTLQOpseBA0XTi6UZOUCNDRkBJAVicCRKADAQuOhsHxYId9n7MumgjLDaSbZs25eH5uq85Pim/XnxaRFPKNDmdDy/7+SO67pIKlKu4628l3Taolv5wZ223PauldRW31RVJeArWCNqkiAtS5JwTc8lLtEx+CluvpDH5XOQToaKicyakwEcgPAQuOhmYMcOpVPyl7Nd3WTsp7l+x5F81Rt/90OHHmoRfosbXlMTVHQ2vU/FQvp/HrzBGQAeibL4I+Bk2eOXsyzAkB3cVGHgLpiwZfMnJC2Q5bmQZ21sijcSAsK2QhAoqAm5DRXUr0PrAvMak8AnQ0VL4LEAAikB0CFhwNWB/oXqBzyD/gktWpo+HA7CAri5OsF+O0VlkSWG8Xjob5ao4GPeL/ea3LHI58guVJ7jo+ORydqqCJTA+76WymOom43Ch4k8tQ2PlJkOb82St4dalgnQi4iJf70Sf0eekGELbEZKkoAnQ0VNTwVJsI5IGABUeDzobq4HZq9grKJDHvHPI/ZC9t3xxl5liXQ/o+r9q/IpLh6ZqzAY6GY6uNRtHay43oo7oOfO6iW2Z7aRCQZWCzMaAnQIPTcGJdHxDQqCO5H6Tj7mE+SEwZi0DARabhWck5oTSB6GJFtMo2zCFAR4M5k1AgIuAvAhYcDU3xA89p+cDoXnz+lg/vornK58Dq2qJb9ak95Gn4Y83RwOUTRVvOZTPX7Pa3F90y20uLgOwQ34f/mZYT6/uAgDqU5NbY5n/BEeMwCxFQBHR3Evesofdy5N9hqRgCdDRUzOBUlwjkiYABR4Oq52ZWsM1SHkVGg//VeXAunqfcA13eKL5df1r8Nop+AUfDaHU2YCeKnPqUP3gUL6kcE1/P2PGFxS8ERF84dZb7YL/kprTJENBtDeWK2OYX4YjoQhYioAi4CMrH4r6hfWRS4lIZBOhoqIypqSgRyB8BK44GXS+KDOh5FHkFvO/Mg3PxPOUU6KJYIfSVpTcEvntnvIoAAEAASURBVI6iIXAyLNHb7/w+TwTccqX30UdfBzXn2RJ5Z42AC52+A3b7CbRu1tzJzyICGskg54DUwXQ9iNesRTOVIpNLFnt63DdexnGBUsRgo0UjQEdD0YizPSIQMAJWHA3jMIidlQ/O8ih4Y+1xCEW2jAf9FULQhjqEioD8Lu6nASRhDdVGvemlSeDkVRCCg/hi0RtK4X0vyGfjnA134cjZ6/AMnEIj2RB9Av57+R60bQpGrOoHAnQ0+GEnSkkEvEDAiqOhDQMYZlXyKLpeXGdXQygyP3TRmaffh6ANdQgZAXkQ/VRfVrkdq3dmlrlgty9Bb4OGeic+BU6IgOwPe+v4oiHzQxIyYbUgEZA50CeeifvHBThOHKSaVEoRoKOB/YAIEIHMELDiaNBM9edmplUXRoLkZvJZl6+8/ceFNv8AfbCeloUIWEZAFkU/1RD8SyxLSdl6Q0BWg+000mwkaFBvZ/H70BCQHWO7v4AjnYShmTeVPtKCPnEeSJ1R2j/mTcWOla0iQEeDVctQLiLgIQJWHA2a3RgDWB5FzgZvREyEUuRx6IOZBRYiYB0B91CqOUWWsS4p5esJAdkHttOXipzuzT21ye/KR0A2hs3V+f8maNby5aEEthCQLdAvNFrtOxCXUtgyThbS0NGQBYrkQQSIgEPAiqNBZ+n/no9N2rPgB5JA0b286UMgZxnz6TDkmhkC8gv0Uw3BR44Ubp+XGayFMhINk1Znw16FNsvGSkZAVofN9UXyQ9BCJQvD5s0hIHOiX9SWUlyMz8zrYc5GiQWioyExdKxIBIhAdwSsOBr0gQYPtHkU2Q+89UF5ljy4F89T97V2+iA0nYUIWEdA9o776y7WJaV8PSHgMs8/ABvqMoo1ejqD34WKgPwKNv8CpIkAh4eqJfVKioBbSnE2+oY+X70K4jNJUiht1aOjwZY9KA0R8BoBK44GDcPLKe+AbB8PhIEMgrJkrA/W0rIQAesIuLwiz6LPfg4aal1aytcTAmo3eQP0DWiBns7gd6EioOvw5R3QGNBmoWpJvdIgIBugb6hDSvsIlluxeI4AHQ2eG7DK4uv+zHhJirBvc3QA6A/xZxxYSkLAiqNBZ0wQfpdHkfXBWz3uq+TBvXiebhahFfrktEtH8RoV0SLW5syENyUks2IpHgE3M6q5Gs4vvm22mA0CMg/sp8tgRoGmzYYnufiBgMwAmz8H0mtYn9tYiEA3BGRm9A3daUiftW4B0ancDSGP/qWjwSNjUdSuCJyAf48CbQK6F6Rr8ncAsZSHgBVHw1cYmC7JBwZZMR78Ns6Hfxlc3UPfY2W07GObbVHzn9uiFmmNWkaho03low7+yyz/wHWoLypL+69LVTWQX8N+6uTEvUcGVxWFauotU8DmeG5zL5In46gTRixEoBMCLnptBPqGLrP6ADS804/86A8CdDT4YytK2g2Be/D/ANCVoLVAmmDoNBBLeQhYcTRo2N1l+cAgC4K3etl3zod/GVx1mYlgkl4Hdpb+EGiNmh9XR0NMyNnBUjwCMg36q86IPwWaqPj22WI2CMhOsJ/eT68D8WUzG1A94SKISpWrYvtfgSMTEntiuWLFlJXQN94H/QQ6FjSw2PbZWkoE6GhICSCrl4fAxWj6QNA7IJ1V1P83A7GUh4AVR8NnGIzw4JJHcWGf+mCsfS+Qohng3cM+nCgs/SEwNmo+tuZogNPhpf7O5+95ISB7xP0W/ZfFXwTkuNiOp/irAyVPhoA6l+Qvsf3vxnHyZHxYK2wEZGr0jZvifqJbcs8Rtr5BaUdHQ1DmrJYyQ6DuIaClQfpZs5BzRhYglFisOBo+xUD0z3xwkInjwe6EfPiXwdWteVfnyXZltO5bm8hQNTuWT/zc4WwYtKxvOoQhr0YyuK0uNSfL9GHoVEUt3MsmIhOds3PPKiJAnd1uTroU6hnQdMSDCPSMgNsl63v0EU34vW3P5/BbYwjQ0WDMIBSHCPiMgBVHw2gMQgjJzKvIj+CvOUECKbo+2q2VZkLIOi0KJ8N9HY6Glpy2Uq1TmEqfJouh7+oa3qsrDYP3yruktA/Gtlzbe3WoQAIEZAvYfyxoFGjuBAxYpRIIuJ1LnkUf0ckRdVBOWQm1/VWSjgZ/bVcpyXXG8LVe6AV8j5C7aBsQS7kIWHE0fITB55r8oBDlf21+/MvgLE9DpyfLaNnHNrF8YquaowHRDf+DZ2tSH/UIQ2ZBbh730LlmGPpUVQsXHv0qbPkdaPGqolBtvV2CUN32VLevXa7aWFD73hFw+T10yY1Gwahjavnez+UvJSNAR0PJBmDz9SGA9VnRI6CXQRqyrrtLXA5CyGx0NuhoEAYn7joBDMosVhwNH2LgydERIFiXL+rcCqjI36CTzia1BKRUbqrgyWYwHAxf1ZwNrdGgHXNrjIz7QUAmQ7/VZGFvgbC0icVfBGR22FCXvqkzdxZ/9aDkyRGQRWB7HcM1cnDj5HxYM3wEZHX0kY9BGtU2AsTl0/aMTkeDPZtQoh4QWArfIcN41D1RkD7cPxqfrwPSvfFnHspBwIqjQV86rs8PAnkI/BEBEFIROO/crPAyIWmVpy7Y3vLsDkdDsw6mLKUhIBvE/fek0kRgwxkhIIhgdLvgqENX8y+xVA4BmRm2fxGkM9Z/qpz6VLgBBNwORLein+hSisdAczZQmafmjwAdDfljzBYyQGBL8LirBz7z4DskhXFlDvx9N/7MQzkIWHE0oB/IjflBIDeDPya1QyoyH3TSgfoPIWmVpy5YPrFwzdGgR0Q1YCaOpTwE5Ab03zbQYuXJwJazQUDWhx11llKduoOz4UkufiEgU8D2d4J0XDofxG0N/TJgwdK6XYi+Rz/Bc6jsUnDjbK53BOho6B0b/mIIgZkgy0+gNUETxXLpTMdloAfi//+I45XxZx7KQcCKowFOAPlXfhDIP8C/5uDKr5lCObvM75q9/6pCm/W8MWxv+VjN2YAIh3M9V8dz8XXnCfkKhGgjhtB6bkyI7zLM60smnMa6wwhL9RBQ54KcB9J+gOWKTPxXvT7QiMaCyUfNNeX6y204TtdIbZ6bCwJ0NOQCK5nmgcC+YIoZjugzkIatq+PhYxBmYqNVQZjJco4IHFhKQsCKo+FtDDC35IeBnBoPZIHNsAiihkKL1MivFyhnRDFsX3M0IGfDN6OZFDJfwPvlLjvH1+aB/Z7KEzxAQI6O7XmOB8JSxNwQkP3RD/DM5/IjzZpbM2QcAALOOTUCfUWj2/C+wDwfJRuVjoaSDcDmG0NAl0rsCDoKtA1oEpAWzGRFw9wn/ikTASuOhjcxuNyaHxByKPjrDMu0+bVRBuf2h/pfltG6j20iGcjE3ZJC4kWXpTwEXGTOSFybP4B0OR2L9wjIRfH99hDvVaECKRBweVg0NP4TEPJ4sBCBvhCQpdBPXo3vHYh+ZjRMX2jl+BsdDTmCS9b5ITAVWA/Ijz05J0TAiqPhdQwqtyfUoY5qsns8eM1bx8kenSK/jfVazyOhSxcVSyb+WotqwOcHSxeo8gLIXOjHmrH+3spDEQQAboYS93Pn3N0hCJWoREIEZAn0A92RRK/vzRIyYbXKIKC7EMlZIE0qqrm7fl0Z1e0oSkeDHVtQkgYQeAznLtDA+Ty1GASsOBrUi31HfirrA4576F0uvzbK4CxDY72OL6N1X9tEUsj5ENUwbnxCyJb7fdUjLLnl4Lgv7xSWXlXVRiaFPfHA6hJE0hFa1W7g9HY7UjyPvqAvj4xyqXRfqFd5WQ19BQGIrs+cjuPgemvyvNQI0NGQGkIyKAMBOhrKQL3/Nq04Gl7BQHJn/+ImPUOQE8Q5GtZJysFuPXkDut1nVz6bko2JWtaCw+FYxOvPbFPCqknlZsGfRV/+EjRd1bQPU1/nCH0Z9tTZ7BXD1JFa1YeATI4+8G+QLmG8BNRcXz2eVV0EBJHQ8s+4z+h9ZInqYlGo5nQ0FAo3G8sKAToaskIyWz5WHA0vYRC5K1vVOnPT7fPcA852nb8N47NcDt1gR2Z5D8OeVdbChVnrFonXVRmFsHR3s9nvwaa6u8jCYelGbRpDQHeWkb+C1NnwEOgXjdXn2dVEwEWkfoH+0go6HBRYUm9zVqWjwZxJKFA9CNDRUA9KxZ9jxdHwIgaPe/JTX4aBvz7c6JaqgRXZK9ZtocAUozqVREBOifvz+pVUP0ilBTtNyecgXas/W5AqUqkGEJA90A90hwHdbWqBBiry1Moi4LZCxvJa9xz3JI64p7DkhAAdDTkBS7b5IkBHQ774JuVuxdGg6zdzDP93YZvqaDgmKVB267lZYNVtV7syUjIiUC8Cgp2J5C3QhyBNIswSBAKyDOz5HUh3GOLSmCBsmkYJt5xRo1y+Ba2bhhPrVgkBfc5xEZxjcDwQhCgZlowRoKMhY0DJrhgE6GgoBudGW7HiaNC12fc3Knxj57uwu3Mbq+PD2W5t+/fA7xIfpKWMRKB/BGQV9GdNHHdJ/+fyDH8QcAnexsKuGsE2tT9yU9J8EJC50Q9eBf0EYpLIfEAOkKvMiv5yL0gnWB4HMbohWyvT0ZAtnuRWEAIaBjukoLbYTP0IWHE0PI3BIudtBuVjtHFN/dD4dKY6aeR1nySmrESgbwTkHPRpfZDkjgV9A+XZr7IRbKp5OPQFYTLPhKe4mSMgU6If1JJEXoXPE2feBBkGiIA0oa/sAcIzrGh0w8EgRjdkY2k6GrLBkVyIABEAAlYcDU9hkHg4X4vIf9FGjnkg8pW+b+5yHHTTlzIm1+obKP7qDQJue0QNsx8NGuqN2BS0DgRkO9hUI1awXI7b1tUBWOCnaCLj9twsOukwU+AKU73MEHDRDXiuc88/T+A4f2asq8uIjobq2t4rzZeFtK/1Qi/g+7tB24BYykXAiqNBB4hH8oVCHkAbWKIRYpG144GWs78hmreyOskK6NcaVh1oJFJlDQvFZe/4nnULjswiX+Wu0K67bI2+oFuhqnNxufav+YEI9ImAi27YHX2mFt2AZTiMbugTsr5/pKOhb3z4qxEEdP2lvjhi79voBNAOoMtBX4POBh0N+gak37OUh4AVRwNubKJ5PHIsciPawDZrIRZd7+xmCE8MUbuCdUJIJosdBNpnOrewIxMlyQYBOQz3LY3EuhKEWW0WIiBLoy9oItixoJ2IBxGoHwG3u1gtukF3plio/ro8sxMCdDR0AoMf7SKwFET7EjR5NxF3xP+Pxt9tjCMSurCUiIAVRwP6hK7ZzbPIBWgDSRNDLfIK9HswVO3y1gse0CGtUfNzbVHz/8ZGzZpThsUEAtKCfv0S6AvQdCZEohAZIiCnwq7qbPh7hkzJymsE3FaGOvmg/UJztQzyWh0KXyACLrphN/SZb0GtoCPZfxqGn46GhiFjhTIQ2BKN3tVDw/PgO9wAXJkDf9+NP/NQDgJWHA3IzyDwQOdZ5CS0oQ8ug/NspTzechF0+wHEh7IERoBzYeO2qAWbu7fg6aTl7RFRxBnWBDjmU0WWRL9uA92aD39yLRcBdTK4e/MZ5crB1u0g4ByMOqbpmP0Q6Jd2ZKMk9hGQmdFnaklGX8BnjCEsdSJAR0OdQPG0chGYCc3/BFoTVHtg110nLgM9ANLyRxBCJllKRMCKowEz8fJ0vji4PZf1oWWGfNspi7uGmbqHMoSesjSKADw0MyKaoa3mbGBUQ6MI5n2+HBX37x3zbon8i0bAzUJeGtv3hKJbZ3uWEZC90C9aQe+DNFKWhQg0gIBsi36j0XDjQDrZFOhEUwOQ9H8qHQ39Y8QzjCCwL+TAxR19BtKXSHU8fAyaD7QqCDNUzhGBA0tJCFhxNMD5JM/ki4HgBcW9iC+cbztlcde9pJ1+et2xJEAASyeuqTkacERGfBY7CGikjt4jBLl9ZBY7clGSbBDQ5G1yLUidwYdnw5NcwkBAVkSf+AQ0BrR9GDpRi+IQ0GgYuR6k95bXQCsU17aXLdHR4KXZqiu0LpXQGSjMRrldJiaJoZgex2HxZx7KQ8CKo2Ekbv7P5QuDrBMPNKvl205Z3N2sIPKi6MM6SxIE2qKBy3VyNGAabdAiSfiwTl4IyILo3/qycS+oKa9WyLcsBJwz6RbYVl8IDixLCrZrEQHd8lJ0dyrtG0goziWCFq1kWybZBP1mNOhnkOb+mMK2vKVJR0dDadCz4SQIHIpKv0tSkXUKQcCKo0FfHF7MV2O3zlsfUrbOt50yuesadvmoTAl8bxtRDU/WnA34jHBuFlsIyJ/Qx/U63t+WXJQmGwTc2vw7aONs0AyLi4a9yz/ivqEJpGcMSz9qkz8CbocujOtuDNHlOHvk36Z3LdDR4J3Jqi3wxlD/R1AtkqHaaNjT/n8QSankInfjhv/ffIVwyYH0BeWAfNspk7scFA+gc5Qphc9tIzfDFjVHA3I2tH4fRYHm9PDVSroNoowEaWRDoMugfLVNVnK7F8q74nsZl4JlBWswfGQ39I2xIF1OsXIwalGRAhEQRLa66AZ9JrwdhKVbLDECdDSwK3iFAEJdI1zI0ZxeSV0dYa04Gu5EN3k5X9ilGW3ooHJKvu2UyV2Wi3XcvkwpfG57ZBQNxK4T79ScDXA8nOizPmHK7pyGX6GvvwhqCVPHqmslE8O298T3s32qjgb1746AIOmxvAfS3Wj27/4r/ycC/SMgU6HvXAF6BsTJ0A7A6GjowIKfPEDgMMioCSAHeiBrFUW04mjQUNlX8zeA6MvJZfm3U1YLzpmCCCK5sCwJQmgXjoY/1hwNiGr46tMomiwEvcLSQbAkzzkOTw9LL2rTgYBzNiApq7Mzdh9gIQKdEZBp0Ddqzqgb8XnKzr/yMxEgAokQoKMhEWysVBYCr6DhM8tqnO32i4AVR4OGrr3er7SpTxD0R0H0RMhFHoCOr4WsYd66YZucyeFg+LrmbIDjgTOqeYOeiL/8E31dE3vpLkYsQSKgM41yf2zn3YNUkUqlQMDtVnJc3D/ewHGRFMxYlQj0g4AshD52Fuhg0LT9nOzrz3Q0+Gq5CsqtWcER1hYh0yuLUQSsOBpuxU37zfwxcg+sz+ffTpktiD506RKRUAfBQsDFkolTOjka3r42iriGsxDkG2lEZzDlXdCHoKGN1OS5PiEgk8K+D4LUqbSrT5JT1qIQkN+ib3wB+gG0Q1Gtsp3QEFCntSzVs1ayLX7TiFHNH/MICMGOmmQ8uEJHQ3AmDVuhx6HeqWGr6LV2VhwNN+OG/Xb+SMo1aGd0/u2U2YKsCR3V0bBRmVL43jaeVmdCVENbzdkAx8OmvusUpvyyEvr6OBC2RWQJFwHB8iV5CKTOht3D1ZOaJUdAhqFvPAHS8e8i0ODkvFizmgjI5eg3P4H+Dpq6KwYadavRDLWiS1Tlqdp/AR3/D7oosRABLxCYC1LuAkIiPhaDCFhxNNyEG/Y7+eMjf0U7+lKCzPWhFt0b2g2Up4eqYVF6YXvLK2qOBhwxi8FiEwE5Cn1eXy5+b1M+SpUNAs7ZUItsYM6GbEANjIvLU3RufD94Dsc5A1OQ6uSOgGyBfqNRcrqrCZ6nakUew/+dIqpkZ/yvTonQnifpaKiZnEfvEFBng4YZ6ZIK3WLwD/FnHFhKQsCKo0ETOb2XPwZyKNrRF5LAlxXIs9DxyfzxDLuF1mjQIohqGDfe2dB8fdja+qydW6f9MPq8hrViDS1LuAi4ZRQjYWe9jzN3SriGTqmZbIn+8R3oa9CGKZmxeuUQkMnRbzYer7ashc9/A+GZSj4ALQeaD/Q0qFNEg8yG/w8DIZq0ezSEVwDS0eCVuShsZwROwD+YeXI5G+7FEaFJ0Q4glvIQsOJowEucvJ8/DLIT2tEH1IXzb6vMFlzkhm77NVmZUoTQdls0cLjuQvFVFGErLBa7CLiwaZhJXgJNYldOSpYeAZcgEs8Q7l6+X3p+5BAmAjI/+sjLcT85FcdBYepJrfJDQDZHv9Eo2JtA+4M0cbk+Q34O0giHOca3Ldq/WkH/At0J0oiIefOTK1fOdDTkCi+Z54nAPWA+AHQlCB7CSGeeTgOxlIeAFUfDteNvzHkDIWujHR0kVs+7pXL5C/IJOD3XKFcOtk4EikSgvd//o8hW2VYZCLitL++K73OYRWQhAj0h4Jbb4JnTjYf6YjhrT2fxOyLQMwIuL8OlHb+56LmR6Ed/7PTdCPyvToYNOn2nS7xu7fjfq090NHhlLgrbGYGL8c+BoHdAOjuo/28GYikPASuOhqtxU/44fxhkCbSjjoZt82+rzBZ0aYjT8/gypWDbRKB4BOScuO9vU3zbbLFYBKQFtsbDPO91xeLuY2u6tt4trdKop0181IAyl4GA28UEORs6F7cEF5EyWmRpkEY8HDj+/9pft3SngGfaWnuZHuloyBROMisSgSFo7BAQLsxIP2uSSI1wYCkPASuOBp1xQOKdvIvMiHbU0dBtUMi73TL4u5DRR8pomW0SgfIQcC+fz+Aa1/XZWEfLEjYCGg4vGhGn9/UzwtaV2qVDQPO3tC+l0DX3g9PxY+3wEZD70E8u6NBTmvC/Ojf/Pf47QaS2aH6gbgkh5Vh890RHPa8+0dHglbkoLBGwjYAVR8MVuCl/lj9UMhDt/AyqwJIdl7xIw/kmyR9XtkAELCGgmeblG9CLoIktSUZZ8kDAhTNfBlurs0G3pev20J9Hm+TpJwIuv8eFcV95AUc6I/00ZEFSy6LoI5+CrgLtDboNpM+QmvBxKZDec1bqKozrY6Px/dEd37ulXnviO42smafje5Of6GgwaRYK1R2BZfHFa70Qbu7R3SCGtgKEkosVR4M+JH5RDBa1QaOY1sprxW3RpIPgb8qTgS0TgbIQkM3Q97X/X1SWBGy3SATcTKM6GdTmOp4wWrJI+L1ry42P6oz8HrSTd+JT4AIRcImGz0Y/Ucf1A6ANxzcuf8HnxyYURA7C9xpRNzQ+77f4jIhdeQ6kk2ra7zSi22qho8GqZShXFwSmxn8atv0y6ASQ7i5xOehrEC7YSD19uNi46wQwKLNYcTRcghsv1k4WUURnMUYW0VK5bcj00FMfuo8pVw62TgTKQkDOja+BrcuSgO0WjYCcHtv8OhyxrIKFCPSGgO4Y4LYs1HFSl29O0duZ/J4ITIiA3IA+A2dD5+K2vfwB38fPXS4v2I/4H8svNKJWi4uM0GjTycf/b+4vHQ3mTEKBekIAIUXRl6DuF9KO+O7RuMLGOGKLKpYSEbDiaPgHbrrqeCqguK2HXimgIQNNCKKK5EEDglAEIlACAi5fw7O4BnR2ad4SBGCTpSAgx8He+vJ4B4hLZ0qxgS+NuhwfeFl04fBv4rikL5JTzrIRcImHMZna7kBABIOLWtDIhWZQC0h/R9RD7RyVWXODyE+gFcrWoJf26WjoBRh+bQuBLSHOXT2INA+++zb+Ht7k6N0ezuFXxSFgxdGgayZVlgKKXIq2NLKmAkXOh65jQBjYWLJC4I0oahkTDVwNHXbarHiST14IyFzo/xhzXCQTXzrzgtkcX03465wND+E4pTnxKJAxBETD25Enym1TuB+OTcYEpDjmEHBjy8foK1eD/gx6G/Q6aKbxospu+KzPX/qu06nIIvhOHaHDxn9pLn8DHQ2drMWPdhHQCw0eu2hN0ESxmENwvAz0QPz/H3FEuBpLiQhYcTToCzHWShZR5KT4Jl+Bl2/ZKtZ1eBHIVqWNtqj5hraoRUAfw2Ol9zUW0wi05ytB+CpLdRBwD/o6c/g0aJrq6E1NkyEgM6CfjIzHTI2GoSM5GZAVqiUzo58cANKlWjuDEMmgxSWofRPHs8b/3/mv5g1ykQ5wZjkHl7X8DXQ0dDYXP5tGYF9INw6kuwlgoHeOB3j/ovlAq4LaQOqIYCkPASuOhvNww8U6tiKKoF86b/KsRbRWbhvqWXe6HlmuHGG13hq1vBE7GmRs1DwiLO1C1Ub0HqOzSNuGqiH16gkB+R1sjmcNwXK52kxjT+fxOyKgCOiOJXIYSPvMaBCfUdkxEiAgy6Lv6HiDZ7DORebGd9q3tgYtAcJzr7n8DXQ0dDYZP5tHQJdK7Ag6EaQPeJOAtCBRXTTMfeKfMhGw4mg4Bzfb1mKAaJ/d/FUx7ZXdirwBbBE+zJIVAohoOKzmaMDnr7+Koqmy4k0+eSHg1ss+hWvhB9BiebVCvhYRkHVgc32gfw80r0UJKZM1BATPBy4UXl8WMSvNXB/WLGRbHtkcfQb3m87FRTk8iu//C8K7kNn8DXQ0dDYbP3uDwGOQdAFvpK2OoFYcDTqQjysGdt3z2HmaNy6mvbJbcQmL1IPePTFr2YJ52z6y3E4JB8OXNWcDohqO9laZSgnutinTddjvgIZWSvXKKysrwuZY6SSfg5apPBwEoA4EdBcKl9NJnQ0vg+igrAM1nqIIyMqgnzv6jNt+V5OOjgUtDqozf0MpaNLRUArsbDQtAnQ0pEUwn/pWHA1n4saLm3IRxSXw0QeH3xfRWvltyAbQVfVdr3xZwpGgW1TDN3iDYa4GL8wrv8G1AKem3AMa4IXIFDIjBGRh2PwjkO5CskZGTMkmeARkU/QX+JfdS6ImGZ0oeJWpYEoEnGPhdvSVF0HaZ/4D0qiqTUAYd+RN0FkTNtI5f8OEvxb0DR0NBQHNZrJFgI6GbPHMipsVR8PpuOniZbiIIpOOb0uOL6K18ttwszIa0dDDoFa+dL5K8EUUTQFnwxedohqO9VWX6skt+8f3gFOqp3vVNZbZYHtdToalerJl1dGg/vUiIDOiv6hzUp3294Nmqbcmz6sqAm7r1O3QVy4EaTTD9OORqCd/Q6mY0dFQKvxsPCkCdDQkRS7felYcDXoT1gG8oJkCt93dxflCa4m7PAJsX7MkUQiywNFwaM3RgM/ffhNFU4egVzV0cFuS6T1ns2roSy07ENDdBNxOFBra/IeO7/mJCPSFgJul1q0vx4B0GQ4dVX3Bxd96QaDf/A0De6lY1Nd0NBSFNNvJFAE6GjKFMzNmVhwNmFl0joaCbrD60q2hbFUpcmSM77CqaFyEnljsPzkcDJ/XnA3I1VCRKJki0M27DRfZ9AKuCw2jXyjv1sjfGgKas0buje+LJ+KIreZYiEA9COj9QvTeoY7Kq0BcNlcPbDwnRqC//A2lA0VHQ+kmoABJEKCjIQlq+dex4mg4KR60m/NXWVtwe2XjQaEqxWXQ1oeinauicVF6wtFwcM3RgM//+zaKhhbVNttJi4DMgWtC117retmp0nJjfd8QcDuRXAPb673xctAg3zSgvGUh4PqORmJqVMyHoDXKkoTt+oZAX/kbTOhCR4MJM4QvRG/efX0RHJxA/fVRh17fBMDlXMWKo+EEDNT6sJekbyWASK5EW5iQrkpxyYc01PPaqmhclJ6fRtFkcDR8VnM2IKrhxKLaZjtZICBr4rr4CfRvUEFLt7KQmzyyQcA99Mc5guQu9AFEOrAQgXoRkOHoM++A9PlFt+mubeFeLwOeV0kEesvfYAKM/4MUSixEIBcEVgbXV0BjQQ+CFgV1Lsfgn3peVnT7qL+Blu1cmZ/NIWDF0XBcPFAXNEjLqWhPZyIKiqCwYHe5Efp+AeLLVMbmQCTDgTVHAz5/h4tqmoybILtcEZBDcV3oi4KObyyVRMAlCNUx4RnQdJWEgEonRMAlXL44voe8jiOfexMiyWomEKCjwYQZwhXiZaiGDKmRJsjCi0k0BvRrUK3U62iYARUuBWH9q3NcHIwjsvayGEPAiqMB/co96E9WDD7tWednKaY9C63IHjHGS1mQJiQZRkfRpHA0fFpzNiCqARE6LP4g4Ga11RGnL5ob+CM3Jc0WAdkC9scki4wCzZMtb3ILHwG3lTSC3Nz2udiFiEtxwrd5kBrS0RCkWW0opdEL3TPT4+UkwrLjqPZyUq+joabRpPiwDehuUCvoP6DfgQaDWMpHwIqj4SgMyjqjOEUxkAj6oGtv2WLas9CKW4+uGP/ZgjShyQAnwwE1R0Nr1KLRYCxeIeCSA8LR7nakWcAr0SlshgjIKugD2EDGRX+tmCFjsqoEAm5Hk5vRf3SsfQ60SCXUppIhIUBHQ0jWNKaLRi5gG7wJymH4RtezzwVq1NFQY6bh2jpT9C4IN+Doc9DxIDocAEKJxYqj4Yh4YC4oj4fL+qsPAhuViH0JTbv94x8uoeHgmxyFexkcDf8GfTgmalkveIWDVFAwxslXoLdAQ4NUkUrVgYDbVeA99IExoC3rqMBTiEA3BGR79J2vQZhgk8NBBe2o1U0M/ksEGkeAjobGMWONOhEYgPPUoaARCN0dAGfhu/9n7zzA5CbON64z9p0hdNODwWB6JwZCx/QSIPRAwIGQQKihhdCDCSX0DqGHQEIgEEIJPcT0hN57MdX0+qfY5zL/3zsn3enk3bu9vS2j3e97nncljUYz37zSSpp3it4A14JS5mggmjepuSeDdwEvcH7ehpVYqofE/YAZn83qyEAoQgNilm8BqNHLvVsozm/fOnJfh6zd2ZR7EqiRoFOHIlqWxkC/GHAbxP+Re1k20Rwu/SKtAQ92DP/08zVoOI31AmvAM1z9Irl5uXZuA2rUeBwsU/08LQdjoN8MmNDQbwotgZ4Y2JGdNM5F52YitbB9HuCGWZLQoPGNz4LJ4E5QaLjEpoS/A8zqx0AoQsPh8cN49tpQoZmh/cP/xNrkF0oubuO43JqDxcwYMAYKMuD2if8nfyq42wKbhAHHnEHuX/G1cClLa5VukjNf2WLqs9J+OI56NxwNBlU2fUvNGKgoAyY0VJROS6wQA+rZMH+hHYStDpgwqVfTfA+0UkdDe4ipSiUty2Z1ZCAUoeFQHr6IWG7O2nHhu0hfUbv8QsjJTQvH3wGrQIVwOsyHgBlw58T3pEMCdtJcqzoD/tPANLL459O/Wc5c9SwtgwZkwPduuDW+jp5m+YMGLKQVqTEYMKGhMc5jU5SirSlKme9ChiI0/DZ+AM9dOzrdc+R5d+3yCyUn35VzHGVXLyUzY8AYKMiAr2Cq27O6zjfZXC4FCWnyQHcA1wE9NN1LYHiTk2HFL5sBtwvXj+aBmQiOAzY8q2wu7cAqMWBCQ5WItWQrxwDd0qNTgXo0mIXNQChCw2944KpHw7y1o8sxpMe9WLv8QsnJ7RdzrV5HZsaAMVCUAX0FxwuS37AcUTSa7WgSBtxmXAc8M90nYM0mKbQVs+IMOBpU3I1A7zxq8NC8ZWbGQCgMmNAQyploMj/mobznglfAl4AuYNFiIGtMpOUnjeQGGo3O7rTt4BgIRWhQa5EeuvPVjiENH9Cn7JrN3CIx14c1W8mtvMZA3xlww/i/fATeA9/v+/F2RGMxoM8VOn2RYgLYubHKZqWpLQP6ookXrSaxPAkMrm3+lpsxUJABExoK0mKB1WSACZGiJ4HEA4Hug52fqJyLdZm6f10IkjiKvzQwC5uBUISGpJV9WO3ocsdzuXK9asKvZjP/+b77mq3UVl5joDwG3KrcJ8aDJ8D3ykvDjmocBhzvPe4RoOfHCcCGoTXOya1xSdwcXD/XxdeShuVwrzEzBurKgAkNdaW/OTM/mmJLQLgFaAKbWcEpQGHngNmAPlWp7W+BJs8aCMzCZyAUoYHPTPqXtgVrR5nbK85TX0hpMnP0TvJjRGdqsoLXpbhj+VwwfzReKM3yy4BvfeQZ5/4JBuS3HOZ5ZRjwE+vyuW//3FJFUUNGzYyBMhlw23INfQg0D8hZwATNMpm0w/rNgAkN/abQEugrAxomwRjVKF0pkYL/JHgZMNbMiwy6OBcGZvlhIBShYW8uIb3E1/ArJG6LOM918nO6KuWp+1Fc9m0qlaKlU5gBbpzzTIza3p8YtU5ujwbtUTiWheaDAXdM/L85NR/+mpfVZUA9GTp7xj3Geg3nGKpuySz1ejDgaLRzf43vMW+y5HPUZsZAzRkwoaHmlFuGuuieLUDD5YSpF4PA+LJIn8U0yxcDoQgNVMC80FDD3gVuhTjPUfk6ZZXwVq1vjt5H7vJKpGZpFGdgQtS6FUID3UeE1q++jqI5i8e2PWEz4CuWSUVgn7B9Ne9qx4DjGeKH1rzPcuXa5Ws5NSYDviHgLa4lNb78DdgzozFPdKilMqEh1DPTwH49RdkeKFC+YwmTyHBRgX0WlA8GQhEado8fqovXjjY/zlYP8iadFNH9i7J/BKwbeBUvus/oCYbI8HEiNrRHbRdXMTtLuuoM6HN07h6gLs6bVz07yyAnDLgfcj2MA5rLY+ecOG1uBsuA5o5yZwBNFMljxP0CqCexmTFQbQZMaKg2w5b+VAwUExqOJqaEhpWmOsIC8sJAKELDL7mUVOlfsnbEqYLt2sF5tcszpJxc0otklZC8akRfGDKxVyI0aAgFvRyWacRyNk+Z3CzcN14AjIyxT9M1z3nvraT6Kol7FOhZpnH2NldVb5TZ/l4Y8D0vGabsr6l7WNaw12cvrtnuRmXAhIYyz6y12pVJXAmHfV5CHItiDPTEAK2D3mo4/KZlCjnSAhU167haejR42zRe2qJKDDwYTbzIRe7FjuRbBgyIWk6vUlaWbE0YaNEzT+On9Xlc9QyyuYlqwnvombS8h4drgCvAr8FdXBuzszQzBspkoIW5P6IVwW/j5dNcU0cBelaZGQPGgDHQGAz01qOhhhP4NQahAZUilB4NO/PgVCvQcrXlxjEkyD1e2zxDys2ppYQXF7NqMzA+atu4q1dDG/2r2zardp6WfrUZcMvz/0FscG8APnloZgwkDLj9uCYmAo2119e6zIyBfjLgFuBaugPoXel5IFHLzBioNAPWo6HSjFp6vTIgoWEsOCQD1Ho/dOLkTHgSbzXCzcJmIBShYVT88KzxC5m7hnw/DPsUVdM7xzwr/qVlvmrmYml3MIDQcHsiNrRHrS/TVDXIuMk7A25d/kMTgES7GfNeGvO/kgy4tbkmPgaaeHfHSqZsaTUzA7qW/Kcw6ZXpLgVDmpkNK3vFGTChoeKUWoK9MSChAQW1zzi6t4Rtf90ZCEVo0INTKv0KtWXEnUaeelg3aTdEjS/3vO9VW96bMzfmZliSORomdYkNbbR6muWfAbd9fB+5u3nvJfk/i9UpgZufa+KJ+D7LkClXw+GB1SmRpRoCA36eGCZi9+8vErN2CcEr86EhGDChoczTaLO1lkkch+0AZivj8Ic55pEyjrNDaseAhAbZDB2Lev3qRT3iM07RylHUouumRuYOICNe/qIFyXdsjTINKBs/izWfZItojW3ZOCDHGtYVvjpxPg+jPTsK6D7/NmpfaKYo+qxhC9w0BVNX+ehMcA3gmdmCcGpmDIgBNy0/l4CfAsSo6CdcH5+yNDMG+smAW5UELgBLg3sBz5aWeD4gtsyMgb4zIKFBxvu4mTFgDBgD/WMglB4N2/Eyph4Nq/SvOH092m0b57t6X49snPjuMjgYD77XOGUKtyT84WanV8MXqV4NZ4frrXnWNwbcSfH9RIKDmTGQYcAdyPUxCSBqu2UzO23TGCiTAX3dxDFZpP8KjoZxHQcGl5mYHWYMWI8GuwaMAWOgYgyEIjRsw4NRQkON5/WQsOHz3b5ijOYuIbdlzMGPc+d6Th1GZDg4ERoQHSba5y5zeiKncls9hBxfHPD3lEOn2m0BxkDk5/T4hGvkG2DzNtgVUUEG/DCdm+P7z2ssN6lg4pZU8zBgQkPznGsrqTFQdQZCERqSyu6aVS9xtwzcvPFD+eBuwU214aaHA/VooGeDWS0YYLrwViaDfKVLbGi7qxb5Wh61YMAxwae7Nb6v7F6LHC2PvDHgvx7wZHyNnM+yLW8lMH9DZsBtxTX1Tnx9/ZPl/CF7a74Fx4AJDcGdEnPIGMgvA6EIDbSm+1bAkbWlUhNz+a6s59Q239Byc/+CB00oNSA0zxrVn/HRwA1TQsO7jVrO5iyXm47/kj6dOxkwLMzMGMgyoHkb/BcD1JPvUTAsG8O2jYHyGdBQSD+Uq53lt+AoYMMpyie0mY40oaGZzraV1RioMgOhCA2b8hDUCxefiqu1ubfJ94Za5xpWfm63mP81wvKrsb1BaDiIySHvGx+1bd7YJW3G0rmZ+E/xxSanF/2NmpEBK3MpDLhduD5UEWRCWLdZKUdYHGOgdAbc4lxX9Jjz71ev2zVWOnNNHNOEhiY++VZ0Y6DSDIQiNDCW0D8I1690AXtPz7c8Pt57vEaO4eaEf7W+ntrIpbSyGQO1ZcD/r17hf6Xx+DWef6a2JbXc+sOAW4brQ9fJFHAisE9g9odOO7YAA25rrqu3gBp0bgELFYhkQcaAGDChwa4DY8AYqBgDoQgNtPj5B+CGFStZyQk5PqvpPio5esNGdA/BAy0eZsaAMVA5BvwEbRov/QX4QeXStZQaiwE3A9fHtUAVQT5T6OZurPJZaerPgB/SdSzXluZkEo4HDPMyMwa6MWBCQzc6bMMYMAb6w0AoQgM9GfwLFj0bam3u5DjvwbXOOaz83EExD1YZCuvEmDe5Z8Atxn/rQ6CvDSyZ++JYAarIgNuPa0TDbT4Aa1cxI0u6aRlww7m2bgYStTR0dNumpcIKXogBExoKsWJhxoAxUBYDoQgN68YPPeZqqLW5feO8F651zmHl51te9eJxQlh+mTfGQCMw4LvHf8r/633Q5PeaRjif1SyDW5lrRBXASeBwwGdTzYyBSjPgfsS1pc9g6rl/N1ii0jlYerlkwISGXJ42c9oYCJOBUISGkfHDjq9P1Npc8sWLdWqdc3j5uUc4D4wVNjMGjIHKM+BW5P/1JVAlcv7Kp28pNg4DbgjXyG1AlUCNqWfbzBioNAP6tKo7AnwDJoIzwcyVzsXSyxUDJjTk6nSZs8ZA2AyEIjSsycNNL1Rb1p4ujZv2ee9c+7xDy9EdHHOxbGiemT/GQGMw4FbnP6aXes0AP09jlMlKUR0G1JPBHQk0Ua/EKZtQtDpEW6qRG8r1dQ3Qe9hHYHcwwIhpSgZMaGjK026FNgaqw0AoQoNevvWA27o6xewpVTd7nPdRPcVqjn1ugZiL45qjvFZKY6AeDLj1+J9pMrYXwRz18MDyzBMDbiTXyTigFudDgQ2lyNPpy5WvTo0++iyv3seeAGvkyn1zthIMmNBQCRYtDWPAGPAMhCI0rBI/2Larz3nx3zG/uD55h5are5RzYcMn6nhaGMg/44So9ZSJUetRY6JoYB1dsayrxoBjPho/6d9zLBE7zYyBnhiQIOXuBKoA3gXsqxQ90WX7+sGAPq/q9gCavFbXm76GQiOEWZMwYEJDk5xoK6YxUAsGQhEaVoofaDvUotBT5+FeIn9e4szg4aD4XIwwNurDACLDiROjNpov2xzr1rukPqehBrm6LfivtYNnwGw1yNCyyDUDfijFIfE1o+7tTOZnZgxUiwHN1eBOj6839cA6CcxYrdws3WAYMKEhmFNhjhgD+WcgFKFhBR5gUs53rA+l7g7yfrk+eYeWqx+rOQU+TgnNs2bxh54MhyVCA+vtiA1LNUvZm6+cbiv+a+oS/zQY0nzltxL3nQE/qWjytYAzuW6Y0M/MGKgWA/pKjrsR6B1Nn+nV/A30ejBrUAZMaGjQE2vFMgbqwUAoQsPy8UPsZ/UggbwZNqHhE2YdDLj74UOTj9lY4DpcEgzGnq49ahubiA3tUet/R0eRTcxVh3NRmyzdNvzXJDY8CWatTZ6WS74ZcDNwrVwJVPnTdbNYvstj3ofPgFuX60yCqK45DfnaKHyfzcMyGDChoQzS7BBjwBgozEAoQgNfOfAPr10Ku1ntUD+ztx6ejIM1g4e94/OxmrFRHwbGRwM3TIQGLdujQZwTs8ZlwG3Hf24S0ARsJjY07omucMncKK6Xr4C+ZPKLCiduyRkDGQb0JQr3S6DJSfXOpHlDlslEss18M2BCQ77Pn3lvDATFQChCA13D/UOrTi9K/mVND80Vgzo7dXPGTzymSs+5dXPBMo7oyfDXLrGh9avvomg+o6WRGXDb85/T/06thrM1ckmtbJVkwC3E9fIo0DPsWmBCVSXptbQKMOC+x3V2DJDANRlcAmyC0gJM5TDIhIYcnjRz2RgIlYFQhIYleEjpJWm3+hDlP+mk/OnCbNbBgG+p+ABOpjFG6sMAf87ZmaPhky6xoc0mLK3Pqahhrm5b/nMaRqGuydbDqobM5zsrN4jr5USgSt+7YJ18l8e8zwcDbh6utcvi6+5rlkcDRAizHDNgQkOOT565bgyExkAoQsOiPJxU0d+jPgS5YXH+B9Yn/xBzdbvGnKwXonfN4hMTQf40JTRoCEWdev00C+MhlNNtyX9PX6N4AVgrYQinJDc+uJFcM2+DKeAU0Job183RHDPgNPz1LqD3uPeA3h8G5LhAzey6CQ3NfPat7MZAhRkIRWjQrMZ6QO1V4fKVmJwbSN7qsnxWiQc0QTT/aasJcEKXSLN6MkCvhhu7xIbWL76Nou/X0x/LuxYMuM347+n/9zKw810LyhsmD3/vvprrRs9UTRRJj0EzY6AWDLhNuN6ej689DQHboBa5Wh4VZcCEhorSaYkZA83NQChCw4Lxg2nf+p0O3wp0Q/3yDzFndxPn5TNgrWJ1PD0Mgp0bseHzlNhwSx3dsaxrxoDbmP8eU3O418DQmmVrGTUIA+5nXDeaKBJt0u3TIIWyYgTPgIZbul+BD4DELk0YuVzwbpuDCQMmNCRM2NIYMAb6zUAoQsOw+IG0X79LVHYC/pOOtP6YdTHgdojPy4+7wmytHgwwZGKXLqHBf4Xil/Xww/KsNQNuff6Dqii+CRaqde6WX94ZcAtw3TwIVOG7A1jvmLyf0tz476bnetOEkZq7QUN5/gpoVDILnAETGgI/QeaeMZAnBkIRGmit8y9CdZwjwX+T/PM8nbzq++qm47xwjbjrqp+X5dAbA/RquKVLbGj9P97e5urtGNvfCAy4tfgPqmX6fcAXesyMgb4w4FuYj+Da0bwf6qH2074cbXGNgf4xoHlm3B/BRKBr8BwwZ//StKOryIAJDVUk15I2BpqNgVCEhnl48KjF5eD6nQD3+9iHmevnQ4g5u8vhZTwwXup8euIhFJ1foRgftdG13qw5GHAr8R/8FHwCVmiOMlspK8uAuq+7Z4GetfoM5myVTd9SMwZ6YsB/hvVqrjv1blADht65ZuzpCNtXFwZMaKgL7ZapMdCYDIQiNNAy619+DqkfzZ1fWbCxhN1Ogls3Pje7dQu2jbowMD4auH571PoyvRuuHxtFg+vihGVaJwbc0vwXPwBfgjXq5IRlm2sGXBvXzslgMvgQbJ3r4pjzOWTA/YDr7k4gwetjsD/gujQLhAETGgI5EeaGMdAIDIQiNMweP3QOrx+pbu3YBz4tZ9bFgD5R5b/Lfl9XmK0ZA8ZAfRhwC/N/fBto3gbr0VKfk9AAubpVuH5eBqrsXQWGNEChrAi5YsA3YjwSX4NvsdwVMJGkWZ0ZMKGhzifAsjcGGomBUISGWeOHzVH1I9dPmqWXrgPq50OoObuT4EXdHYeF6qH5ZQw0DwNuPv6LqiS2g582T7mtpJVlwE3L9XM6UO8Gzf9hk/5WlmBLrSQG3DZcey8CvX/pvrYdaCnpUItUDQZMaKgGq5amMdCkDIQiNDD+3z9kjq7feXAD8WESOLt+PoSasyag8+fnyFA9NL+MgeZiwKkX2ONAAuA+zVV2K21lGdAwHP8JVVX0rgCzVDZ9S80Y6I0BP2HpLlx7Y4GuQ74A5n7U21G2vyoMmNBQFVotUWOgORkIRWiYIX64/L6+p8F/Qu6m+voQau7+wU9rg5kxYAyEwYAmUnNj4nsnn5EzMwbKZcB/YQiR3QtX77G0Sl65VNpx/WDAtXLtIZz6HjYSHB4Ea/UjQTu07wyY0NB3zuwIY8AYKMJAKEKDPqOoh8rxRfysUbC7Bx+eqVFmOcvGHRifI2a/NzMGjIEwGPCT+/0z/m+ex3JAGH6ZF/lkwI3kGnojvp4uYzlTPsthXuebAS98Hcr1p8+x6t3wDmBf26nNSTWhoTY8Wy7GQFMwEIrQMDh+mPyhvqy7P+GHODGbigH/LWwNLTlnql0WYAwYA3VkwHc7vjS+h/6N5aA6OmNZ554BNz3X0PlAw3LeAZvkvkhWgJwyIKHLHQd4L/OCwz9YWmNHdc+mCQ3V5ddSNwaaioFQhAZejP1DhM9u1dPc0bEf9n3xgqfBtyroc1RWkSnIjwUaA/VkwH+2UK1/+nQcw9HMjIH+MOC/CjA2fiZeydKei/2h047tBwNuDq6/M4AmwJUAdmI/ErNDe2bAhIae+bG9xoAx0AcGQhEa6O7rhYbT+uB7FaK6UbEfppgXZNftEPNjnwAtyE99AydErUu3R60vgP99G0Xz1tcby70+DLiD+I/qRfwJMFd9fLBcG4cB37vhHK6lyUAi806NUzYrSf4YcItxDd4OftY33x3PQ31lxawEBkxoKIEki2IMGAOlMRCI0CBnvdCAYl1Pc6vFfmxfTy/CzdsNhp8vgE2YGeBJao/azpsYtTkBseHBMVHEl1TMmo8BLwhO4H+q1uhFm6/8VuLKM+BW4Vp6DqjHjCp6wyqfh6VoDFSaAbci1+pr8XWre+L5wOax6ZlmExp65sf2GgPGQB8YCEloUIsJs17X09QC6F+kDq+nF2Hn7S6Ao4lg7rD9bD7v6NGwVSI0aMl2nec8ab5zEE6J3Tr8R78En4BVw/HLPMkvA/6LAL/jelKF7WuwP7BKW35PaIN77tbm+vwOnAvmBPODF8ElDV7w/hbPhIb+MmjHGwPGQCcDIQkNGnvHrOn1NveNPYh6OgduBPyoVeuInmLZvvowQE+Gy7rEhtYp46OBG9THE8u1/gy4Zfifvgv0sr1d/f0xDxqDAbc419MDQM+BRwDXmZkxUC8G3J5cgyeA2bs88L0v1ZMhMxzX/ZgwNWrxpTOzIgyY0FCEGAs2BoyBvjMQktCgl2Fay+ttvnvof+rtRdj5u8c4V2OBtWYFdqLGRdF0mqehS2xo+xDlzHqfBHaeaueOH5v8NP/VKeCQ2uVrOTU2A66F60kVPPWaUQ83VfQYWmdmDNSaAbc7196HQI1EZwIEB3cMUK+bg8A5YIcOr7xIdjfbqXcX39uB3qxmMQMmNNilYAwYAxVjICShQQ+FiytWsrIT0vwD7q2yD2+KA/2DXa1ZGzVFcXNWSIZMLDUxav02JTbcPTqKUi9WOSuQudtPBvQFCncb0H/2QmBzd/STUTs8YcB9n+vpxvjaUgvy+skeWxoDtWNAEz26fcGrYF4g4WESOA9IaPgcIDqkTV/PcgyTdcyd7D4Fn4Et0jGadN2EhiY98VZsY6AaDIQkNKhl5LJqFLJvaXpFXF3rWvt2XDPF9jORfwVH/2ymUueprO3RoN1SQgNNjq2MrTZrXgYkLniRQWLD7QDxwcwYqBQDbiuuKQ3T0fX1VzBHpVK2dIyB0hnwPW02jK/D7buO8/M1qOfN6qkwetA63oE1n43M/Qbo3W/+ju2m/TWhoWlPvRXcGKg8AyEJDVKcL698Efuaovs1fuhlaeG+Htlc8TsnhZynucqdn9IyhOJvXWJD66SJ0cA18+O9eVodBtxvubdpGIWGU9DyZ2YMVIoB33OGCZ19ZU2tw7sBhliYGQO1ZMAdyHX3ZvccfY8HvdfR60HmJ8vV9jYd2z5Mw4Fe6orjw9rYXgjQ+6FpzISGpjnVVlBjoPoMhCQ0aHb0K6tf5N5ycD/CDz2AUMXNijPglo95Oqp4HNtTTwboCzojn7x8rUtsaHuXbiiz1dMnyzsEBty2/Hc1J857gP+xmTFQSQb8JwWfiJ8PD7BcspKpW1rGQM8M+CEUr3WP0zmJ9fod4X4S0/90j6Mt9zrYK46zAesaiqHetuOBRLRmEBxMaJj6wrAQY8AYKJOBkIQGjam7qsxyVPAwP1mQhIa9K5hogyblHoUn5rNw0zRoAXNfLIZQjGDYxISU2HDHaJuvIffntf8FcKvwv/0IqOvwZv2kUipGAABAAElEQVRPz1IwBtIM6JngW5Y195K+KMWndm2m/zRDtl4tBhy9LL2Qun1HDn7OhjcIowLt52VYiqXe8TI9/NyiXeGaa8T3zNF1q2t5MaC5HOgt0fBmQkPDn2IroDFQOwZCEhrGcRO/pnZFL5aT/1a4xvKdUyyGhScMuF3hSQ9sPhllFioD9Gr4dUpo0HwN1gsl1JNVU7/ccP67L4LJ4OCaZm2ZNQkDbj6urRuAnhMSpbdskoJbMevKgNuUa41Off5LFBNYPg2+3+GSO5F1hkhkzZ1C+FigiSXfAZn3UXc8Yfdnj2rAbRMaGvCkWpGMgXoxEJLQoBv7dfUionu+fpzev7uH2dbUDPgHsh7md029z0JCYgBx4dqU2EBLtpkxIAbczOAOoIrg5aDNeDEGKs+Ar/ipVVnXmb6Askjl87AUjYE0A+pB41YG9N5Km7uVsNPTIWzPCRhZ6BDh/aSQ6r2ASJY2L1CMSYcQR8fN1T0s91smNOT+FFoBjIFwGAhJaHiTG3YgXzGQHxq/bNY7A+5kuNLL42K9x7UY9WLg4yiagckhn5LYwPLyevlh+YbIgO8arPHH+h/zkqmux2bGQKUZ8ML0MVxf3wG1Mp8IvlfpXCw9Y6BnBtQw4k7qHsddStiHYAbwIji3+35tuadA3NO1oT+NaULD1CffQowBY6BMBkISGl7nJn5TmeWo8GHuBHzRS/eMFU64AZNzw+BJXa8LPJgbsLg5LhJNMQPHRxEzaJsZA4UYcL/gf6wKoIax0RJoZgxUgwG3ANfXjUDPWH0Sc/tq5GJpGgOFGXAHcc2px4LmZJgGHAqmgM3BCKDrcpnux7qV4nCWMv/VLd6fG/LTmCY0dJxk+zUGjIEKMBCS0PAKN+1bKlCmCiThftb9oVKBJBs6Cf/SqG6HtAaYGQPGQH4Z8JNEvs9/GU3K/Ty/5TDPw2fAbcQ1pue+Knb3ACbpMzMGqs2Ahod5oUCi6hdAc4cgMsh0z5P4lTX3L8Kf6Qgt9dOY2TRys21CQ25OlTlqDITPQEhCg7qr3R4GZf7zXHr52TkMf0L3ws/QLL72Cd1T888YMAZ6Y0CTprmHgf7TGlIxsLcjbL8xUB4DfvJltSjr6xQTwVlgpvLSsqOMgb4w4AWHxbjemMshMbc723wa0w1IQliXIKZ74Y86wkr5NGbX0TlcM6EhhyfNXDYGQmUgJKHhOW7klZpUcA8IZ9bhcs2P09OD5cQiKfyEcIZXdMNotvcDK4Jq2PRlJHoAx9A7oyKmWen3SqXEJHLp8v/6kygSouPAb8FmoKcKyhLsF4c7AjNjwBgIigH/Ev7n+AX7PyyHBOWeOdNgDHhx6+r4etNYebUstzRYIa04wTPgFuS6k+B1YIerfjLJz9i+It5einW9G67ZvSjpT2N235PDLRMacnjSzGVjIFQGQhIa9PkhXmj7bT8lBR4EEZ8i6o/5zxsxjrSgXUWo8kj405JueD5M4XeDVlAJG0Qi1wBV3vtqL3DAzX09qEj8Nwh/JLVvftZVVh7C0atRNPNHDP9nezDzDnbycAfrM4JCdgmBk8CXoBwRpVCaFmYMGAMVZcDtz9+Z/6n/7NsyFU3aEjMGpmLAjeRaexbwLPETk8Zj4qeKaAHGQJUYcDQk+SEVNJz4OWuOZRl/jafXT2M2gjhmQkOVrixL1hhoRgaSinIAZXdPcDO/tx+OqPWcLpgRY4t9Rbe/QsOdJMP40YKWCA3Zh4pa+S8CqoD/vuCRfQ9UmkrvkL4fGo3gmCXLOK7QIcWEhrjXhybOdFxP+mZ6NBycB+T3MSBrmmmcOR2iM8AUsDswMwaMgSAZcOvxV9ZnbNW9fZsgXTSnGogBDdVx9A50nwOeD+5yMFcDFdCKEjwD6k3j5u1A2tnePo2ZjpvbdRMacnvqzHFjIDwGQhIaHuWm/kCZFKni+jhQpfVMoApub0KDRIIhYCaQtVmj6N0LSEYteYV6JhQTGpTOYPAN+K82MqbJEleIkc1X+cgf2QJgfsCMyBFd+Tor7Ml+gkoyiRTKMzFtTxdvKK1VAWUtyXoRGpSGQ1zwnMl35aveCoV42IVwnaPFwEPgSWBWZwbGcW18G0W8XJkZA1kGfJfiZ/nb8r913FvTY5izcW3bGKgEA242rrMLgb5qhDDtENuTluVKpG9pGAN9ZaC3T2Om03Pbcr0eBeZMh+Zg3YSGHJwkc9EYyAsD/4ejQgDmu0kWqpSW4ptaO+4DawH1bFAl9jjQm91GBMbjRcunIo5ineO3/gcLvVQvndqXrPYkNEgw0FwFLyaRWfLCFF0L5FcCiSLq/TAAyOJ8o11ZT+IcnVpPwpL4OqY3yw6deIkDzgB/BUl6WiqsNytFaFicZCmXO5XEpgXUWyM9tLJ2PwEJP/uyLh9Wzkay7doxwE1gjolR23uAAaqtR9cuZ8spPww4hji55L74b9bnyI/v5ml+GXDLca3xfPfP47Est8tvWczzfDPQ06cxsyVzp8XX7HiWl4FC75LZg0LYNqEhhLNgPhgDDcJASELDg9yI03MAlEtxX4QGCRQfA7Wo6zj1JFCX/tvRH1aPHxLbs521noSGnYisivPlqYM0x8J3YAugHgQrgUR42Jx1WSI0MNdBdCA4AgwFejgpvT+A+UFf7AUi35w64CXWNZfErWADoB4FNwClvy7oyUoQGnT4p3dG0WNwOBAOfbr7ZhJdJA4/NA4fwlI+XR5v26IODIyP2jbuEBkkNLS59miQrkczY6AAA+5g/tqaMO09wH3SzBioBQMatuNeBzyvnN4XfliLXC0PY6CLAT9J7gVce7yzZD+N2RWra83Rg9X9Feh+qeuW9yO3MVCP2lDNhIZQz4z5ZQzkkIGQhAa1WDxeAQ4ToeH4EtNS5V8VbVXseXmJ3gdzEIQg4B8MhXpGJELDOcQ9O4Z6J9DKF00Gn4P5gUzDH84F+2gjZbT++3wPicNUsZMfh8fbyWLmODyJl4SXsiwkNEhYUW+DxIazonwPSwKKLIsJDTq2AKZpJ/xokO2BIcFEHH0fJHYdKxJi4NysHgy8GkVt7VHrM11iQ+v4idHA1erhi+WZBwYkMHihYSLL3+TBY/OxERjwn8PkevOVPPWeUyVuvkYomZUhTwx4wYGGmvSnMXvy38/3cBLxPwO8LznezfynNNPvYj0lUMt9JjTUkm3LyxhocAZCEhrGcON9ugJ891VoUJYXA1WWVQFeD8TmxhH8z2QrtUyEhg8IS6CKOGXwwxAQKgravIRuANTKfzVQnqOBLBEaNuzY7PyttNBwb2fKHSutLORHb8JMMaFB4sp+4EAg4YX5KRZFNHj7LZKVyJI2bcNp9DBYKAWJMPJBaZjViQFO2vwIDR92iQ1tHxE2rE7uWLbBM6ChE47/v39xpmeU073KzBioAQN+/gbNCSShi9uU/xrATDXI2LIwBvrBgPse1yrvO+5VwDuP+wScAPRuGIqZ0BDKmTA/jIEGYCAkoeEubrbPVYDTcoSGdclXFV219s/S5YOf+OeVru3OtatYU/xSu7+tQ1z1LtAxvBhFz4NL4u3RLGWJ0KChEmmrtNBAhaCbqceB/FJPg56smNBwYuagNejEQG+GEaT53g6ZfZuyrbyK4WX2lcppJmnbrAQD9GJYlTka6M2QDKFofZY/xQyVSNvSaEQGNCmkOxZMAa+D9Hw3jVhgK1NQDLjFueZuBjxTfKXt1ywHBeWiOWMMTMWAv28ybFafdPfXrgSzv4GVp4pa+wATGsrkPNt9t8xk7DBjwBioEgOTSTfbAl6lrLolOz1bFwKJHIMB4+86TWHDufkrvFxT74abAK0ufrLKGVkuCZIeBNmKtSrh1bRsfpXO6/4oGnREx0dAdj8rk/iubNPjwX/tYkWWaVzM9iJgHWBWJwYGRZMeosb4yyT7lqhlqZmjtr+P6ZjDJAm2pTEQM9DC5dJyFBubALUo/5f75e7xTlsYA1VmoOVFrr/NyGQkGAv0zCFMM/6bGQOhMuDvm7wXtuh9ZxlwBdgC6P5JRd9tD9RoZmYMGAPGQK4ZCKlHwy3cWAv1HugrwX3t0XApGUwA6kmwF1BFfxTAHBUurzZnW+n60qMB1dqnuZNPsutn5zg8ERyUp/JeqiuKX5MwoXAq73029aKgtafTXmLtxs6tjpUBLJR+pXo0KFXEjAXHdqS75SEKwCS4aN6Gv2ijgC1KmPy4rsA+C6oxAxOi1uOTXg1atkdtF9XYBcsudwy4ofyF9aLM/9jx4lzq+OXcFdQcDpIBTbDn6EXnePb4a1DX4mpBumpOGQNTMeCHAx3JNTsuvn7fZXkYGDJV1OoGWI+G6vJrqRsDTcVASEID6q663vbb+iI0bEluqtweFeeq1v57wJdgGLtWAXppzooEfREaliAtWv2ie4GU67nBruAbMBmcBWSjgHzJCg3q5TERPAhQuf3XMVhEmjviz1rpweolNODSJj+MolbKPeu3bKilU/MvqHwbgmL2ADtU1nmKRbDwmjHQwuSQ16TFBoZU8NJjZgz0xIC6rbszge6b9AhT13YzY6CWDPiJ+g7i2vssvg6vZ7lILT2wvIyB8hnw99AduWYfia9f3kf1aeGamQkNNaPaMjIGGp+BkIQGKs7uzQpQXqrQoAr/J+AJoGMSY6iEFwHui6LXqSD7F+Y/JDvjZV+EBh2yO3gSqKItPAPWBBIfFC4rJjRon/KfAHTsokD2IaCLaI9WR6FBfu38ED/4PNfVLDUM5X0g4aSYSYBRGX9XLIKF146BV/2XKNoe6BIbWqfQ02GH2nlgOeWXAf8pQr0gI6i6n+e3HOZ5fhlws3DtnQZ4dvpJI89lqZ51ZsZAThhwq3LN7gtaauiwCQ01JNuyMgYanYGQhAa6zLt3wiPcvY1f9LaoiA0llVnLTKmV43hxypPps2P+Be/yPHltvnYx8FUUDaFnw8spsUGfvVyrK4atGQPFGHCItu4xgHjorgS1bJUr5pSFNx0DbgGuPRoH/ISlvPM4hGy7FpvuMrACl8qACQ2lMmXxjAFjoFcGQhIaaPV2avEOzNxt+PVGYE7lyB13OfwxHMINy5HT5mqKgfFMiIrQ8FEiNiA8PJvabavGQA8MOATSzqEUdJJxK/YQ2XYZA1VkwI3g+rsbSPj6COwP2qqYoSVtDOSRARMa8njWzGdjIFAGQhIa/trx8A+NKXcSfk0BM4TmWT780dhYNxnYZIL5OGEFvWyPBq3MHA3fxGLDnQUjWaAxUJQBtxn3AFXu2sFhYEDRqLbDGKgqA24Drr9HgQQH9Vj8JUgPn6xq7pa4MRA4AyY0BH6CzD1jIE8MhCQ0XMHD/tPwyHOalEcvJKuG51tePHISkVTBmC8vHpufUzMwPmpbFMHh58ywxtwlZsZAXxlwc3EPuAPofjoGzNvXFCy+MVA5BtxWXIPMY+Svx1dY/gTUcix85YpiKRkDlWPAhIbKcWkpGQNNz0BIQsNlPOSZPCw0c0vHLyJ7hOZZfvzRzPO+V8P5+fHZPDUGjIHKM+A/QXgg9wNN0Iew7LaufB6WojFQKgPqWeN2BmOBBLCnwI9KPdriGQMNyIAJDQ14Uq1IxkC9GAhJaLiYB/zX9SKieL7+U0NqjbdKcnGSStjjroZDVS6sFbMEtiyKMdDYDLjluBfw1RxfudO9/3uNXV4rXdgM+LlE9uY6ZJ4of00+xHK9sH0274yBqjBgQkNVaLVEjYHmZCAkoeECHuzfhXka3NP49kCYvuXFK7cUHGqui3Py4rH5aQwYA9VkwE3H/eBCoJZkdV3/YTVzs7SNgd4Z8NfkIVyLn8TX5b0s1+z9OIthDDQMAyY0NMyptIIYA/VnICShQd+4nlh/Sgp54D/NxrAOG79ZiJ3Sw9x1cIiY5OYu/RiLaQwYA43NgPsx9wRNFDkJHANsYr7GPuE5KJ0mf3ZHgc+BhLC7wMo5cNxcNAb6y4AJDWUyaBO8lEmcHdbQDEhokPFQrbe5s/Bg3yhqCXA28m1vi6LNN4qinRbAvzczTBEW7QYQSqJxmX2bsD1LJkybj4OXUuHLsq5umoMAeUX0oChoSbw52PsG+CfgBb1XG0yMrcESgDGo0R3gK5CYWmzmTzYKLJmh2/tLr4ToCDAKUCnoq7llOEL5nw2P+/f1aItvDBgDjcqA0z3tUrApeAzsxD3iZZZmxkAdGXAzk/lBYD+g96Rbwe+4NvUMNzMGGpEBCQ0yE9Y6eLBfY8AY6AcDIfVoOD1uOQhNFFyF+v8XUfSZWjU2z3A9DdsPAvZFy2f2qRxfxvu0P40DUnEPZV2V9veAXl4U73agtNN2LBtTwDdAFX+tfw74VFePNj97nwfqLfJfQFmiCWAdkNi1rKT9y67vm0Rk+R9A19JyzSGOuG/BnOWmYMeFycA7UTQtX6X41fhooI1tDvMU5cArh2jreC75ewRj5s2MgRAYcEO4Jk8EzCPlezjoOSbh3MwYaDQGJDQkYkOjlc3KYwwYAzVmICSh4eT4Aa5W/VBMYsEzUTTnH2Lfjsw4RtfKzgp6VmgYHu9TJX1oBtOzLVsVSDC4AbQBmVTkT8GZ2ohtDZaq/P8JzBiHzc/yNfAhGByHFVpItJCQobxkM4ALgPKQjzJeoqbycRhhL8ZI8mTTp/MdS5WpDHPw5F/UTi3jYDskYAbao7ZzJ0ZtjH9q41umg/YJ2FVzLWgGHPclp8n4uOc57l9unqDdNeeaiAH1vHGnA56Bfs6hv7NcsokIsKI2PgMmNDT+ObYSGgM1YyAkoSGpzPdUaa4ZMXFGm7KcDHjRdR8DtfwnthIr7eBSIBEgKzRsHYdruEIxO48dOjYRAZJ4F7MiAYKXGm+n8Kt4fCaymx3ElsLlSyGbnUDtvzOzc162lb7yL2aHskM9Hwr5LwHitGIH9h7ubsKtb8Ccvce1GHlhoD1qvTIRGiZGrVPUuyEvvpufoTHgpuH+wDAtNKvIfQZ2DM1D86eZGZD4pYmN3XjAs9TPP7RcMzNiZW8YBkxoaJhTaQUxBurPQEhCw3E8rKkUu6S1v/7sdMxlcF+HI47KulMPAtn3wCuAF43oJ0CV+azQcCxh+lyn5pzgpSRaDag3QdpIM3ofZIeLbEOY0twCyHTcon6t+8+JbCpeITFAMVeN9++hjYw9x/ZTmbBkc0lWeIGKfp8EZJYK1xwPZYpCvleDXs6Yq8GsURj4NormpVfDmxmx4ReNUj4rRz0YcD/gPvEs0LNB3dVNnKzHabA8izCgzzV7weG7+BqViF5M+C+ShgUbA0ExYEJDUKfDnDEG8s1ASELD6PhBPVNAlH6BL0d2+ONOwj9VjjWM4ELwIpgWFBMabmbfx+B+oN4DEgR4GYn2B4ldyYp6TKiHQdrUW0Dxd08HZtaVt0QKiR8SMwrZcAKVzkWZnbOyPRFkJ69Moj3AinwvJiSszj6lmxVXCCrVnLqc0mPCzVfqERYvfAa4wBdAbHg7JTZMpmfDLuF7bh6Gy4Br5T5xApgEPgU7heuredacDOhLSo7ntu+px7PRD/nRc9LMGMgbAyY05O2Mmb/GQMAMhCQ0HMnDWQ/oIYHw9X38UGV6VIc/bvsO/3Y8jO12sEJHeFGh4V32S1jYEywI1PVXN3ClKXFC9jOgbbXstwHZ/OBtoPADQSEbSOC/gESKNQtFiMMkQLwGJJgsHodNw/J3QOl/FYelF8vE+/6QDsyszxvHibnJ7C1p0y2GC5PApSVFt0i5YYCuMMMRGt5Niw0TotadclMAczRQBtyK3C+eBdy73C1A9yEzYyAgBtzsXJc8Ox3PVn+djmG5TkAOmivGQG8MmNDQG0O23xgIgAF1reelKFoXhPwyFJLQQAXeP5iTeQnqfRo3xAFVxkd2OKKK8QdsTyvOjuoI878SDRQv27q/EGFC2uZhQ+LAk3GghkzcCnT8R+BOQOU7+iNQ2O4gaxpGcQdQOj/P7iywvTZh9Gr36d3DUiLGWHA7GAeydg4BSnv+7I7UtsQK+XliKqyMVfcn3CIdt0gZB9shATMwPmpbGKFhXEpsmITYsEPALptruWDA9244hnsGYq/7EnCPdLqPmhkDATHgZuG6HA0+BzzL/eSmmvPJzBgInQETGkI/Q+ZfUzAwG6W8CuyUKa1amo8EGt/Ow6UTd7OuluLQLCSh4eD4gazKeAi2B07oHC7V4Yyjd8C2DDdo4wU32gdov3AJULwTQPZ6IGgq+w8huj6Sl2PS9ekwlMAPcdiM5QigNLcBaZMII5FCPSW2Su/oZX0x9p8FbgSHgrnBzeA5kDYNx+DFyMdLhxda19CKawrtKD3MDaOYE8DVpR9jMfPCAMLCYggNH2TEhm3z4r/5GTIDbmnuG48C7pOOe6pbMGRvzbdmZUBDLd3hgIYEf60+zXJ7ILHezBgIkYH/4ZRgZgwYA3VkYD7yVkXwjIwPp8fh6qp+LTgfqGI5BXwKFgAhWUhCA8ME/IN4aCAEJT0VUq0QS2uogc57Mai3gIzWjIiXiYiX4alMQoF6LySWCA7Jtpa/AMojfb2Il5eBjl0F9MWyeWj7LfDnTCISNpRvqsyZGB2b08fxLii4t0+B7lyy5P/hlu3TYRY5FwwgNiyB0PBhSmyYSFhfRLJclNOcrAcD/ssUh3DvQHj1Y+OZ/0aCsJkxEBoDbjquzV8D3hH8e86rLHcDraF5av40PQMmNDT9JWAEhMBAIaFhURxTl3O1Es+VcXI1tlWpvz8TXu/NkIQGPYSp5LoF6k1KnP/qLFXp/k2XP+48gtRlt60rrOAcDbOyX9fCg6l4Wl0MKM3rtIGp9wK9JKLltBGbRID/xkjCeEmJXgfvgOxwjCROseXD7Lghs3NjtuXHRpnwY+Nw9ZzoyUawU8cf1VOk0vb5SbS+IbnbSotvsfLGAMLCUggNH2fEhq3zVg7zN1QGNPTK8Wz1z4+HWC4eqqfmV7Mz4AZxfe4KaDTw1+u7LA8AGm5rZgyEwIAJDSGcBfOh6RkoJDTsDCuqfKkSV8h+S+AkoEpjKBaS0LB3/ODta0W6WlwOJuGvgYZGxOZ+Gfv4gySEZdLzYflUmFZ1nK6HU4FefH8IdAMX58mLsFrf1EvhPrAqUA+IK4B6TgwHiWlYhtJSLxm6YU6FhQmT/RjcA9YGie3EinrUwG80DGwD1LtGvW2yJgHk42xgge0dCZM/axTYV0aQQ+DwL13rlXGwHZIDBvjyxLITo9ZPU2LDt59F0Uw5cN1czAUDmqfB7Qu4Z7vx4AhApc7MGAiRAfW8cQwjc08AnqXuE/A7oN6QZsZAPRkwoaGe7FvexkDMQCGhgZccX/matwhLqkSpcpaupBaJWrPgkISGX0GPHriL1az0vWf0T6I82hXNjYh9/EVXWFGhYVrinAXUY0HnXZX9p8BSIG3rsPEOUBzF/Q/IilVvx/sVpxAkMMj2BNov8SOxaViReKH8te9dQM8M/2lOFt3sBbbGdAspvHEywbwYRUq7AuZmwLUPAfxY1+cKEBpkEogNyyE2fNIhNrT+H4oW593MGKgkA24Y95C7APc69wxYqZKpW1rGQOUZcDzvO3vk0MjgTgXzVD4fS9EYKIkBExpKoskiGQPVZWA+klel7YxUNmqRVli2kphEOS3eP3cSEMAyJKEh6S2QrYjXk6bV4nO2YocTGjLhEAM0hKJk0zCLxcHMPRyhCvtwUM3WDOW/KNDQjP7YtBz8ETiiP4lMfazbE15VOdhl6n0W0igM0NS8IGLDUYgOVgFslJMaZDncztxLPgUMYXPngBmDdNOcMgY6GXCrc53eAvQcnAAuBUt07rYVY6A2DJjQUBueLRdjoEcGEqFBFfUx4BSwPXgF3AcGg8QGsLIdUAuw9odkIQkNu8QP2OVCIghfrgb/6PLJPYmfuhE3q+1Nwd8E6Wu8Aly4gfD6PHgfWEt3BRi1JIyB5mbAzc695C9AFbdxQM9oM2MgcAbcMlyrfwVq1KAnohcf0sMhA/ff3Ms5A3q/beZ33JyfPnO/URhQ68j5gImn/Dh+XmR8b4ZkuSvbiSmOwmlZiTZJAgNZhiQ0MJeAfyFcIRBuEjckKmmuhkU6AtzF+PkdaMbxv4gB0Vgg4awK5taPr4GTqpC4JWkMGANNyYBbh/vKS/G95U6WCzclDVbonDHghnKt0hPWaTgF75DucbAD0HPYzBioFgMmNFSLWUvXGCiTAfVYUJd0jYv/A7gdrAES4+EQPQvWSwICWoYkNNDa5B+mmjQxNKNbfzSqwyn3q9jP7OSPoflcDX9U5r4MGynDB3cD/Krb6EJlHGyHGAPGgDFQgAF9RtAdBSQSjwejQVuBiBZkDATGgJuJa/Vg8C6Q4PAWOABYz7/AzlSDuGNCQ4OcSCtG8zAQ8sMgJKGBryH4h+hqYV8ajsk8vZ+7he1nXr1zzFXhKwI35bUE5rcxYAyEyoC/v9wW38NfZUkvKjNjIA8M+E9j0uDhJ02W4PAlOA0My4P35mNuGDChITenyhxtNgYGUeB5gLplLgiGgArNyk9K1bGQhIYteGDq4blWdYpaqVT9w14tYhdWKkVLJ8uAo2eQvxY2zO6xbWPAGDAG+s+A/7Tge/F95u8sh/Y/TUvBGKgVA44esu5WoDkcJoFrwaq1yt3yaWgGTGho6NNrhcsbA9/DYbrS+08gtrOkotwN37B9H6C1PkgLSWjYDOokNKwbJFPdnHKP4CffvzarDgNuevgdB14AA6uTh6UaKgN8keKXfJnic3DTZ1FEt2EzY6AaDPjP6qpFeCLgWe0OAwyxMDMG8sKAPgfuLgDfAr0/UUl0DOO152ZezmCAfprQEOBJMZeakwG1gDBWzgsLjPuM+BpBdDdAWY6uB/eAZwAvMT6OwuYCIVlIQsPG0KQH5QYhEVTYF33e0iEs2RjfwvxUItSpi6iuh/0rkZqlkR8G2qPWlyZGbdT+2viTtT5FDXDu/HhvnuaPAbck95n/xPebV1hulL8ymMfNzYCjB607Akig13NT8zj8BphQ29wXRjmlN6GhHNbsGGOgCgw8T5qoyNFuYMYe0p+dfYeCyeCOHuLVY1dIQsP68QMytC9zFDgv7uexrysV2GlBFWHAtcDxf8HnQP8hsyZhYELUekIiNHSIDW1jx0dtizRJ8a2YdWPAtwQnE+79k/vOsLq5YhkbA2Ux4Cc9lUj/BJDgwDueOxtoSK+ZMVAKAyY0lMKSxTEGqsyAbtrcxL3IUGpWx8XHhNSrISShYW0o1YNx81IJrV88t3Ts697186EZcnYIOX4Mqs2H0QynO1VGxIY/pMUG1j9mSMWKqSi2agxUgQE/bIvP6/oea+qO/jswuAoZWZLGQJUZcCO5dm8ENHL55+jNLJnbwcwY6JEBExp6pMd2GgO1YWBrspHQsFAfstMNXseM6MMx1Y4aktCwBvRIaNiq2oXuf/qOST79mN4/9T8tS6FnBhwc+xclfVrTrIkYaI/afs08DVO6BIfWb+jZkAMhsolOUsMW1S3KfedOoGfS64DJis2MgTwy4L+0cibX8Ffx9fwsy92B5hgzMwayDJjQkGWkxG264ZoZAxVjYFVSehCMAn8pMdVjiXc4mBmogl8pW46EVi4zsTM4jlmLowAeOG4V/HgIMJFRC7OAh27ufjycFV8Z32tWPQacegC9DF4EXCMtvPibNQsD9Gz4yYAouoLz3tpRZjfFRS0HtEYT6A5sZgxUmwEvfJ9OLvODMeBArsWnqp2rpW8MVJ4BNyNp7gr2AcMBn8eM/gTO55rmU69mxoBnQEKDrNx6RcfR9msMGAP9YoAWbV/xQSH2X52QeFDMZmPHbwHd16LbikXqR/jNHKvKV7nQZJUBmKNbtG89+mkAzvTkQlvHTncq/nJO/cO7p/i2r98MuF/H14a+8GLWZAx8Fw1ch54NX3T1bNAkkYPOGh1FaBBmxkC1GXDTcv85EtBA4O/5l7Gcu9q5WvrGQHUYcNw3HXNhdX4ek8Ymx7up2xTYPbU6pOcpVevRUObZsh4NZRJnhxVlQPM0XArWABPAK+BT8AnQzXoWMAQsAQYCtciuCbS/kqbeCEPLTPAxjpNAMUOZx1fwMKeu8U+AnVHXr6hgwpVM6gQSOwDwuVI3HUv1vGBITMvdLGthe5DJu+BfVcjsx6T5wxLS/Rtxni0hXgWjtG7LdCi0YE87UxRdcmEUffFdgcRvIUy9jMwakAF6NizRErXcyoN8/q7iuRs/jtp/Ok/HpLxdwbZmDFSFAd+76jiS/jnQPehkcCr3f+ZyMDMG8siAWwiv9wK6ptVg9hbgGat325aPWJo1HwPWo6H5zrmVOGAGeO+N9gQPA4kN6V4Fk9n+AIwBW4EQlWJaaCo6jIPkyrXOCRZ3LTeFKh93IulrVrApnPR2epXtwibnW5+Uqompp4eur+OrlJuG0STXg5bJ9fxNJlzXcq2Nlx6VfTpaXtrgvpufic9719opy6+2DHwdRXPyuctHu/dsaH2U/r+z1tYTy625GXDLcjv6N9D9H+HX/QzoXcDMGMgpA2o4cb8EjwNd1zz/3VVg9ZwWyNwunwEJDYKZMWAMBMbAIPyZA0ghnhcMBKFbUkkLwE+3ePyA270GzgwjD6n4pYo/XmTYJhrg3oha1QQwqUNsuJ7eKU4t6dU0XUeHgvGgmkJDtgwa7qP81NOk3hYLDZ/R0uJfgjaot0OWf30YGIfaxDCKG9JiA70dqiW+1aeQlmtOGPBdzemp6O9J9A506rFoZgzknAFHz0b3Z0CvHX9tP8OSBjUXQM/XnFObD/dNaMjHeTIvjYFcMBCS0LBw/FCTAFBNWxB14V0yUCX6CtCb2NApMnyHyKAKztudYsM09Fq5kYbWqo1r1LAYWhj8hJ1nsixFaFDL2hDAMIOpTC2/Pc0nkj6gJ6FhFiJOn47M+rRA+Wb5VJj2Jab9aDXRaqDUluhYaDgFIQ/6I/cmEDe9mV6MVoiR5aOVcPkmWwDM79eiKB0uX5cBSwDNy1LIVDblsTgoFEd8K47SHQEkSCYmQXINoIm5srwlcWyZYWA0XDFHw5mJ2MD67pkotmkM1IgBhxDs9gESnbk/u+uB7m9mxkDOGXA8H91vwGtA1/ZX4HywdM4LZu73zIAJDT3zY3uNgWAZeBrPhJDs/3BGCMAclT3/MGPiv6qZFxmo9U1S7wRyEa4AxSp5U4kMSeWmQ2xomdwSDSSNLfatksdzke59YC3AC6339ziWvdltRNAkn+neCKPYVnkPBqVYT0LDgyRAC143u4wtpb9eKlQVdIVtFYepxY8WQB+mcOFqwAtNjxYLDdF0HLIJ4Dh3dg9HzMa+a0GSh5ZTwEUgOdcJH7um4v2c9e3j7Q1ZUnnoTOMN1pcDiUlUOApoKEeSzwes4183e50tDUt5BijeWDAY3BBvJ8eOY3sDYFYiAxOjgWuOjwbqPJkZA3VmwCEo+gmC1eWce4I7B8xeZ6cse2OgAgxoWJDjPut4ZrlJgGeWewDwDNVoUrMGY+B/lEcwMwaMgZwxkFQoQnI7JKFhaPwAO7BKBHWKDGOiQb5nwr7RNMk5uYI8kwpokv2JrDgJEklPhkRkSJYdYsM0PIUH8PCNfpQcWKVlX4QGCRQfgyeBjlOL/VfgdsBLQ0nWk9BwCCmo4p5+kX6HbfF5AkhMadD90n8+9fssPwcPg2WBehv8BHwB7gI92aXsVNoSKlZiLszbSGYyX5qTSMC271HAotOuYU35bgFmBYqTCA+bsy7jJcmn+RFLXXNHgLlBIjR8yLpEGfU22BZ8C+4HiR3Oinw6DdAbxw+X+gtLXQvqBZHY66zIF3yOfgEkZhwJJARtCWYCPwBPAJ2zVmBmDBgDuWTALcht4WrA/dG3APNf9xMH57I05rQx0J0BN5Tr+ViAMO4Fh09Zng4W7R7PtnLMgAkNOT555npzM6DKR7oCEgIbIQkNVPL8g+u3VSBmKpEhEQuKiA29igzJ8R1iw4B4gsiqig2J0HB8ifyokq2KsCrQ6oHwPpgDlGo6Dzo+3SsiOXbxeN8OcUCyrYq4HlKJ3cvKTfHG+SzHA15UutnebCmfdbuFdt+4NI6jeIWgdBObhpVzAd2Zu1ni4yFx6CiWSkuCQdoSoUEiQ9quZIPWSm8SSb4GD3Vsdv5Oy5r+U9d3hkTR66xLYKE3Rqf9mTWdjxk7QzruDfJJaZsZA8ZArhnwn2sewy2Ge4x7D/wS6N5kZgw0AAN+yNBWXNN3AIlqus51vf8EmFie7zNsQkO+z595bwwExUBIQgOt4/5hla349ZewoiJDIhZkxAZVnt36UUvRngzJcclSYgM1eIZR+Iro9/vrcJHj+yo0KJmLAS8A0WSwHuiL9SQ0KJ1XwOVawfYDb4FfALXoqwI9S7yuXgey+0FahPCB/AwF8pGxoEXtUvYozsZg7Q6MPjaK/kPYhVewPRIUsnkJ3ADsC64GSmM0kKlSr226hHazRGhQPmk7gQ3FHwRWjNfVS2KPDJ5n+w2Q2Ous/DfZiJebsZwM1JviMrAdUM8GM2PAGGgoBtyPuG08B7h3OO4NbvOGKp4VxhiIfC+eE7m2P4yvc55rTtsLGTm5ZOB/eC2YGQPGQGAMqAIyD1gY0H3SjzsPvQUjJKGBiql/Gfsd3FXKehUZErEgJTZcP4DW5u9RYb4/HmKRxCm2PK1rCMa/cVzXQTWsHKFhXRxR5fhjoIp/X6w3oeFUEhsXJ/gvlqowLwCU36ZAgo0q0whI3iRE3BivpxdtbOgYXkyK2qXsUZx0rwAF0VuCUTCRWyFz5Dpsv6AIgP2RKv+XxNujWcpGAe1fWhspS4SG5VJhWv09UHz5S0uOX3+X5asF8CJhib3Oyj+SjdRyI9bvAu1A6apXxkkg9HsGLpoZA8ZA6QxosmC3K+B+4Z9xD7EcWfrxFtMYyAMD6sngezSMia/zKSzvATxrbfhQHs5g7KMJDTk6WeZq4zNAfTT6FXgUJBUGVRoSfMP6fWAbEKKFJDRMD23wpvF/FbFhvN29S1/2ScmcDMWEgiS8HLGhS2RY4GW8JruqWV+FBviMXgPPAp3na0BfrDehYU0S03X+A6D0dwQyVaxPB38FuvYTe4CVR5KN1DIRJ3ZKhWVXLyVAeWWFhrkJ/gQgKnROSjUH8b4GjwP5mJyTJJ9jCJPx8uPTXMpvdf0kQsPyXUF+7ff8ygcJDT+M15NhGGwWNfFxXdG9HV/vUCunRAelT9dTs0oxwISRq/L/vpVPYh49Jh+fHK5U0S2d4Bhw3Isc91X3KeC/7rudrxScm+aQMdBvBtwiXN8ngffja/1LlhcAu977zW3VEzChoeoUWwbGQGkMDCWaWmlVOfgOPAnuBteC68E94BkwESiOwuYCIVlIQsNgaNLL1x8qRNC+pOO2ZzLHREjobfkuQyBm7JjkcALHroBQ0WPPhi6RYQ3if0XrelWtr0LDpXijciwN9gK6BkeBUo0XYn9MtsKdHK+Wdyr5vrVeaSfX9oWsPwc+AweCxM5kRf+FhZKAeJnks2QmPL2psiiPjNCgKG5boOvmDG1hmwPFzQoXO8fhx7OUiQvFK0do4DKJJCK+ANI2PRuPgXNTga+zfl1qW6vq/SFhIW3D2ZA/R6UDbb1/DLRHrY+l/vdjuOHN3r8U7WhjoL8MOIZJ+Qn1uBz9vYtnh8v2oOpvJna8MRAAA34uB57Jjt6Mvvchzzj3LDgAzBaAg+bC1AyY0DA1JxZiDNSFgefJ9VuwG1DFo5jpxfZQMBncUSxSncJDEhqouPqXrlMrxIUqwlcBdxBDG1KVjYLr7yEyLMo5aumoDP849mHhYmJDl8ggcemrf5DN54DoVbO+CA1b4kW60kqxvPD1JcthoBRLBIDle4j8Z/YpHwkLiW3LisKEBZNAlnOAL4DijgRDwR7gG6DeD/KxmF3KDqU3Ghw+NfZ8ilOtbpprs28JwHp0L1gGzA12BcpH/8GzgGwUUJpLaSNl27Ou8Gy5fx+Hq0eDTH4o3vVgHbAy+CeQmLIsSOx1VrJCAy9e/tiTWSqujr0ayL8RwKxCDCA0/CXz339HvRwqlLwlYwz0gwFVtPwnMXmP8BPq0UjhdP8yMwYakAE3F9c37xXuJcCz09EQ4ng2uo2B3tfMwmDAhIYwzoN50eQMLEz5VcmQyFCqHUdEHcPNNhgLSGgQJ/7ho5bvSlmn2HBgD2JDEZEh8WEqsaG7yKCu+Y4Ks/d9xeSgKixLFRpUsf4EPAF0TGLDWVFl+z4wIAnsYVmK0LA1x+uaTirvSm4IUIVZvXmypkr9w0DHCB+As0FvLxmXxvGT4wos16RXkXsLzEjc3cGTqWPki4ZRSHxQuGwUUDryKW3bs6Hw3oQGcXgAkHii+BIY7gE/BWl7nQ1epqYylZsXLX+sjn8HbArMKsgApE6L2HBld7Ghtb09atuvgtlYUsZAPxjwFbBzuBWMB9w73V/AQv1I0A41BgJnwK3GNc5z3fEO6t+d3mNJb1b7TGYAJ86EhgBOgrlgDCQVrL68DKwHbapQjAiIPm7yfnx9IC75rnXnVtgZVWJ9z4ZCYkMvIkPiSqfYsE/XxI93sxORQeYQnvzDspQx+x2HNPfvTBR/eGUpcBvE5+BPqXSHsj5rarsaqy0kOj+YuYzEdf3QkSaas4xj7ZA+MNAeDdqXeRraM4LDtR/b50T7wKJFrS4DjvuVuwi0A4RLXxHTvcXMGGhQBtz0XOe7gvsB78ce/2VJY4GGGJnVgQETGupAumVpDGQZUNdbbopTjQPPxktvH8vGZDBDOrDO66EJDepCemEVOCkoNpQoMiTueLGBDZ33lMiQ7HbvsOuOZMuW9WDAncc50MvKj3vI/WfsOwGox0YxkwCgOMKCxSJVOLxWfvFi1yc7kNij+nREoJERG1amJ8PbabGB3g4vT4haNY+JmTEQCAP+c4GXcx+bBOj15M4H8wbinLlhDFSJAfXi8XOXqGeinuPqpXgVWB+U0guzSn41XbImNDTdKbcCh8iAKq4vgq/Ar0BPrZmMw/SVGokMt4GQLDShAT7VilMV6yY2aOJHmpKzczL0lrF6sBwD4p4M6ejuz/jO0AR94smsPgzoE1ruFfAhmL2IDzcTLrFIGF4kzjapOCOLxKl0cLX9GoTD14CeBJZCZWI8bcHPkhaKG3wYN5jZEBruSIsN9HT4BhFCQo+ZMRAQA3pEuavBFKBhFRJSTXAI6AyZK9VgwH8Olh7AfgiRGp8kOqgh53iwSDVytDS7MWBCQzc6bMMYqB8DdJf3491VYeElwI9JH8NSX51gcsDoP+BpoLHbivMCkOgQkoUmNHwGVVdUkaBOsQFlaAp93nVuemr97oMrbmd85zy7NfpwkEWtOANuFc6BWgL/WSRpVeglENI92U/SWiia5lRANPL/25GFIlQhrNp+SQzVfeiQPvreUEKDyj6aOUroxTAagWFyWnCgt8NFY6NocB/5sejGQJUZcHyRxyESesFBPRxMcKgy45Z8KAxoziW3G3gQ6P1KeAjQwOd6auALpQB59MOEhjyeNfO5YRmgrhrtCR4GvAD4F3m9zAvqwfABGAO2AiF2/QpNaPgQ6ugqV1VLxAadrwqJDPLXj6/VQ3C0tszqyYBv+dC52L2AF6rQvwduAZowM2sa2kSXTf8VCP2PR4KsKc4KMWZK7dT9YAhIhyW7Z2Wlpxej/vqlvIcBCV0LAl3niWldYSrPMUA+Zk1hmgAze59KCw1zsh8hJ+rr8AsOCc/GRwM3QGj4OCM23Ien4tLMGAiMgYKCw9DAnDR3jIEqMaDeDP7Z/jZLPd/Vy0cC3CZgYJUybcZkTWhoxrNuZc4FA4Pwcg6g7vXq3piHG9//4acQiDkqgO7vNXBGFYkq9C7x3fbvr4H/lkWPDPhvd/OwdOp2uUwm6s1sS2hQV3lVvPV/TdtObHwBdgbaPxIkpmtGPZYUnoBuzdFFIKmga3iUesqkv1oxim3FPxgUs/74tQ2JfgQSn7R8C2guGZl8Se/TeuLvcNYluKgcCpc4ejpITELDv4EEwCQNeoxEx4PcGxfIUOZp+G9abPg6rC8D5Z5jK0ClGegUHGjM8HM4XMxS/2MzY6AJGPBDK9blmr8CcLv2ooMaqc4GKzUBAdUuogkN1WbY0jcGmoiB0ISGN3lQFOvynoPT4s7FfyqZ/hOLOfC3kV10wzgPnwHN2TBjqqRJhV5htIhEh6X2afVf4DKwPVDFeiRIjNYT39thC5bqoaCXmkR42Jx12VzgY/AkkNi4APgK3A56aikv168lSFd+SqBbCswGJJbov/0EkEkE1YSHivcHMD+QzQ5eA4+D5cAs4CCgeFsB2UtA2xJQVgQ/AOJIYSNB7u0x+GGOhlMZSvElwyeotJkZA3lgwAsOf+WviPDn8ReWDLMwMwaahQH/1Yqdue7vjP8DPJf8fA432n+h7GvAhIayqbMDjQFjIMuAKiNCIOZe5eGgSkxOzW2G/3rQbZnTAjSY225TzgUt9e66VMFuZl09GmQ3gKQyrm2JB+1gfZAVGqYh7FywD0jb4myo0n1IKlBChMKOAA+C98EcoCcr168NSfRvQKJB2hJRJAmbmZWsn/sTRqtotFgSKV5eyvKEeF1Cw2dgunhbix8CpbWvNsyMAWOgngz42fov4S/Jvcvf725iqSFOZsZAEzHg5ua6PwC8C3g+ebzAcjQwAa70K8GEhtK5spjGgDHQCwOhCQ16KNzRi88B7/bq+gTKcGHATvbk2lB2qvI9uEgktc6rAjpPkf1J8B6sUMkvaGoRPwz8DtD9seAQI/mxYwFsS1gpppb3A8HxNOAjMkzRSwcvIN6SCr38UAVdFeZRQL0PdgN0wfTzG/yGpfatA7I2LwEbAFW0rwaKNxqk7WI2FD4ZrJfeUWQ98Uu7dwA6diFtYGm/sgJIR4yOL6Esz4aO1Tl6HSiNxAoJDZez880kQpGlhAaJJWlTzwwEnOjkdKCtGwPGQD0Z0Bcp3Bnga8B/390LNqqnR5a3MVAfBtzCXPuHA3oWdhMdjmFb7zFmxRn4H7sEM2PAGDAG+s1AaELDMzwE7u53qeqagPsPZXizri6Un/mtHHp0kcPVoq8KpyqvqtAWs5+yQ3GOLxBhT8JUQf0EPA0UTz1Y0q3lbHqRQPuy+FI7ezBVgE8EOu418EbH+oaIB761b1W2VaH/HCR+SAhI/BjD+jlAllTo06KRRAfEMB9/IsvnAS2Jfns0y7RJRFG6GkYxS3pHkfW00DA9cb4FvCh5038i69fIjl1RK0v5OAEoP3F7O9CLgrYTKyQ0PMTOR5MIRZYSGiQ+ZU38nZINtG1jwBioNwNuCH/90YB7ga9kPcWS+5nTPdzMGGgyBnyPn8O4/um96P8P/y6PADcfx/8WzFje8bk5yoSG3Jwqc9QYCJ+B/8NFIRBzjBV39wXiTJluOLrQ+4fZYmUmUK/DtiHjT8EMRRw4inBVXIVCQoN6BBwKxsdxskLDTIQr/cuBKseyTYDSO1IbKbuS9bFgaAbfT8UptLoZgUovqaAPYH3/jrCHP2LXu1HUpkr7ZHA5kB/XAokSOk7hEiNkvJj7MFXg5cccgJZCP5/BmiynBTK1juhYWko6TUKB0nwW6P91DejNbibCe6lI8ovWmGhOUMivkYTL1KtAlf6DwTCQ2J9ZkV8tcUAhoeE69r0V708vFmFjBaBjXwI3gqyZ0JBlxLaNgaAYcN/jFnAAeAdwL3Dck/xnAduCctOcMQZqxoBbkP/AvH3Pzv2Y4xhC6HsLZRtG+p5c2EeY0BD2+THvjIFcMaBKkBCIOW5w7r+BOFOmG25ZyqCXuv3KTKBehz1GxhcUyXwlwtvBpUCV16zQwAutr4Cr8nlmHCcrNGxFuCrfw0HaxrJxdzqA9WfB3zNhpWw+SKR7QFK51jFqxaPFf5ujcJ1eCIt+wbZ6IyR+SGBRmRSuXgTJsYnQoIr4aWBzoHg7gbTtzIbC0+UVTxIolgZ7Ae0fBXqyrNCQ+HUKB70Jsn6NJExGK43n1W/EP6pISCBQvoPiMLXCaPuIeFsLreucLaWNlD3K+v3xtgkNKWImRgPX4isVj4E/f1WVL9ekMrNVY6AiDDjuAe7ngP+yfza9z1KC+EwVSd4SMQZyy4D/byQNHwVK4XiW+q9ZvMNyHCil0aBAOrkKMqEhV6fLnDUGwmYgNKHhAW7kquTk3Jxe5G7LUSFWwVdVQjU0IGsSEV4B54CfAMXLCg1zEaaeKGsB9WxQnONAb7YcEVTRPTcVUZVkCQG8CHuRQL4tBJKKNqsFTS/NyvdX8V71Zpg5Xo8X6va4KXFmpI7YaeqZ8A2QH493hnb1aPgzYYqf+Hov68uAucGuQMdOBmcB2ZZAfiBseJPf94AvwTBQzLJCg/zS/3MSOCl10PasK/2RcZhefLQtX2YFK4N/AR2n8FmATIKLeJUYozR0nmYAH4BnwRpgMaC8dNyaQPYSuNGvdf8RXxJBmsr4KsU9qU9hjhsfDdygqQiwwuaYAf9ZwK34ez8C+I877msOEdUNzXGhzHVjoB8MuG25/j8Hl4MfgZTo4Od5QMh3t4JFAc9PpwaA2BTX/RHQSOF7O1zCUs/gvJsJDXk/g+a/MRAQA6rICIGYG8ON+qlAnOmHG/6hReu4G9yPRGp56P5kphZ4VUazdiEBLwJVfIsJDeljShEaluSAo4EEjOfAMJDYCFZU0b0PfBSva1sV5AVBMVuCHYq3EbgI8PD32/J9JMAclf5VEIHmppLsNiQg8YMXbh93Y8WKLanQ78O20l0e7A6ejLcV9gxQhfxeoHCJD58A9TIQD4kNZ0WChMo0IAnMLG9m+71M2FVsK5/lUuGJXyPjsHlZ/g1IyFDc7wCVh2izePvHLBP7Ays6z4q3aByo5UNxmMLVq+NIkJgJDQkTLCdErcelhAbePFunID6cN27qeUZSR9mqMRAaA24kt4FbgO6FqkBdCZYNzUvzxxioLgNuLq77/cADQP+FuNHD7cj6Z+AQwHuD+yXgGe6m6/DHh93N9suA9wi3OOD57fTMzruZ0JD3M2j+GwMBMRCa0HAnN+rnA+KnTFfcDpSDSptbv8wEan3YxWT4eoFMNyesHawQ7+uL0HB8gfSSIFWMVaFVxfY8ED+8WYsiHug+nGshWh0sDVRx5iHvJ5AcxLKQbUCg0nsFSLzYExwO3gW8SPt0WLiZAWXVRGkzqqW+mB/s8qaKvNId1bHpf4fyG1rLRSs+LQwKiUXe6fhH8WZJB8TrM7FcDLQV2GdBXQy0ICz8GoHhu7TgwFCKVxhWsVpXNFszBvLAgK8k/YlbHAKkf2bdwXK9PHhuPhoDlWXAfZ9rf35wHKCRw6Xu5/oamvt7V35uZ7YngSVSYeuyLbEifp/JbY8HExq6TqqtGQPGQD8ZCE1ouJWbtCqKOTc3G+WYDE7PSUH+i5/3ZHydk20etp1DALS7UkKDKsOq0Kp1XnmkxaUZ2ZawoQpx2i5gQxX+dAt9ev+m8X5aIbpVllV51nHXgtjcDwj6Dv3h4Sj67QwEFvIjiSxfeaHwX7NIwmzZ5AzQs2EpxIUn02ID4sPk9mjQWR9E0feanB4rfu4YcPNwTzwJ0BPMCw5PsVRlKnsfzl3JzGFjoG8MuDvj/8DTLI8EqwAaKzTMIjE/cfk/kq2OpVuKOLxraChSrns8mNDQ/cTaljFgDPSDgdCEhhu4QY/tR3kCOlSTWuZGNKFuFF2XIe9vbOv62AfsEeMSlqq0nwB2AoVsIIGK01OPhvRxSkvxl08HFljXEAXFUy+FQqbjtf+PBXZqiMOr3cPdL4hOfPenOLwnPz4mzjXdj7etZmfgMSbaRHA4HoFhUlpwoMfDG99FA2ndMjMG8saAQ3h1B4I34/sjzwY3GsyVt5KYv8ZA+Qy4YVzzvwE0Rvj3BL0r8I7g5gYLxWHrd0/fbUf4BDAY7AxooOipx0P3owPa9WRAhgAAQABJREFU+h++CGbGgDFgDPSbgdCEBlqd9QnCRjB3GGXRw2nRHJTmRXyk3tTNEEp8xZ0yFFy+3S1210YxoWEdouzdFa1zTcMzlMcucYi6KxYSMdSTQfH2iuNlF7PH+0/N7mBbZUvKl/LDnUOSOke8UHR+VWIX1tM2PRvKVz0qzIyBqRigF8NK9G54Li02aB3B4aJPGZ8z1QEWYAwEz4CjJ5efKO+B+B6pCtSVYMXgXTcHjYGKMuD+zXWvHj4SHWjQcAyj9PM1tHbPxvGO4B7qCOutx4NiBTu0woSG7ifWtowBY6AfDIQmNFzFzffDfpQnoEM7u9EdHJBTxVzhQVrSpKD9GTpxCnmowr52xgn1FFB40qPhzHg701oQXU044x+TuRZYm9ruI+h50JbaNYx1pa90ZSk/HKKI7ybJMJfhvFB388NH5mdEHH5UEmBLYyDLABddK70bfk/vhvaM4PAu4Utn49u2MZAfBvxQsz9zexwPuJf63nrbsxyUnzKYp8ZAOQy4IVzn2WETaqDQO1PKvGjAu6s7FiwE9D/JvMN06/GgCSbvBi+DJcHi4D3AO3DdzYSGup8Cc8AYaBwGQhMa9DKjMfYNYhoG4lT5Dd1UiVZlfN5eHO2P0DCctL8Ez4KtwLLgbNAONEwjscVY+RpIMNgWzAfoyuvjXcIyMfVwuAekhYt12ZYYcT2QcPEj8DD4BCQ9SzJ+rEAPip+zfyDHzXU78bK2IwHiZo3sDts2BrIM0LthWXo3PJ4WG9i+PBvPto2B/DHg5uBW+DswDqgipYrREWD2/JXFPDYGSmHA92B4lGs8nuBRx7gzgN4xUuYnAOcdwi0IRoFvQGsqAqvdejzszPYksERXHMf7S3oyya49NV4zoaHGhFt2xkAjMxCa0EBF0smnBjF3VvwwGRJ4gVTpV2VaFfOerD9Cg9JdFTwHlJcgkeFUkHqIsxVFa4EXQBJP18TxYABIbE9WtF8+pW1jNt4B2ifRgS6P0cogbQX82Bdx4+v3OWy+dETWTwYSKqbJhNumMVCQgTGoVvRsOAyM7xhCMWjnghEt0BjIJQPqyeB+ChBxveCgng5/AiNyWRxz2hjoEwNuH6711LuCm5XtNwENZTI/JPPfHevJb7rHg4/zOPH+keztWHb2gh3aPbzmWyY01Jxyy9AYaFwGVIETAjH3R26+vLQ0iukzYf5FDIU7eLsTD2+pkZfzkI+UfF5Ye7S52LsISAsMPR6Q2jkv67TA9WgpP9xKnKtvwTMgGVc/LUd/BGi1MzMG+sYAN9Y5uJkN79tRFtsYyBMD7ofcL68C7QBx11FJ8S26bXkqhflqDJTOgJ/sUe8JGvZwIngVICy4WTrS6LXHQwlDK0r3pgoxTWgok1TGw5gZA8ZAhoFEZJghE16nTXc2Ge8dRS0N0nrsx7CqNfwOyrRdnUgtNdsliajW/xGAh2gzmtuGUv8dSHShd0fLHiwPBpeBN8BfgGwwUNzFAeJE9Ai4CxSyFQnUsIsh4DFwA+CFvJv9gK0NgYSXB8G9YBJIm1o51kwHxOu84Kc/3dktxkxsbd4tpPuGjr0GLAUkpkgQy+ZLkJkxYAwYAz0x4L9KsTsxhO+Dj8El4ALuo2+zNDMGGogBJyFNPdVmB5+DC7nOJ7PE1OPBP08R4XTtq8dD9ATgud7CMRLi/OTSCBMtegbHpqEV0TKErZqE1GkpoUG2csfCfo0BY8AYKJ8BCQ2J2FB+KhU70p3GTVitIuW0YFfMi8om5Ki4ui9Bb633lc22vNTO47CkMl1eCrk/yh3ScQ2O10N/LFAFXC/NcWtFtADrbwKuUz+PhHo8aP0mMBAkJnH7RKB9rwEJFVq/GqRtTzamAAlSTwPF+ReYDqTtQDa0LwuuraIm8SgbP739aerI/7BO2c2MAWPAGCiXAT/BLiKsGwO41/hx6IirmhjP6Z5oZgw0OAO99ng4h//Cv7uTkB1a0X1vjbckNCRiQ42ztuyMAWOg0RgITWigYuZfThqo26XbKS4TL1rB2xA8VEU4W8kN3vHKOugupgGCl+TVHyDdZ4DEhsTuZuU7oJ4KMokLqqCrAi8xILHNWFHY4XGAxLP947AfxGHqcaDK/uWgFcg2ATruSG2k7ErWx4KhGaj1sJjJt2x8bUvcUB5qfUxMrSgql/ab1ZiB8VHbIpzcwTXO1rIzBqrIgJ9NH/Fa8y7557q6mu8HdN8zMwYamAH1eHA8X/1kqXuxTPXS7W1oRd1pkcggmBkDxoAx0G8GQhMajuWGTAXIzdDvkgWTgJ8oaBJlOjsYl8yRXhjwrXK0ONw0mYjCPPEBmruBczlVrwQJY+qRcCtI7EFW7gHpVjy9bNDF0g+7YOG/vvEay+HaSJkEhbtT21p9Fvw9E1bOpnpmjANXFjj4RcJOKxBuQVVkgC9VnBp/peJD1ncmq/Q1U8WcLWljoBYM6HnuJ9B7gaWe79+AS8APa5G75WEMhMVAb5NJ1t1bExrqfgrMAWOgcRgITWg4Kn4RmbVxKFZJ3D3gHdDMFYhlIYIXTF9xTyrwfVmqtX1tUCNTq9sa/D9Ww0eXzI2gXglDwewZJ1R5l3+JEKAWO16oo18BmY6b2a/1/rMcUTSU4txUVAkZE8EhQGLFKmAhUM719BeOUy+KbBkIin4PvgLWsi42amR8/vLZzOcwH0JwWKFG2Vs2xkANGXDrcGu8FrQDiQ7PgH1BqffHGvpqWRkD1WCgt6EV1cizT2ma0NAnuiyyMWAM9MRAaEIDFSn/8jFXT07nb5/bIy7XWvnzvWIez0Zt+6tWKtF7RQNcX0A3ginUqCfgyQIV86akhFqY/+CIzzl3XwAmaSpqB7BHwsLP4hhLxNsbsbwIcLzf/yLLkaCQaT6Fo8Er4DkwDCQ2ghWlfx/4KF7XtnpNLAhKtfWIqOMOKnLA6vH+5Yvst+AqMDAhat2GT2F+nRYb2J6CAHHp11E0ZxWytCSNgToz4ObgVvRbwP3OP/O/Y3kFWKPOjln2xkANGOhpaEUNsu85CxMaeubH9hoDxkAfGAhNaDgwfulQq3EDmRtCudSCo0pnMxvnVx+bHui6V6raim7fFg1SxVg4scbEae4D8l1JL8MMi3AMN3CFhI6RxBsP7gKJbcCKfE5EA82JcDh4F6hnwtIga38j4Fug484D04HEfsmKwu8EEgN0/GlAPUSeBoNAKXY9kZTHLEUiz0u48hlVZL8FV4kBTspQxIVrp/5ftH5J2EGPlX6Oq+ShJWsMVIMB9fJza4OrAPdRLzogyDrEUFeo11U1nLA0jQFjoIsBExq6uLA1Y8AY6CcDoQkN+8QvGtkx6/0sZgiHu5so22eARv2mtUG8Vb46N/McfBm1FhUXksrWd8RZlKEWA6LoQxibocasbUh+qnSPZPFDQOOyex1IgEhsC1ZUcX8cpCvvm7KtYznfkYY9JLYYKwqn6/BUpiERirs9UK+F50FiM7KirvTZa+cCwpTej0FvNhcRJHIwNrqoyYdJoNaiTlGHmm3Hd9HAtenJ8EzyH0iWhL00Phq4UbPxYeVtJgb8fEb7c0vj3ucFh3aW14GNge5NZsaAMVB9BkxoqD7HloMx0DQMhCY0/Cp+wVCFrMHM/SQu25YNVrC+FudHHMBUzNP0KjScQc8HxQW7gFrbHmSovJfqyNity6a6974E5iBMvRQmA/VkkBCQtuXZ0LF/TAfG64xJjl4tEJ4OOoENHa90ejLNHaF46i3Rmx1KhFLS/Jh41/SWmO2vHgN/Zx4O5mjYmx4OnyZCQ9ey9ebxHfNzVM8BS9kYqDsDblVuV5cC3lG86EBvMHc8GF5318wBY6CxGTChobHPr5XOGKgpA6EJDT+PXyqWqSkLNcnMTUvZvgK00DS93UbT/eTXeujV8CH7mFFxEj0gHoMtFjU3hCFfMd+0K2eHSKIhMEeMi/ddxrLQsAV1+VWl/lSQNZVHkK0D9vZr3X82Z1PH7xIHr8Zyp3g9vVBPBsXbKx1YZF3zPiT5FokSTc8OpXdBsQgWXjsGvoyiWdujtvMQHCZ1CQ0aZtQ6ASFi39p5YjkZA/ViwHFPcrsC5qPxggMT5frJlRne5dLDy+rloOVrDDQaAyY0NNoZtfIYA3VkIDShgcqUf5kYUUdOqpg10xN0tIpTh25qU4+ViVszKWT3ClTXXA17so84Ai1bdbHVyVX5/6Z77ltf2BG+J2KDy/ZkSEe9jw0Nf0gPnRjGttI8E8hOAdpeWxspU48ChSc9GhRf2+uDtF3NBi/eBed8SMfTkAsNiTg/HVhgXf875XNUgX0WVCcGmCxyGf4nY7r/V1o1OaqZMdBEDGgknTsJvA+4Tzm0OIco6lZqIhKsqMZAtRkwoaHaDFv6xkATMRCa0LBd/AKxSmOeA7dBXD5aaJrefOX5biZ77F6BanNPEjagowL91zqyNJi8mZeh25wGGjLxBVMl8L85lmETv3mTORuPJkxDF4RfgMQYauFFgOtZSjCgN0T0MPgE8MLsTd2AeVmOngVbgWXB2YBeE9HfQGISZuSLhIttwXzgQKB4l4DE1MPhHpAVLpYiTAJCod4TBHfajqwp3hqdIbYSDAMIDtvSw+Gt+P9yezCOmSPGQE0ZcAO5TdHry90IJgKJDi+A34K5a+qKZWYMNB4DJjQ03jm1EhkDdWMgNKFhy/ilYc26MVLVjDWhlW+Nubuq2eQj8ZkREz5dJmqZTAWqm9iwdtSiz1l+SzHmrXNR/kn+j6Z8kECkingxPJmKq9WNwTtA8dXz4CmwMkjbqmxoWEOSpsSDU0G2W/BahPEy3RlP/93jATR22p6sKR0N+0jbdmwoXGn0ZCezU0II16lZiAygNLUybGK5v9s5CvH0mE81Z8DNxa2NXmeOe6gXHOi55W4F3PP0CUEzY8AY6CMDJjT0kTCLbgwYA8UZCE1ooNXXvyysV9zlvO9xZ1BGWsO7fb0g74Uq1/89OND9MfW5y+u6JoA8qtxEK3ic5kZQBX3Fwmm6/djNfvcvoOEJxUyCyRzFdsbh87BcAgzqJR4v1tEiIC0w9HJISbunJdZH4IiSYlskY8AYMAaCYsCtwH34XPAp0H35M3A+yIq7QXltzhgDgTFgQkNgJ8TcMQbyzEBoQgNj0P0LwiZ5JrVn392IuIyH9hyvKfZOQ8+FZ4Ywf8An9Gr4GgxjnRr025ReFd8Q7Gqc+EdxR9xh8fmU2JDnFjQNq3gTDC5eVttjDBgDxkDoDOg+7LYBuierh4NEh1fBaLBw6N6bf8ZAnRkwoaHOJ8CyNwYaiYHQhIa14peCLRqJ5KnL4p6mnK9MHd6UIZpPwB3A5y6PB1oHdHsNxjQfwtdAvQiKmDs8vm5vY5nHijpjnqOxICTei3BtwaUywNcpDmmPWv/HnA6/YfxOKMJdqe5bPGOgAgy4Obkn7w8ei+/RPF/cI3GYeoeZGQPGQHcGTGjozodtGQPGQD8YCE1oYBJI3/rQ4BUex6fpfDnX6se5a6RD/8GkAJOpCTGkJLovwILtiU+jevbLT0Sml9i7QN4qdctTtvN6Lp/tzRMDKGNzZiZZHcfcDntrjoc8lcN8NQYqx4BbjHvzseA1oHu1ejvcCXYBPX1BqHIuWErGQPgMmNAQ/jkyD42B3DAQmtAwgge+XgB2yg2DZTnqZqWM48EVZR3eeActwBCKCRRLQsP/s3cecJIU9RfvhWOPnHM8cpIskrNKRgUFFBUlSVAUEAEVCYIJVKKiIGIEUUBEQIIkyTlnyZLT8SccHFz9v6+3a693bmZ3djZMd8/7fT5vK3R1d9Xr2e6q17+q1qC3pIZjRs/v9yrCmUraCFe7Agz0LBo59tEasSHw1YonEBy+fLYXk6zAVXYTWmdA6zYEvvATXsju2e8Q/hXgTdnvejutn9J7moFyMGChoRzXybU0A6VgoGhCwwrZQ3+XUrA3pEqGM2nr22CWIR2mOjt/iaawuGLZLbDWQeALE6mr7pxlb43rX14GxifJ7HzR5RimULw9peDQ/RDbdqR1aHw2M9CpDKRfgtqU+zWif6A/lL7oeI3wNMCi1NpuMwMdxYCFho663G6sGRhZBoomNCydPej3HNlmF+HoYZOsrXLLt1WKgbAT13YieAAsWKmmuTGlY+CtJJkPT4aTERzerSM43DUhGbtN6RrlCpuBYWdAU97CDuB8gIddKjrI4+EksC6wKDfsnPuABWTAQkMBL4qrZAbKykDRhIZFs4f7vmUltPl6q9MSHgMsUmWrHgNhK66t3HGfBP0sJFm9lrtFxWSAH+M4Foc8HcHh/TqCw00TkjF89cdmBswA9+xZAZ6V6RoO7xNqSudT4FiwmhkyAxVmwEJDhS+um2YGRpuBogkNC2QP9G+ONhHtOV/4btbeldtzfp91ZBkIG3B98WBP5wH7Go8s2T56kwzgwbA0gsNZCA6T6ggOZzR5GBczAx3CQJibe/je4BqgaXESHR4BWljyQx1CgpvZOQxYaOica+2WmoERZ6BoQsNc2UOczwWW1pan5ss2V3u51QcWQAwnNlfepcrHQFiV6/sieB2sV776u8ZVZYA1GlZEbDi/r9jQPQkXq2mq2ma3ywwMjYH0mX0A9/JbgAQH4X5wGGjyuT+0GnhvMzDCDFhoGGGCfXgz0EkMFE1oYGHE9MF9eEkvwvrMh3hrqiTh63LJOs21IVxIm18F0zZX3qXKx0DQ2iNyu9Xin1uUr/6ucZUZ4CsUayA2/EtTKljL4RdVbqvbZgaGj4GwOPfzQ8AdIIoO9xA/FHDPt5mBUjJgoaGUl82VNgPFZKBoQgOD7fSB/cNi0tVvrVKRgbkf788HmhcbwrZZm7/Q79G9seQMhIW5zg+CiWCXkjfG1a8gA4cnCbctmxkwA4NnQOvwpFMh7yaMosO9xI8EKw3+eN7DDLSNAQsNbaPeJzYD1WOgaEIDHd30If2zklHdKzI8nHTzqYHu0LzYEMbQ5mfATSVrs6s7aAaCpgbdkP3Gjxj07t7BDJgBM2AGCs5AWIZ7vNZfyns6PEr6GLA2wPHRZgYKy4CFhsJeGlfMDJSPgYIJDSIwfePL56RKY31EhjjfeZBiQ1wUcvXStNoVbZGB9BNq5/E711uvM0B3iwfybmagLQww/2dBplv8DOz9fJLM0JZK+KRmoBQMhMW4xx8IrgdxIcnniJ8CPg68HkoprmNHVdJCQ0ddbjfWDIwsA0UUGljfIJw6ss0etqPXFRmi2PBg054NYR7arO92/27YauYDFZiBIM+d44HEhv8APB1sZqAcDLCWwwXxHkf8FRaVPOKNJJmzHLV3Lc1AuxiQo2PYC1wK3gO6/2uR4D+C7YBFu3ZdGp83z4CFhjwbjpsBMzAkBoooNGhhxDIMuPsVGWJHfBBigzobE4A77EP6SZdp57TTqTUbngArlKnmrmvnMtDzacyxuJ7l0f02C0me9E6SLNq5zLjlZqBZBsKs3PM/D84FbwGJDlos+HzwZWDxuVkqXW64GbDQMNyM+nhmoIMZKKLQILfCswp+TZoSGWJHvEZsYI5mPQtr0m51Ng6pt9V5VWUgbMw1fwXwvxg+UdVWul3VYYAf68xMmzgWb4Y34j1ucqgvV3SfyfZVqtNit8QMjCQD6XS6T3L/5wVL+ixQP0CfvZa32wFgyZE8u49tBmoYsNBQQ4iTZsAMtM5AEYWGJ3iwMoe90HYdtePVw5iat3r5N3x943+jrPYBV4EGFviEfXgSTN2ggLMryUBYgmt+P9Ac3oMr2UQ3qnIMvJYksyI2HILI8PxkoaHPfe/SCcmYj1Wu4W6QGRgxBtLFoTfiOXAceBxIdBDuAz8Aa4CuETu9D2wGksRCg38FZsAMDBsDRRQaHuZBeuGwtXBkDrQqk+xfn4PPWN6VTDOg2HA7ZWbr+eQl00KSlRpXKexM29Wp2LFxGW+pJgNhFq77xdn1/wPhtNVsp1tVNQYeSZKxeDDsgSfDw/UEB/LvZrs+3+sBUtUuvtszwgyEFXkWHApuy54N6h88C34FtgR+TozwFejAw1to6MCL7iabgZFioIhCwz08PC8fqQYP43GbEhuaFxlUM61AHZ4GdwxjPX2o0jAgT5bwM6DOJA/7MG9pqu6KdjwDhyfJVCwMuR3Cwk31BAfyDux4kkyAGWiZgbAgz4R9QH4xybdI/x3sClhU2mYGhsyAhYYhU+gDmAEzEBkootAg5f4/sYIFD/sVGwYnMsSWhv1ovwaam8Ych53GQNpp1FdIJDp5rnunXf4KtHdiMmZDplX8E0yKooMWi6xA09wEM1AABsLMPBu2B1pEWmv8qM+gqXcSqL8DVihAJV2FcjJgoaGc1821NgOFZKCIQsP1PCRvKiRb9SvVKzbcmZtG0ZrIoBOEGQFTLMJV9U/n3M5gIKzHb+AloDdWO3VGm93KqjGAh8MyTJs4Hi+H0/lusd+4Vu0Cuz0FYCD1hFuf58Qx4CEg0UHQek+/BFuDGQpQUVehHAxYaCjHdXItzUApGCii0HAlD8U7S8He5Er2ERtaFxniAcP34EAdBRaFsnUuA2EcvwGm0aS/hVMIp+1cLtxyM2AGzIAZGJiBsBTPigPAFeA9oL6EPOQuA/uDZQc+hkt0MAMWGjr44rvpZmC4GSii0KAF8R4c7oaOwvF6xYbmFn7sr0apW6S8Gq6pKbUn6a1q8mKS9SkTLaK5csyoCXcjrQXZRso0/3rvkTp45x43/fTZqfwW1Fm8CyzduVy45VVnAK+HefGA2AesUfW2un1mYOQZSPsS2/Lc0DNEU/H0HBGeAPJ2+ATAi9JmBnoZsNDQS4UjZsAMDJWBIgoNWtjo8aE2rE37p2IDI35Egv6+LtFM7cJ34UEdgk2y0p8jJJ0c3WBvdcwngOnrbN+cPO17fp1tw5X1GAe6ebgO5uPUMhA+yyV8AzAWCyMpGNWe2GkzMGoMMMXi1slrOnTfjuCwG0vs17unjVqdfCIzUB0GtHZD4KVAuBLkvR3+TfqbwGs7VOdit9oSCw2tMuf9zIAZmIKBIgoNf+Fh99wUNS1PxiJUdeGhVzfMBA+vJMl713Ksg4FEhP6EhsPYXu9rHXOR/3y2r4UGiCivhSX5CdwO+B2E3wIPwMp7MV3zOgywYORjUWiYHHa/huBw3IRkrL156nDmLDPQGgNpH+OTPEd+BZ4Ceq4I/wOngx3AHK0d23uVmAELDSW+eK66GSgaA0UUGn7Hw00eAbbkhe8lyao8+JNJ4DigeCOPhhvYxhuJKUzigqZgPAwGEhq0YJQ6Ft0gb9ORqJefL/MYibxHg44ROyma1rEiWA5MDeqZymjax5z1NmZ5sxCuCTTHtPY4+fNp26pgPpC3hUgwWO/XVGZtMHu/pdq2MYzlZ3ASUIfwfvChtlXFJzYDw8wAgsKHERiunCwyjA2T4+kXLC5ngcnP3Jok0wzzqX04M9DhDASez+kaDpcQvgP0jPkA3ASOADwXtfCkreIMWGio+AV288zAaDJQRKHh1zzM3hpNEop7ri0XS5K13kuSC+6ljmOAhIaj6tR3NvLeB7WDzt3Jw90+GQceAAMJDetQRsf5G4g2LRGd/2nQ3+C7VmjYkfKq76bg5SyutMpJUMgbnZjkNaDtOr+EEU0FicbgOjkBTAQqI7wAtgXR4vm2JiMeS+XOARIocA3t3VeCyOogb+LuThCPr/CPQPsW0MJ2VJV2hrfBbgWsoKtkBlpmADFhWbwbTuTzmOMnCw150WHsC5T5MW5eS7R8Eu9oBsxAAwbStYF4doefAQnaPA9T6JlD/yDsARZtsLOzy82AhYZyXz/X3gwUioEiCg0n8gBDRbf1MBAOgg8e8uO3IK3Bbz2hge9pJ8/0lO/9qzf3b4IvZznNCA0qquPrPJ9WAuN6pIP/9dJU4z+NhAYJAswJTRYHnwEMjJP/gGj7E9H1Vjgr+DC4DWjQLy8HGZ/tSkWGrxNKVEGASc4Dk8C6QBaFhteJs55B6rnwS0K1RXU4HawMPg7GAwkP0XTeF8F9YAcwM5BgIYHkbFBQU0cvfdtEG8OfwUwFrairZQZaYuD5JJkBL4evsHbDXfUFh9TL4dJ3ksSDnpYY9k5moBkGwkI8X3YDfwV4nPYKD48Q/wX4JJilmSO5TOEZsNBQ+EvkCpqB8jBQRKGBQWX6EBtTHhpHsqb6/nVgEPwe7ovpoPnoOmfTIPo3uXxxp/IajEdrVmjQvjeD54AG7Bqofw8MZI9RQPtFiwN/iQx5+wOJd3MZrxH/Uy6tqKYuXA5WAPMCCREng7zJ00JeFlG0iOf7dq7Q/MRVf4kP0+TyJR4whum1I4ipnESIvH2XhPI17aOgFmhX+CmgnuFhsFpBK+pqmYEhMTAxGbM2gsPv8HJ4u1Z0IL/2HjKkc3lnM2AGGjGg6RNhDXAowPuwd1FJPA7DdeAwwDPc0ywaMVjwfAsNLV6g+Gasxd29mxkwA6PEQByEyl3elnS9BQk/SZKuj/RDBi6OySW57RIGFgG4Nw7aNHXh80Bv9XlLnlwFjgKt2q01O0oc6AYa+I8D8ia4DOTtehIfBfeA5YHu3xeAvE0gcSlYOZ9J/K5c+lni4k/lNO0i2ktEEHB6TV4U8rRYAuyZQ/QQqD0HRYpiXbSr6wBqszWYHdxAB+8Q4GdeUS6R6zEsDEyTvH99d/Lezm8m782Pqvb1kIT74oFJPxLjDs2AGRhJBroQ/rt4kdH1fbA+Z5oDbANOAXOCwwGCQ/IKz6G/g33A0qRtZqDSDIypdOvcODNQHQY0gJTpjbUGibYkwTUxXehxnjpkrECe8uNgXQPzbwN5M2wHomnKwTiggfRV4EHQyB5mw+1gXfBvMAm0avJYyJuEDJkGwkulsSTRwL+RLZxt0PSHWnuRDAkG0+U2PJeLx2jt8RmX9LGFSKk++/XJ7Uk8SjBjnfyCZXX9k86cPC/OAD8Am5P+Mh3B/xK3mYHKMMCN7HWcok6gQScwrWL1SclUc06bvPevyjTQDTEDpWKg6/+o7gUZCIKe2R8HHwMbg08ALOglwxUZruTZpLTNDJgBM2AGKsyAHhBCgSz9ljMDwbBAgSpVgKq8rUEwvKwnASFvB5LgLXavbUpMA+n+sEtv6fqRvbL9dVx5mDTzRv8xytWbOrEK+Xk7koTqNhZ8KIszIO5jXaQ2ARJQ5Nmg8luAWvsdGRICZDsClas935vknQzydhKJ/O/+YtISRCRuldwC3IVvADw0tKhq2BeIT5sZ6HgGWEByRf4xFux4IkyAGRgVBtLn0ao8g74F8CxMn0s8p9OpfnghhV+Dz4J5R6U6PkkzDNxIIcFmBsyAGRgyA0UUGr6aPYQWH3LrKnWAn+mtPQ/o/RgQ6xOHvSaPg8N6U40jD7Dp/Mabe7csTUyeJGcCeQs8Du4FAw3CWxEa1KY3wNkgb1uSoK29YoPEjt/mCxCXl4E8Gv6a5Q9FaPg+x9D5ts+OFYM9iTwIJIiUzALeIul8WdqVzqPVtBCbGehYBljXYf+etR3SBSSvxBviS9xAdB+xmQEzMCoMhG6eR+uDw8BVYALQM0rgWRt+CXYA845KdXySegxYaKjHivPMgBloiYEiCg278ZDRQ2f5llpU3Z00/Qtevi1uvpk1U0KABuFrZun+gmaEBq2bcCt4AcyZHUyuj5wzOT5LNwpaERp0LN50pFMzDiVcFHwKSNi4AkT7IZEPwOFgMbAauByMB6sD2Y5A9VxFiZy9SXwgj4ZZKaM2/w8cBBikp1/r0L61IghZZTGt0xD2A7zETb0bvk5Ins0MdB4DLCL5t9pFJMl7k4Uk/zghGbMZ/+hTdx4rbrEZaCcD6Wc08V4MR4FrAf2ZXuGBPkuv8DBPO2vZYee20NBhF9zNNQMjyUARhYbPZw8aDSZtkxnIhIa9n4YfeTVo4b+tAAsu9X4GcnLpKWPNCA1Hs5sG69vV7H4K6UlA8y4bWatCgwa+Ehs0qNe5hZvBMiCa2v5dEMtMJK6H4Vog2lCEBh1jSXAVUDtVB4kOuHUmc4OSW6Bt4T+AdqWh2mozAx3FAB4MKyMq3Dul2DA2ZHnPU+Y4rfvQUcS4sWagMAyE6XlGfRQcBa4F7wE9t4T7wS8Az/owX2GqXL2KWGio3jV1i8xA2xgootDwaR4ieqis2zZWCn3iwOA65ec4qqmH7XKFrm7zlZPgsASYt59dVGZxMFM/ZYa6SV4iqkfFLPVu0NoNTItJPRy+STh1xRrp5piBARlASPjIe8nYk/FmeKWR6IAg8RDbD53Q40E14DFdwAyYgZFgIBUePsaz6mhwHcgLDw+TPhV8ASw8Emfv0GNaaOjQC+9mm4GRYKCIQsOWPDQkNHx0JBpcjWOGv8KPHrgVHBBX4woVtxX6zYSrgf7H+LJI+HBx6+qamYGRY+A+PrPL4pCfQlA4B0zoR3Q4Y+Rq4SObATPQPAOp8LAJz60jgZ5jaIHps0zPs8fBGWBPsEbzx3TJGgYsNNQQ4qQZMAOtM1BEoUEPET00NC3AVpeBwFv99AF7Ud3NzjQD/TKQrgS+O7+hVwFrX4TjwUh6ifRbG280A+1mgLlos+LpsDueDlchOkyqFR2Ys+U54u2+SD6/GZiCAS2MnS4uyRpPgXWbUo899R+FF8H54GCwPmBaRrMmb7/wNfA3cDrYFpDXEWahoSMusxtpBkaHgSIKDetwQ9dDgikUtsYMhCMynngA2sxAKwwEBk/hz9nv6GnCT7RyFO9jBqrEwDtJsjBiw8G59Rwuo31dVWqj22IGqslAmIbn2DZA4vl54HkQhQfWdgo7N9fucBZltRbWKeBYoCmH54DcfSCwflM4BDCNNewBED0qYRYaKnEZ3QgzUAwGiig0rMYNWw8GFoW0NWYgTAtHjwINELWugM0MtMhA2JTf0GNA/3fnggVaPJB3MwOVYuD1JJmtUg1yY8xAxzEQFuWZthP4OWhiqqCef6k3RE6U0H7p85FnpUyLUaaLU/6L8CDwMrgS5ISInpIl/GuhoYQXzVU2A0VloIhCw/LcrDXgwbXb1j8DYfOMq2P6L+etZmAgBtLPjP2I35Pe+rwB9gWd4io6EDnebgYGZID1Ho7BC+JhcPqEZOyWjyRJVd5wDth2FzAD5WMgzMEz7kNAi0zXWO2zL4yhnNbF2qunYLiY+K0ge0aGZYjruYk3RbTSejxYaIiX0KEZMANDZqCIQsNi3KwlNDA/zjYwA+kcwvfhy59kG5gslxiQgbAiv6Ubsv/Buwk3HHAXFzADHc5Az3SL3s9kZp/L7B6P6PBnBIjtnk2SQcwP73Ay3XwzMCoMhF2z59x4wktBzoOhtgJh/6zsSoRaUFl91K36lgo3kXd4T16pPR4sNPS9sE6ZATMwBAaKKDTIJU03cdzRbAMzkM6zl9sei6hXZo7gwM12iRFkIF0schd+T1pMS/+Lmk7hL5yMIOM+dLkZ4NXmNIgK99UuIjk53f026z6ci+iw06tJMku5W+vam4EqMJB+8lnC+p7gd2C/KVuVPgv3ZZu+bnFsz/bU24/1YTV9NW+ph8NRWZkyezxYaMhfVsfNgBkYEgNFFBrohKWDm8OH1LKO2jnsmHH2o1yzv028v3UudmM735+ewvTw/DL4YRY2+j61yun4R4PvgI+BWluIjJ3q4DO1BeukdXztq+Or/MxgINuTAjVvGZIPkXcmGDPQzt5ey0DQ/yKdq/Buhp8SzlpbymkzYAaShMVypkNI2AFB4Wzw5mSRYQpPh3fZdhFfudiNBzALytnMgBkoHgP6xHr6CWiJCogN0VJR4pKYmhymUyfoV5Xe4+FG2iTYzIAZMANDZqCIQkM3N2q9Rf3xkFvXUQdIV0TWFIo1aPZa4CXQaCEz1nZI4Dg5H+RNi0o+CrTtFsCcw+QtoPJ5Y3Gl5AmgcnhSJLz5TuP/IMwP6HE3TPNVLg9cFfu1Rdiq404EuPEnrMmWMNhNNgaN7HNs0DkkTNTaFWTYQ6aWlabT6edUteI2/KaLXn2VMH+dmz6SC5qBTmDg8SSZFtHhE3g5/B7R4bV+RIeJCA57dwInbqMZKAcDYRzPt3OB+lO/BvP1rXe4irzja/KWJE/Px3WBPCCGweOh7xlGMWWhYRTJ9qnMQNUZKKDQIMrDB+CEqpM/vO1LFx5CXJj0AF9iu4djy8ugns1F5vNAg/JaoeG75L0H5AUg07ziK4HEhrEg2r+JvANWzzI06NRAXseUuBDtD0QeB/JsyGOBWKBByErOCQ/5ZO1s+0yEp4BXwOJZXgx07oPBBKDz1xMadBzVV3WwtcxAWA+KbwXwHPidhS1bPpR3NAMdwgD/MNNMSMZs+l4y9tcIDi/Uig6IEbd1CBVuphkoOAPhIzzX3gCXg2XqVzb92sSBfbdp2kV4BrCw5HB4PPQ9+iinLDSMMuE+nRmoMgNFFRqkBp9aZeJHpm1h+yS5gEFg1ySOP3+Dc0hcuAY8DPJCw9SkNcD/BcjbziQ0gF8/y9QUBokAZ2XpGEiIeBlcFDMIJXicnUs3E5UQovNdWlN4QdJq18m5fHlgqJOufL5l3VBoYFPCwDj5qSK2oTCQzln9IlSrU8V1CpeBFYZyRO9rBjqFgcOTZKqJyZgN8GI4HuHhSbwd3iW+R6e03+00A8VlIPBiJfXYo38TfgW+AGpfbFD9cB74+eR2pOLCHeT9pCdvODweJh+9DTELDW0g3ac0A1VloKhCA2/mw5+qSvrItmu155JkXQbeYa0659mdPNT6ZBzQwDsvNJCsazxU04H83NlWFPvUM0CCQN5mIyGvgSgsSHiYCA4CEjFUnyVAF+jP5H0goWHPOoXuJe/OXP68xCWabADk2aD9jgL17Egy1fZp62103mAZSDtlh0G5RMEPwK/BfIM9isubgU5mgJul7o1NGVMxltF0jPFJMntTO7iQGTADg2AgaNruxuC74ELwCqBPEV4Af5x8oPRrFe+StwOYBiAwBPVZM1FiqB4Pk8/UppiFhjYR79OagSoyUFSh4Ulu2syRsw2egS76oYdq8PcgyA+ql+RY5KcLPeqw/QkNPDzThR7/RCjvhW+BgWw/Cmigz9vu1Fbjr9ISAl7M4kpfBxYDjUwPa5Vj4NrH1LmWcMGX4uraQELDuuyl465Sd29ntshAmB9azwASG94CPwCztHgw72YGzEAdBpgXtoS8H3qmXXS/jzfE1cQPmJCMXapOcWeZATMwZAZS771leJ59GRzQ93CBFxrpFAv6JOG/IOfVN1SPh75nakPKQkMbSPcpzUBVGSiq0KBB8sVVJX0E27UAx2Yw/YUTCAhDnCqgQfhNQN4J0foTGni4pqKEBubPgHXiTg3CDcmnL5xcltu+G3HtfynQIF8PYtWHwWhyF5CYUc+mIvNRoAUgl80K6K3f94COJ6+EeqY2avvR9TaSt2C2HXdI2/AzEFaEfqbNpL+7Vwi/CfJC1/Cf0kc0Ax3CwDvJmI1q13aIadZ4eAhPh2OYkrH+YDwkOoQ6N9MMjBAD+px4qOORNFSPhxGqbvOHtdDQPFcuaQbMwAAMFFVo0Hy3qwaouzdPycCmZGmwvSHBWUBvmSUSHAmeB/npDv0JDRrsy5YGfwBMxUh2BPXsk2S+DW4Ds+UKaC2HDwPcEfvYKaRUx0/0ye2b2IikjqlyV4GnwOPgX+BZUM8GEhrUIXgf/Kjezs4bLgbCBly2GwDXLjwNdgF1OmPDdT4fxwx0BANdiAnfR1x4NgoM9UN94aL7bNZ92IUbqIRnmxkwA6POwFA8Hka9srUntNBQy4jTZsAMtMxAUYWG6xmc3Nxyqzp3xz1pugbnfDUizAmYW3i7BukaYP8VaHvE88TlWaD0MqCRzcoGDfrPr1NgL/IQM1JPBgkLzdj6FFIdvz1AYdXpeKDzHgw0//8CcC+oZwMJDdqHeZTJX+rt7LzhZiB8ist8P5Dg8AhgSo0Fh+Fm2cfrOAa6EBE+jOhwJJ4Md9QXG8aGmK8ylP9Ix7HkBpuBtjPQqsdD2yt+IzUQbGbADJiBITNQVKEBF/zQaEA55EZX+AA70DYN4rfqaWPYHCcAeSMorz/w1jldYGxPwlVBrd1OxhM1mQeR1jFPB/WmQciT4vOg1uTJoP32rt1Qk65dNFLpJ8HvasrF5EBCw4wU1HnlUWEbFQYkLISdwaMA7sPDgKkrFhxGhX6fpPIMoAAvhJCwN8LCxXgyTIgCQ014UeWJcAPNQKkY6M/joe0NsdDQ9kvgCpiB6jBQVKGBt9jh8erQPGotWZczaTD9zclnTBfn0yCv1oOgdurELOwjz4erJ++bxpblr44pz4JoXyGivB/GjDrhcVmZj9VsO4u0xA+t2dDIbmLD32s2Ipqk59ysJj8mBxIaVsv2PzTu4HC0GAhcm3RBrf8S6rco4WE3UDutZrQq5POYgcox8HySzMDikFuxUORJ4NEoNuD9cHjlGusGmYHSM9DI46HtDbPQ0PZL4AqYgeowUFSh4c8MQl6oDs2j1pJpOdOb4LTJZ0y/8cwbrXS9Bg3Wo9UKDco/CUhA+BFYDuDunk6vmEDIYn+pzc3f1wEL/iXfARIw8tiVtGwZoLrcBz4DFgb7g/dArn7pWg1XkbcRiPZ5IhIj9gHjwKeBzvcL0MgGEhp2Yke1bb1GB3D+SDOQCg5f4jI8BCQ4aA2HfcH0I31mH98MdBoD3LQX1zSLZtudLTh5IItKrnNlz+eCm93V5cyAGagOAxYaqnMt3RIz0HYGiio0MBANqptt8Aycxy639N0tsM5COk/+NcIls231hAa9Yf4pkBigQblwL1gXRNM0i7itXnhHLEi4Abg/V17XVF+FmApE24uIjrNDzCDU4oG/BxIbtO0ZcDKYDjSygYSGn7Djy0DHtrWVgVT84nqHOwHXN7wIDgHNrvPR1tr75GagagzoM5lMvfggekEQHw/+jlCxD9uWrlp73R4zYAYaMmChoSE13mAGzMBgGSiq0HA8g44PBtsYl08Z0NoIGpyv3pePgIeCxJuAh0GYqe+2KVLTksOCkom8F4bD5uUgS4G8wNDMcRFI0i9fdDVTuJ8yEigYzKYeGP0U86bRZyCwnkjvVyokhH0fzDn69fAZzUDnMsD0ihUniwyTF5PM5T3NwpK/pdzn3+pZmLdzyXLLzUC1GbDQUO3r69aZgVFloKhCA3P/0zedGvDaBs/AWexyzpS7pV8CwEsg4PUQhjp4n/Lwxc3RFIwngH9Phb1GYWN+k//O/u/fJPwZWKCw1XXFzEDFGEBE+CzCwmV4MryTExh6v2CRz0N0uBdvh+PwdtjybHuJVeyX4OZ0OAMWGjr8B+Dmm4HhZKCoQsOh2YBjjuFsbAcdS+shMFhLvQhqmh2OzLj9Xs2GqiY1peJxsH1VG1itdoU1+H3+A0gQYwpPOAPIu8ZmBszAKDDAwinTTUjGbIqQcCyCwp0ID5PyIkNtnDKa5mYzA2agGgxYaKjGdXQrzEAhGCiq0MCigalHw0KFYKmcldDaB1+YsuryZOgdyG095fbK5axCi7S+g61UDIQV+J0ygEnFBq3jcBHA68FmBszAaDJAJ2EuvB125EsWp4LH6wgNWofHZgbMQDUYsNBQjevoVpiBQjBQVKFhTwYVGlwsUwiWKlcJLboXHgTjzXHlLm7FGhQW5Dd6bPZb1T3hNrAj8KKeFbvSbk45GMi+ZrEHng5n481wA9MnNm+m5ppioUUnb02SaZop7zJmwAy0hQELDW2h3Sc1A9VkoKhCA2/iU6Fh1WrSXoRWBVYST4WGhwm9+F4RLonr0A8DqTh2IL9VvkCS3hueJDwYzNHPTt5kBsxAMRjowhvi2h5viO63Ca/AS+JIpmh8lFV6ZyxGFV0LM2AGYMBCg38GZsAMDBsDRRUats0GE+sPW0t9oDoMBN5EsdZXz6r/+jKDzQwUnIHA29DwRSDPBnk4vANOBx8ueMVdPTPQsQy8miSz1E65mJzunohnxM2sCfFTxIdPvpEkFr479pfihheAAQsNBbgIroIZqAoDRRUaPp4NIraoCtHFbUfYJeP6fEK7oxf3QrlmUzAQ1uY3exbQopESHW4B+j1bNJuCK2eYgfYygIhwNNMt5M1Q90sW+XyEh/vxgPg14sPOryfJbO2tuc9uBjqKAQsNHXW53VgzMLIMFFVo0ABCAwd/KWBkr3929PC9jO9fjcrpfBIzMKwMhHn5/X4HaDqF7huvgeMA04NsZsAMFIWB+5Kke2IyZm0Eh4PAP8FreYGhXhzB4cnH/WniolxC16P6DFhoqP41dgvNwKgxUFShYUUGCRow8HbSNjoMBESGlHMGaDYzUEYG5JETtgEXg0nZ7/nfhJ8G+sypzQyYgQIxcHiSTIWnw4p4LuyDF8NZCA3/qyc2vJ0kCxSo2q6KGagyAxYaqnx13TYzMMoMFFVoWCwbJOw7ynx08OnCVHD+u4z3n3QwEW56JRgIuof8GLyU/aafJfw+GFeJ5rkRZqCiDPBVi8U0ZaJn6kT3DfJ+aKapWlSS/b4GdkG8WJZ9+JSzzQyYgUEyYKFhkIS5uBkwA40ZKKrQMHc2OPh246p7y/AzkL4RPjPj/qjhP76PaAZGm4Ewlt/zTuDa7HctT4dLAdOyQvdo18bnMwNmYGQYQJg4ta83RPeriBQXgu+8k4zZ2F+3GBnefdTKMWChoXKX1A0yA+1joKhCw/TZoOAH7aOmU88sF/Pwt4z/QzuVBbe7igyE5fld/xxEL4eXs/SHqthat8kMdBIDTL34c1+hoXbRye73KXMHgsQv5TExIRmrNVzs9dBJPxK3tRkGLDQ0w5LLmAEz0BQDBRUaVPfwATihqVa40DAzkH5CUF+h0DoZTbmtDnMFfDgzMIIMyJMh9Wi4hFD3Gf3O6VyF3cBMI3hiH9oMmIERYuDNJJkHAeF4xIS78GL4oH/RIYoQ3a9S/i/jk2T2EaqWD2sGysaAhYayXTHX1wwUmIEiCw18Tjv8psDcVbxq6WDsIq6BBmH7Vbyxbl7HMhAW4fd9BHgq+60zXtF9J6zbsZS44Wag5AzgsjTThGTMRxEcvofg8C/CAb5u0f3dkjfZ1TcDw8WAhYbhYtLHMQNmICmy0KDF2872NWonA2FarsFlQGLDN9pZE5/bDIwsA+liqJvxO9e0ofey3/wjhIcDfyZzZMn30c3ASDPQxQKRy+HxsKvWcsCL4Z681wPbPtVMBV5Jkpm10OStSTJNM+VdxgyUkAELDSW8aK6yGSgqA0UWGu6gg/+fohLXOfUKWi/jciCxgTe/NjNQdQbCnPzWvw5uy373+u0ztggHgAWq3nq3zwx0AgMSDSYmYzZEfPhwM+3F1WlevCNe7JmS0T0BseImRIuT2X8XsLLFh2ZYdJkSMGChoQQXyVU0A2VhoMhCw7l06p8pC5HVrme6cr+uhwZcJwIvoFXtC+7W9TIgb4Z0asXD2e9fazpcCXYHs/UWc8QMmIFKM8BUjE37X/chFR9uRnw4BeFhd7DqfUniL9tU+ldRycZZaKjkZXWjzEB7GCiy0PBTOvL6FB2fp7O1n4H005e/5XpIbPgjcAeq/RfFNRhVBgJvPsPPgKZ16f/gXXAB+DyYeVSr4pOZATMwqgwwj2os0y3OBu/2LzjEhSYVdr+L58OtTLfgc7o2M1AKBiw0lOIyuZJmoBwMFFlo2IfOuzrznh9dmN+SPBmCBCBdl6vBHIWpmitiBkaNgXQ9h435/Z8G8MBO/x8mEJ4PdgIzjVpVfCIzYAZGlQF5KeCtsBrYA++FX0tIGFh86H778CSZalQr6pOZgdYYsNDQGm/eywyYgToMFFlo2CLrwG9Wp97OaisDQSLQ+4CXPBaC2nopfPI2M5B+CpZ7VDgdvAokwkl0OA98Dlh0aPMV8unNwEgzoPUZNFUC7Ib48Eut34D48E70fCB9ZzN1kNeEvB8mJGO3ejtJFmxmH5cxA8PMgIWGYSbUhzMDncxAkYWGZemkq9O+VydfoOK2PWzKtRkPXgObFLeerpkZGC0GUtFBAukZ2f+F7l/vAK1v8lng6RWjdSl8HjPQZgauTJIxCA8rIRpsgwI5SzPVQaT4VRQnesLulwkvR3w4BuwEludTXFM3cyyXMQMtMmChoUXivJsZMANTMlBkoWE6OuZao+GYKavtnGIwEJbn+jwGJoI9ilEn18IMFIEBrWEStgS/A68DiQ5a0+FCsCuYqwi1dB3MgBkoDgN4QVzYV2jIr/cQ493v4CFxC6IEn+mc5qt8OWO9F5NkxuK0wjUpOQMWGkp+AV19M1AkBgosNIimdNG1vxWJMNellgENmMJ1QAMprd/geai1FDnd4Qz0ig5a0+Gl7H9FU4946Rn2BQt1OEFuvhkwAzCA98NSiA1nIyI8ObDgEIWHdNHJNxEc1jeJZmAYGLDQMAwk+hBmwAz0MFB0oUED2Nt8sYrOQPr5S32JQmLDBcBvV4p+yVy/NjGQfr1lQ/5HTgBPAf3PCDeDg4EXv23TlfFpzUCRGGBe4uzvJGM2QnDYHw+G34O7ESEmNhIg8G44rkj1d11Ky4CFhtJeOlfcDBSPgaILDRq8Mr3RVg4GwqFcL013YeGrMK4cdXYtzUC7GEi/4rI6/ys/BA+BKDo8QPzHgDeUYUy7aufzmgEzUCwGtFgkgoIWndwFHI/nw1WID68iPjxDepVmass+J4JH2OdS9vkZ2BWsiauVF65thsDql7HQUP1r7BaagVFjoOhCwxFZ53v2UWPEJxoiA4HvhQcWzE5X4N9qiAfz7maggxhI1zz5Lv87twAJdhIetL7DX8GXwbwdRIabagbMwDAz0DM1Iz/lom8cAeIpBIiLER5+GgWIV5LEi9gO83Uo+OEsNBT8Arl6ZqBMDBRdaNDn4dTZXrtMpLquYUWu2cNAgyXcOcO05sQMmIHBMCBRIRUXJDJIbNB9UP9PTCUL3wfcEzUNw2YGzIAZaI6Bp5NkOsSERxtNv+gn/2m+eIGXla0DGLDQ0AEX2U00A6PFQNGFhtXoTKuDzds8W7kY0Kf8Qly34V7iK5er/q6tGSgKA5o+ETYAPwJ3A90TBX1aVkLEbmDhotTW9TADZqC4DEhsmJCM+RiCw37gNNZ+uJHpF2/0IzLwWakezwfEhhWK2zLXbJgYsNAwTET6MGbADCRJ0YUGFhVMO9R0sG3lZCDswDVknY30037fIvRXKcp5IV3rwjAQFuT/aHfAF3lSsSEKD1rbQR5Em4PpC1NdV8QMmIHCM/BOkiyMALEZooIWn/wNuAEBYnwUGQif50E+y0ANoVM5F/udj4hxrY7DfgcyZWOrCUmyxNlJYi+sgQhs/3YLDe2/Bq6BGagMAwUXGsRzeAb8vTKMd2RD0oHR5VxHDYiuBot0JA1utBkYdgbSr1hoGoXWs7kB6LOZ+j+jXx8uA98EK4GuYT+1D2gGzEDlGWDBpQX16cw3kmSOZhqLyHBITpzo9YboyeuegPhwD2X+infEkeBzrAWxyrNJYmG0GXJHp4yFhtHh2WcxAx3BQBmEhn/TSeZNna3cDKQr7O/HtdQAiC93hS+Uuz2uvRkoIgNhNv63PgNOA/nPZ75I+iygaRbjilhz18kMmIHyM9DjFdH9QWOxoe8ClJkAMQkPiP8iPOABaWszAxYa2nwBfHozUCUGyiA0nEzH+D3APGVb+RkIH+Ja3gn01hVPyuAvipT/oroFhWUgLMv/2L7gfCCBT/93wqPgFCBRoqk3lYVtoitmBsxAoRhAMFg2+2rFsXgv/FMiAuGA4gPlHi9UQzqzMhYaOvO6u9VmYEQYKIPQoE6yOsZLjggDPmgbGAjdXM+fgA+ApsZ8rA2V8CnNQIcxkC4quRb/b4cCTWF6F+jeqv9Dfc1C/5Na34G1cWxmwAyYgeFjAAVhWgSIFcH2iA7fYwrFmeAO4m9H7wfSv23mjBzjs+zzEsKEPsd5GeHJYF+8KTZlrYlxhyeJ10dkVV4AAEAASURBVIJqhsj6ZSw01OfFuWbADLTAQBmEhk3p+KozvHUL7fMuhWYgXUn/Sa6tPtsnz5WZCl1dV84MVIqBMAP/c5uBY4G8jPR/qHvtRHA9OBp8FExXqWa7MWbADBSJgS7EgUXwgFiZSjW1lgyCxM1RnKgfdr9DmXsRMc5BlPghx/4y60ys08xilkUipk11sdDQJuJ9WjNQRQbKIDSMo6Orzi+Lmtmqx0BgFetwRnaN+fKWBaXqXWO3qBwMaApF2A6cBO4Duu8K8ny4BhwBNgLTl6M9rqUZMANVZABx4UBEhEn1RYZ6a0DEvO4J8nqoIifD2CYLDcNIpg9lBjqdgTIIDSjc4S1weqdfrGq3P3yca/w40MDmIrB0tdvr1pmBojMQ5uH/cEfwK/AwiMKDPB5uBj8HnwbzF70lrp8ZMAPVYoBO4fyIBh/HW+FrTJs4CdHhUsInBxIgKMPaNAMbX9tYiGPvzqc5t8ErYnneggy7Z9fZfO7zzSThPlsos9DQ4uVoyh2nxWN7NzNQVgYkNMgK7rIebqKOLAbZtVpaW/+pKAPpm9JDaJy8V/S97RPAkVx3vqxlMwNmoL0MhAU4//pgXbAOWAHEudBPEL8OMOUiDe/h/5apGDYzYAbMwOgxIEFg7qRba3otxc1pKdRRvbRYikHgkiHpQj+YtF13MvGW/mp0Jf3NdXsWplywptxzHO8xxNXHFHYRCu8l7z/GPLTnKEv2wIa7WPdSSTc6Q8LUtGQMO935QfLeDigZTw6894iXkNAgW7Mn8F8zYAbMQOsMlMCjQY0LvwYTAGKDrfoMhHFc678Bnr/hBbAriAOa6jffLTQDpWBAa6poIddwOLgUIAj2ej3oCxfKOwJsBvjsps0MmAEzUHwGHmfhSjwj3hnc1IzuCewzHo+Jx8HJ7Ls/nhCfquexwPoRZ4Db6YDP9XySzED8L6Bf8WMUWbNHwyiS7VOZgaozUBahYW86qhp0Ll/1C+L25RlI54PfnV37WwnXzm913AyYgSIxEPBCCizoFnS//hOgv94rPODdEB4AvwV7gBWBxcMiXT7XxQyYgV4GmDKxFcIBn+bsfojw3cGJDnFNCIXd41nsclw8MFM+5tOx3knGbBLzONfSyuNt2uIxr42hhYY2ku9Tm4GqMVAWoQE33bTDulPVLoDbMxAD6eBlH67/K9lv4EzCJQbay9vNgBkoAgNhXv5fPwl+DK4GWm9HorHA8yf8GxwNtgZFm6tcBAJdBzNgBtrMwOFMEUMsWJgvV2zIug274KlwFALEn8GNCAQvDiRCICRsEZvAvjtJfCDdFfMUJ28S2z6Ry2tX1EJDu5j3ec1ABRkoi9AgF129EftJBa+Bm9QUA2F2rv+J4D0wEZwCFmhqVxcyA2agIAxo+ltYFUg8/AN4BEThQeGT4GzAOi1hfTBDQSruapgBM2AG6jLwEuucIRKsCA5FMPgxUydOJJQ3xF3gDK3JEHck/RuEiStiWuGzSTJ9j0fDmM3y+W2KW2hoE/E+rRmoIgMlERpEfXgUXFLFi+A2DYaBsBi/gz+CDwALO4VjwByDOYLLmgEzUCQGwpz8D28JjgAXg+i9JOHhfXAXOBXsDlYCXqunSJfPdTEDZqBpBhAU/oUQcVp+B33hoscrYsx6+fw2xS00tIl4n9YMVJGBMgkN59DBZGFAmxkQA2EF8A+gwYgWnjsUzGhuzIAZqAIDmh4VPgeOAzcAPJd7PR8kMPJ1i3AC+CJYDni9hypcdrfBDFScAQSFK5l+8dN8M5lasSUeEBNRWGfO57cpbqGhTcT7tGagigyUSWjQQFKDSub82sxAZCCsxW/iyuy38SLh18HYuNWhGTADVWAgTMP/9WpgL/AbIC8HTaHSM0HgWZauAUEHPnwWLAnyc6CrQILbYAbMQMkZQGi4BI+Gk/LNYMrFkUypQFAthFloKMRlcCXMQDUYKJPQsA0dR3UoN6sG9W7F8DIQPs5vQ1+m0G9E87x3AXaxHl6SfTQzUCAGwnT8j/MlmrAv+D24H3wAovjwOvErwbFA3hFLA3s+FOgKuipmoNMYQGg4AFHhXhai4Ss9SfJ0kkxH3rN4NPAyrRBmoaEQl8GVMAPVYKBMQsPCdBLVgTykGtS7FcPPgN5ghk+DB7LfylOEWlRu1uE/l49oBsxA8RgIM/H/vgHYH/wJPAjy4oO+ekFHOvwa7APWBbMUrx2ukRkwA1Vk4M0kmRePhqd6FoucZm95MpC+Jr9gZJvbbaGhxQtgF7oWifNulWZAQoOMzlkZLDxHLW/hS0B4N9jMQCMG9EnMZEdwAFgF8GxPTgfH89t5jNBmBsxAxzAg8SFZGawGWEwyWREsD/JTrBAlk3sy3J2FD6VfnSNhMwNmwAwMFwNa/HHqpPsrDEwX5B5z54Tk3T/MnCSvDNfxh3gcCQ2yNXsC/zUDZsAMtM6AhAahJBb+xtunl4GFw5JcsfZXM2zI70WLRk4CerN5Lliv/fVyDcyAGWgfA+lnNhEb0vUcfkD4T/AkiNMuFL4LtBaEvnLzLbAFwLPOZgbMgBmoLAP2aKjspXXDzMDoM1A2oWE/OnrqAC4z+lT5jOVmIF0c7mR+O3g3pL8hPGPSQYbXcSj3hXXtzcAwMqBpFOl0Ci06+UtwLRgP8gLEG6RZuC395KYWn90EzDOMlfChzIAZMAPtYsBCQ7uY93nNQAUZKJvQ8BE6dOrw7VrBa+EmjQoDYTZ+PweDZ7LfEmsxpZ/GnH9UTu+TmAEzUEIGwiLcJ7YEBwEtPHk7eAfkBYiXSF8FTgJ7gw3AXCVsrKtsBsxA5zJgoaFzr71bbgaGnYGyCQ36xBnT24Lm29vMwBAYSD+XtxO/pZuABgv6VJ6mVejrFZ6aMwRmvasZ6AwGtBZMWApsC74H/gJY063PZzd1b5EAcTX4Bfgq2BjYA6IzfiRupRkoGwMWGlq8Yu44tkicd6s0A3F9hpIsBqlroQ5bMi8L6PCpMpsZGA4GwiocZU/AJ/CSGcFjgFXpk9/yO3uR0GYGzIAZaJIBiZgJAkSyXIbls3BJwm4Q7VUiD9TBk9x3EChsZsAMmIFRZ0BCg8yLQfbw4L9mwAwMgYGSeTSopeEHQG+J5hxCu72rGajDQPppPASHcGf2G9NicGeBDesUdpYZMANmYBAMpAtQLsv9ZDtwKDgT6F5TOwXjLfI0NUOf5/wu+DRArAj5r2QM4rwuagbMgBlomgEJDVFsaHonF0QeNglmwAxMwUAZPRq2pBX/BJ/g35qvCdjMwEgwENbgqIgOyQ5gOsCn7pLTACvQdz1PaDMDZsAMDAMDYSoOMg4gQkyBWcmLNonI40D3ogczZHF7XsGHzQyYgaEzEEUGezQMkksLDYMkzMU7goEyCg0s5pd+b/gYBnwHdcRVciPbyEBQR39nsAeQK/T74F/gDHABv8H3CG1mwAyYgRFgIMzLQZepgaYNLgwkUER7jYhEh4iHs/ij3KMmxEIOzYAZMAMDMGChYQCCGm220NCIGed3MgMlFBp0ubTgVvI6Hah1Ovniue2jzUDgqyfJl8GOQALEK+AcwCJwydX8Hj8gtJkBM2AGRpiBIC8rrfkQRQiJD4prbQitMxNNXhBPAQSH5L81YC2artgHYJPNDJgBM9A7bWJNczE4Biw0DI4vl+4MBmIno0SLQerChBP5sxfg02FdepNjMwOjyECYlpN9EnwRfBRMA7RopESHs8E1/C7VwbeZATNgBkaZgTA/J5TwIEh4EBYHi4KxIG8vkUBwSCEhIh9/1vexPFWOm4GOYMAeDS1eZgsNLRLn3SrNQFmFhk25Kv8Cn6MjdGalr5AbV3AGwuxUUKKD1nLYGIwBEh20fsh54N/8Rt8ltJkBM2AG2shAuhbEglRAooOwWIYY170sb7pvPQG0LoQECIW5eBdehTYzYAYqxoCFhhYvqIWGFonzbpVmoKxCg97KvAwYzHXtVOkr5MaViIEwB5XdNoNEh26g/7GLwd/Bhfxe3yC0mQEzYAYKxkCYhQpF0SGKEDFcmG3y3MqbhIa8ACER4onJ6HqbuM0MmIFyMWChocXrZaGhReK8W6UZKKnQoGsS9LZ4fTA3gzfPjRcltgIxEGamMluAT4HNgaYnaeHIK4F+u+fzu32e0GYGzIAZKDgDYWoquACQ8KApGEI+Pi/p2n62PLueAHkBIsafovg7bLOZATNQLAYsNLR4PWpvgC0exruZgUoxUGahYVeuxGkAsaHrP5W6Km5MxRgI8sDZBGiKxTZgHhDATQAvhxR38jtWns0MmAEzUDIG0sUpF6HS44BECIVCjLOe0hQWhYgn2SI80Tf0QpXwYTMDo82AhYYWGe9qcT/vZgaqzECZhQa9QWGxqsSfuazyL7RybUvnSa9Fs+TpsDXQQm2y54CmWEh4uAzRIf5vkrSZATNgBsrMQJiB2kuIiMKD4lGYUCjxtdZeI0MCBN4PdcMXLM7WUua0GRgyAxYaWqTQQkOLxHm3SjMQBzMl++pEvCbhFmLT09lYPuY4NAPlYiAsTn23zLABobwfJgJ56VwEtK7Dg4Q2M2AGzEBFGUi/5FMrPsS01ofQtI2paxqvxSqfBhIihHw8S3e9Rb7NDJiB5hmw0NA8V31KWmjoQ4cTZiBloOxCw2G04nCwGIOxx9MW+Y8ZKC0D6Vu/jam+hAet77BQ1hT9ti/NcAW/dS3CZjMDZsAMdAgDvWtESHyQ8FAb6l5Z74XJq+TnRQiJEXn8j/uphF2bGTADPQxYaGjxl2ChoUXivFulGSi70LAaV+dWsC+dhRMrfaXcuA5kIKxAoyU4aDHJtYFWfdfCp/LkYXpFKj7QKeh6n7jNDJgBM9DBDIRZabxECIkOCiNiWl4R+vxw3gIJpmD0ER8kRDyT5SlkWpvFCHiwdQYDFhpavM4WGlokzrtVmoGyCw36v+aNRHIvHYGPV/pKuXEdzkCYEQI2APqdfwwsC2T6H74SXA2uAVpU0sIDRNjMgBkwA5MZSNfHmY/0gkDiQz1o7aepQN4mkZAYIdFBUJ9Da+pojSiFEXxy2wv6woet3AxYaGjx+lloaJE471ZpBkouNOjahJP4sxfg7W/X/cqxmYHqMxDUWZbgIOFhExBXddec5JvB9Rlu4P9Ci6rZzIAZMANmoF8Gwhg2zw8kQugeG5FPS4yYGtTa+2RIkIjCg8Lnc2nFM3RpfQmbGSgiAxYaWrwqFhpaJM67VZqBKggN6gA8ArRo3naVvlpunBloyECQh8N6YB2gaRZLAJlcgx8AEh6u6wm7Hia0mQEzYAbMwKAZSD0jJOxKkJCHhCDxIcZjqLxpQT2T+BuFhyhIxHQMJVrIS0IeFTYzMFoM3JidaM3ROmFVzmOhoSpX0u0YTgYqIDSIjnAcf1inIVmdh/JtyrGZgc5mIMxN+9cCUXj4MPGxGSd0XhN1Jm4C8n5gzQd7PcCDzQyYATMwjAyk60bkhQeJD1GUiHGFc4B645QPyH8JSHyQ8JAXIZTO45XyiRJhOtqwOZgdyCP1JtqgNtvax4CFhha5r/cP3OKhvJsZqAwDVREaNKh6DFzLQ2qzylwdN8QMDBsDoZtDrQbk7SCsCfRGTiavB3kF0clLhQeFd/G/9B6hzQyYATNgBkaUgTANh58HRPEhxmOo/BifuUFVoijxAtsjokDxInmC8hUiXrR7gcswC/W4Dyi8HUgY17Nna+qW+7JSmJO8dYHy/sM2CxEQMYJmoaFFci00tEicd6s0AxURGnSNwtH8+TZYnwcRDyObGTAD/TOQrvPwEcqsARTK60GLTsreBSwsmX7h4lZCeQo94E4eLNjMgBkwA21jIEzLqaPwIPGhP8zaoJoSlzV9IwoPMYyCRAzlTUE8P/BvcMRBZ4cfsguiQrIqx0fUDgsR1zPnt6S/SYiFnflzBrgXSGCRN95GbH+DMLMgTz09uyScX1a/rmFptklo51nmqYPw0J9ZaOiPnX62WWjohxxv6lgGqiQ06IH6OLiHBwlig80MmIHBMZDOPV6OfaLwoHB5oAXSZG8DPB1S0UHCg3A//29+wwQRNjNgBsxAsRhIB+FzUyeJEQrz8ZgXwznZHu/1RPuYvNsy0SF6RPQXdr3ZZ++6iXAQ2eqvXTR5cziLOCJK14aIDMsS1zNmd9J/Ij098evAFaQPIMTCwvxRGY3xHgUrABZI7lI5LCj/fLAF0DpFq4Hj2a6XUrb6DFhoqM+Lc82AGWiBgf9jH6EiFnh4BJT6sFlFGuRmmIE2M6A5tGFNsA84HSA0hIlA/2fC24AOXDgR7AroyKWd2zbX26c3A2bADJiB5hnQoDzMARjghw3AZ4Du+0eAX4JzAN6i4SHwKpgE4nOgNpzANrzi0ufFJYRfH7ge6bMGz4pwSk/ZcCjxe/ruF/Yn74lsu+rLoDicCXhOycJR4EnAtjR9IPHnwCJZGu+JwNdB9EyzNWBAQkMUGxoUcbYZMANmoDkG/o9iQkUszMADRA8q3OPig6YiTXMzzEBhGJDrblgD7A3qiQ8SIugghj+AA8AmYI7CVN8VMQNmwAyYgSEyoHUlwnxgJfBR8FmAoJAO9uWB8DjAsyA8AE7t/2Typgu/Bq+DJXrKhn/05OX3DJ8iT58RxTQdIhU6lulJp3lMv0jzPtKTF+4m/b3J29MyDKLDMX3znMoxIJFBsJkBM2AGhsyARAahQpY+6KSub1ehRrkpZqDgDMiLIfVmkFfDiUBvvt4A+TddT5FW51FvnbYHenM2dcEb5uqZATNgBszAiDEQZuc5oOfCeLDx5NOEZ0nvPzmtmPp16TNF3gxfAFpnosZSj4Vt2aYXT/JeQATJW/gzeXhB2BowYKGhATEDZY8ZqIC3mwEzUAkG5HZ3APg+D5PzmIuHe5/NDJiBkWWgCzfZ3rUbslOlXkWLkVg5h1WIb50VUMB+4X7Cu8E9QPGHwBP+34UFmxkwA2agsgxInE7OBYgMWpC4S/f+aDMSqRUStCDkO1kBLQD5YBbPB3hHpGtN6LkjITt/TJVTnsrUWECcSLqBzpkHXhZdmRdFzS5OmoEcAxYacmQ4agaqy4AGPJpTmJwGDgY/qG5b3TIzUGQGuvBmSP6bgfm90cJsxFYEWrhLocCbqmRnEE3/x4+QeBjEUHHQ9WIs5NAMmAEzYAbKyICmWiRMvUvOAPtxX59AmLf3SNS+KFqSvDsoK085PBbST17m9knztD6DxAgJFbJasWIm8l5Ot/T+kVdF8hfQYKwYtLhljfiQphEh6oYqq20SKd4itHUAAw1+PB3QcjfRDHQeA3p4sZBRguAQruBG7/lmnfcbcIsLy0CXOmFXZ8hqmXo/LEqC6RQJc297sRZxvWnKmaZkpAJEJjykQsSj5CFqdL2SK+ioGTADZsAMFI4BrfGT/B4czj376AbVk6C8QM221UnfkuVNJKwVIpbKtt1GqOeIrF6ZS3s2xb9dr9JX1PNnQSAhPI9Z66THZXkSLbpAP5auKZGJDr2iRD49np1juk7Y9XY/B/emAjEwwA+hQDV1VczA6DHwf9mpdLOsmIV5aNBdQMr2yjwLdDO3mQEzUDoGgt5M6U2WOpERMa0OYd7UUZPokAkPvXHSXc/nCzpuBsyAGTAD7WBAL4CSjbIzy5NBHgYRB3CvvpuB/+HkfRKsSZoy6TSLm0izX9d/Jm/von8XLexO7HC2I1CE5YnfC3hmdD1CiIU5+aPnwPrkXa+coVn6SehZOYagZ1G9MG7Pb4t50zVxfgkui1NfeVWMhsUXc/BuGwwDFhoGw5bLdgoDFRYadAnDpvy5GJzNTXpH5djMgBmoEgNpx1GiwxKAzlgaKi7MAfImF9bHwWN1QP4Urrv5fR03A2bADJiBYWEgzMJh5sqgwX8ev+Re/AT9t7nJlyAhuwN8AvyUbUcoIxMS7iGyCnl3kdag/Wbwb9LfIMTCffw5l/ShaTIJPyTcHiBCFOF+H7qpSxQd8qH4iWm9LPsB9Z1EOBpmoaFFli00tEicd6s0AxUXGnTt0s8YfZPIrtyoT6/01XTjzIAZyDGQdmaj6BCFCC1OKfDGq8+CYMz5TZ4DUYSQIPFEDs9w/3iftM0MmAEzYAZGhYHUm20LTqU1FPBi0GLB6do/2dnDSUQ+By4CqwGJyetQhjV+ZGEz/pwDJFhMAzYAa7NdwoWtPgMWGurzMmCuhYYBKXKBDmSgE4QGPVzkIqe53zyI+qxq3IGX3E02A2aADqjeJI0DUXiI4aJZ3syEeZPIgNjQKz5EIeJJ8gQLEZBgMwNmwAyMLgNhPc73YSDxgEFyradCuvbC5mzTFATEiq4XCG2NGbDQ0Jibfrd09bvVG81AZzLQAUKDLmzQ20w9hDRvW2r324Q2M2AGzEADBtJVyMexMWLRXFx5Wjcib3JrfRZIdIh4qm981ObYclqbGTADZsAMmIFBM2ChYdCU9ezQ1eJ+3s0MVJmBDhEadAnDTvz5I5AL3VaIDZr3ZjMDZsAMtMBAujbEOHZcJIeFc3G5+tba62RIfHg6h3xaXhHv1e7ktBkwA2bADJiBUWLAQkOLRFtoaJE471ZpBjpIaNB1DPvy53jAYkHJ1hYbYMFmBszACDCQzi2OwkMMF+JEgtJaI0LTN/IWSMitF8GhLv7Xk1/rGkyuzQyYATNgBszA0Bmw0NAihxYaWiTOu1WagQ4TGnQtwzf483NwGdgGsYHPJtnMgBkwA6PJQFCfZB4QhYcoQihcMMN8hGNArb1CRhQjNF1DAkQeEiv4VFzXB4Q2M2AGzIAZMAPNMmChoVmmaspZaKghxEkzAAMdKDTouof9+cNnkpJbAJ+97HqM0GYGzIAZKBAD6Tfa56VCUXioDeUVMT+Ytk6lJ5EnQUKigxZBE16qCXN5XZrWYTMDZsAMmIHOZsBCQ4vXv95bgRYP5d3MgBkoMAP69vC3wNXgkvr17JohSb50epL8FpFBi0SGPRAb/lK/7LDlbsmR1gDfy454AKEGAX/M0sMdaLG6N4fhoBrEqM7iUpzazIAZGBUG0u+my2NBuLnxKdOFKyU6RMhTYm4QQ8U/BOYAU4M6FiaSKSEiAo+I3rjy8mnFETHsMQEPNjNgBsyAGTADiT0a/CMwA1MyUEWPBi3O9gT4MTgY1DO96bstm0YhgUGd8NMAaziM2CKRmq6xJ5gOyB4G94JtlRhG0+c8JV7QvuQnw3DcWTiG3naKS3FqMwNmoJQMpB4SWqRSwsNcWZiPKy8PlW0gTCRaT+I1oHuphIf+oDIC5S1OwIPNDJgBM1BUBuzR0OKVsUdDi8R5NzNQXQa67kds+AjtkwjwFbA26R3oDEsAGGn7Iid4awROMgPH3B7cPgLH9iHNgBkoLQOph0QUBJpoRSpMzEbBKD7MWRNXOmIF4vKYmAk0MsSJINEyCg+qS4wrfDVLK8zFu6IgTrbNDJgBM2AGzEDxGLDQULxr4hqZgQIwkHow8Lb+6f8wFfpkXszhonzCCTg3yBtAnd1ak3eUvCa0aJsWYHsSNFp0bSm26c3fI6DWHiQjv5+mfLwP3swV1Ln0VvFt8E4ufyzx5UA3eAC8AWR6+6jysumBOv7qwOdN9RZ0/nrtU1nt+yFwnxI2M2AGOpGBVJiIQoDuF01Y0L1J950oQChUOo+Yt0yWL6+pfrxO02kdOeEhvW8pLY8KhRH5tOKIGmkbiNrMgBkwA2bADIwcAxYaRo5bH9kMlJ0BXMUWujhJZr2KvuknaMxBPViKaRUP75hr3KeJ/wLoDV+0p4h8FlwfMwg3AqeCxbO8Wwgfy+IxuJnIvWDbLOMuwhtA/nwzk9Zbv2+DHwLZ1uB0oM56tN8T2RtI2Lg9y9S6CoLEh0lAwoGmVKwEov2JyD5gfJahsr8FnwHTAs3bPgzYzIAZMANNMND1LoWezdBEeRUJUSCVSCpBQmE+HvNiqEUxtX0m0I9AIZE36N4m0aEeJFDEfHlaKB5D9uuS8GszA2bADJgBMzAgAxYaBqTIBcxARzPAQP31f/Myf4skOedjSXLSV5Pkwh2S5C8LMxPhK3Q65X3w1wxHEj4PNgO/BCeBVYFsEXA20ID/o0DeCIeDvcAEMBTT28IzAfVMxZAXCXcGPwN3AtVjRXA3+BE4BUhkmBVcAV4CEjIQVZINwG+BBJHtgUz7fBrsCi4AqwOJEzYzYAbMwAgxkN5bdW8SBmGpQDEbO0h0EOrFlRcxL/Fls/R0hANY+D8KROEhH47P8pXXCG9kz4wBzuHNZsAMmAEzUAUGLDRU4Sq6DWZg5BigY5hshy7wTpJszkB8aw2wb2PWw4cJ72RMfmWS7Mfg+/V9SMcOscrIw2AbEE3bZwCfB7Gc8jYEi4KhmD5lp2NfBx7MDvRzQnV87wfyQHgayNQBfjKNUXFCeWGoTpdmeRISjgPfBxInHgMHAIkWfwYyiRP7A4kbNjNgBsxAgRhIBQp5fAmDtHSKRxQnJETMCmIY4zEdw4WzcjMTTgUGsFSoGE8hQffjemHcXi/8P8SKMMBJvNkMmAEzYAYKwICFhgJcBFfBDIwiA/251KqTOKmmLhqoIzJEu+Dentjxv0yS70ybJF/aBbDP+COYWXBOklw2N9tXABIi2N5rKxG7B0SRQRvUWfwXkFfDUOxxdr4V/BhI4EAQSXE6YX+mOr4NlgCL5QrOlMVXJpSAIc7kLZG3i/IJx82AGTAD5WcgneLxHO0QBmlB90mJDRIkGmEWtgnaHsN5culpiA9kPDf6iBUSwyVI1AvzeYrn0PXWQCfydjNgBsyAGRgaAxYahsaf9zYDZWGAt0CpTd+gwroXqJP4v5rtr9Wks/m5LzFA7/o6ng0n8ML/fMb1e+I4gGAwHfkTWZfhfQkK+cH7oqQfqTmWks/XyRsoSx3aWtuYjMOB1lE4OsMDhFon4i5QzxYicyqwX52Nj5I3I1C9ZbVvB9VhfSfd4j9mwAyYgY5nIPUy0IBfeLI1OoKmbkQRQkJELfSMqpe3eJav7TpGExY07U/PxZz40BuP+TFUmRjPh1k8FWiaOKeLmAEzYAY6iwELDZ11vd3azmXgdZo+ASzSgIIFydegWwPsvNUb1Oe2L7kzCQSFlREc/rQKH31Yn/RqSbIJQsMVRJ9F2JhfXgMa9OsctTZnbUZNGm+JPp4R2jxfTRkl1eE7IMNyhFuBw8BZYFlQzySqSGyQB4a4qWe0KbV5azZKsGmyQ1uzp5NmwAyYATNQh4H0a0cScFvwqIiHC/KKiIJEPlR8IPAsSxfTVDl5szVp6RdA9AySl4Sed/1B7au3PZ8f4/mQuL8WAnc2M2AGSsSAhYYSXSxX1QwMgQEN2K8FHwNrgJtA3vbIEvfkM5uIf5Qyj7FcwzeSZHmiYSP6UAclyTMf79l3TjwBAtMOtuL8F65Int48/bdnWzolYbMs3ijQm6RagUL1l0URZHXiWi/hq0BTMe7PIAFBHg7qeKr9svw971bSOv824GwQbU8itCddAFJTRSRCfApcAKJtHiMOzYAZMANmoCgMdGlNnlcyDKFSQcK7ptFJdOgv1LYIiRMSoSNmI648idIxT8dt0YK+XiKRAtGhN6yN10srL494jHyennM1aQsbcGKbgoEwjqyDwYF0wySw2cxAQwbyne6GhbzBDAyBAQ3y5gJ62MpVcTzQ23XFbaPLwF6c7jpwIfg9kNgwI9gEaIrBGeCfYDCm6RDbg13A33noqKPyPtDAfmqWZCBvfsSI3/MbWIO5tVPfzVcpWVTxeQbtB21FGakT74FGdjUbvgaOBOcBravwA6AOVzQJBvpN/QyoPbeBdYAEhEuAOp6qj+q1BZDQ8TfwUyCB5edgUaDja79jwUVAIoPsW+A48DCQoLECwIMjXWOCwGYGzIAZMAPVYiAdZKu/IgyjpQtuSnTIiw9RhIh5McyXi3n5MMYlaGhR5JhWKEQxnmgrFvRszosPiucFiRivDWO5fH5/8Zpt6YKmrVTY+4w4A6nHkDxF1wCH9D1d0G/wE2BVcCP4HT9B9bsyC9MS2QzoRdQz4A9sf5owZ2F1EluDmYD6lOoD2krMwBBvQiVuuas+kgxIVPg82A2sBCQ25E1q+m1AgzUN+IpmUaHVja5qpgeABuq6mc8ONADXgPpyoIeGOhbRHiTyENCDI5rexkgk+hFQ+QXBMUADeL35UYfhF+Aq8A/wScbj8gRYk6wvMmbnd3E7vw8d4iPv4uDAOc5eDg0AwaFLosXDQPXZFsjmBDysks2B7lfPgp3An8GJ4IdApvao3LJKYGoHD6n0Nxivp8ruD7rBMkBtWxKcCtYH8fgXEv8ueBFEO5TIXmA+oN/v3kDtlADyY2AzA2bADJgBM1AgBlJRYzoqND1QOBA0EKxXpjY/puuFeo4O1d7nAOpLROSFC/oNvflxe72wtlx/6bgtF/rLJvUvYlB/j75cMjfgxU5cVDXMQVp9t1fAOeAL4A62b0eYWfgTkS2B+lwrAPp9yXqUeYIQC/QXk3OB9pcQsRfYle3ar912Y1aBNdtdkbKdfzhuCGVrs+s7sgwsxOGvBQsD3fwfBK9m4A1377e9lyU+BpwHNGh7HhTF4sB0pqJUaITqsTjHfQW8PgzH1+Bd6z88Bj7o/3h/QHz60Fas67Ah5dYF6qzIGNjPR4doLo5x9+6k9ZCamG7pWQBMb22eyNL1At3P5gEq9yiI+xLtNdVzBvBab05PRHnzAe3XyHT8xcCTQB0hmxkwA2bADJgBM9DLQK+4oee6IOGiNh7zYhi31wv7KzM2d2ztqz7mcJleVqgPmxMfGsbrldN+EXF7o3TMbxAWxcMjbEGbTgOHZiH9qdhHC78nT/3/jcnDezXE/uBGpK8hvTXbJCDQh+pCREinJv2H9O2kv0ZaL6oeAd8nfRIhFvRi6ADAeKLtHNyoGmFr9gT+2ywD6jjbzMBwMnAfB1sUfB38BbwB6tlcZO4KjgaXg01BUaxThIYC8B008F8NrIM+sS4aAQ+jHadKkl+rbvIcuBlIuLoe6IH0AqHNDJgBM2AGzIAZMAM5BoJeXuXFiloholE65sdQx4jxfJiPxzL5PMUF+jDDanp5IxFC4kcDMaI3P18mH9cLGL1QuTM7jrbF7TGuUP2uq+hrKZ6zsAAJ+mCpt/JMhHgjd+XaGZ4n72DyziDMLKjvdit530A0OJG4RIYt41byNE74FnkcO6xH/EowO+ls3JBOxfgfefQNu64jbKfdmJ3cQsMgr4L+KW1mYLgYWJIDLQf2AKcOcNCX2P4jgOtV8h0wL9CNytZRDKQPsxto8tpACjgPrru/SSgvCx4uKb5LmFmQ0ICnQ/qw1ANTeISH0CRCmxkwA2bADJgBM9CRDKTrAbxJ04U2WrqOQRQdpqUiMV4b9rettmw+rRc0+bTi6ksrzG+LcYXRPh0j/YQ7se3Pk7cHeYoo/Vv6WpchCnyWeE6ICLOQljfpoyBvEgmUL1saPJDGJv/RdqZgBL301vYXJ4sMaaHn+BtAPEaa6T/lYsBCQ7muV9Fru2JWQamSzdpVFJTQsACw0AAJHWoTafe94CTWqPwNDxvRQCgLesisAVbOYTPi0VDgw90kJDrcBeRV8yDHkJhlMwNmwAyYATNgBszAKDGQTidQn6bNgkdsbjqQz4sOijdKy0tBUxrydhgJiRjxpY/2zQkN6VpXKi9hIG9vkVDfXrYEuCKNTf6j7RqHykNCLypr9k+nYKiMvDFsJWXAQkNJL1xBqx1vEnItqlU2G1V5AzbobfTDjQo4vyMYOKFxK9PpEv9gu5BZqqCvRCIvPuxCWg/AzMJrRBAcpsBjiBDvx1IOzYAZMANmwAyYATNQTQbShS3jlItBNjFEr2O9wDmflzoSGBYECA/hNELhHSCrnY6vqRqvp1t61rWqt12CjKZrqE9Wsz1dx0FrdMRjELWVjQELDWW7YsWu701UTwO7XwDdYP4CGt0g5mSbBobfBpeCuC4CUZsZGIiBrvGUuCZDVjh1V1yGhKbvLA0UF3YA04NoPNjCoyQeAYgOffA4z7oJsaBDM2AGzIAZMANmwAx0KAMSAA4ECAvpixy9zNHCjRIItC2AV4BM/f68zUfiqizjZUKJFnmbl8R/e178hHrb52K7pm08lN/J8XIxYKGhXNer6LX9gApuA+Tyfgo4HshTQTch3UTkkiUXqDmABoP6/T0AvgBsZmCIDKTuivdwECFnqdvgQmRE4UGhhAh5Q2wF9LuMxkMzPEtCAsRz4A0gEUwukHnUy9N2ufmh7vvTWPBgMwNmwAyYATNgBkrLQPri5Wd9q59+DWIx+jl79uSnL3n0UnE1cFeWJ08EeZ1qLCCTWLBqGpv8Z60sXzkPgkXpfzFG6JI3qkzb5fGsF0O2kjJgoaGkF67A1dZbYk2H0A3oS0CDuZw7e3rT0Nz5a8GJ4O9ANxKbGRghBtJB/1McXJD3TM7Sr14sQgYPzRSL5+JLEJdyX6vSk9WvSayQK6FEB7kE5sMYV77KxLC/+ISsrMLaOGkvhAkvNjNgBsyAGTADZmDkGVCf/r3Jp9FLnnA66a8TnkufRKLDIUB9/fOB7BTA2g9hXbbT/w+rk/4k2BzILgHPgG+DA9mudRt0jJMor36PraQM1MyHKWkrXO0iMzANlZMXgwZsulk8D+RuVWTT22qZbnSjZfNxIr1BL5vpoaAB/B9zFdcDZD0wB7gVSExi8N3QULGT3QEPlETeBHmTAr4p0O/oOnA1qPf7kXL+UaByF4NMVSfWYz8huAb8M0sPIkjnCUps0O9hxgbQNpWJ0FSN2njMUxgxlvhQTQ98/W+9O4hQZbWfwoh6aeXF/BiPYTyG0nKjjPlZ2gIInNjMgBkwA2agUgwEPdv3B+rr3A3OZDB8H2Fm6QuMT5HQQPot8Ae2q++Ss7AGie2B+j+Xg19SJtdPSj/tuCP5G4L7wXFsV/+5AJaukTUL9VHfL7MwOxFEhuTDQB4J6qftRJnrCTML3yMiPAlU/qdsP4owM4kQyXlAfQhxrP7j1pTRi5h2241ZBdZsd0XKdv6uslXY9TUDo8DAaAsNGhzfCb4IbhuF9g3XKdbiQP8ASwE9WHQ/+SE4CPwXTAX0ENVaHXpg1rOpyZQAsDZYFdwBou1F5GTwKvgfWBFcCPRwlidAtIOJ6GH1AtCDWMe5BGwJPgCy9cFZYHmguhbE0mkd01EZQeJDPoz5CqfNIZ/Ox1VGwsVAocoIeiuhazRSJu7rCBBpnvLjthgfKFTHpV6ZmJ8P83Hto3REbbq/fLUhbs+FXfF3xWabGTADZsAMdAYDQc/Xm4EGw6eDDcC2YFm6QI8SYuEn/NkDIA6kA+avESI6dP2LEEtFhmuJaGCuUH2YC9i+JyEW9HxWn1DPGQSIVLAYR7gGZdQfKrCFJagcfYsuiSN1LBUklmQDfb308+Y1ZVKRZiUyH2f7yzUb25m8MTv5mu2sRBnPbaGhjFetXHXWIHouIHVSN83x4PUsTlBIG22h4Ruw8HNwHVi3kIxMWSndO+Q18BdwdLYZ5TkVHr5D+AOgQey+QG1bDdwOau1QMo7MMvNCwyzkPQZ4+KYPbD3UtwASGrSPhAWZBAo9qP8BdgB6y64HgcrxFiERt9EuJfII2CdmOAxj4CAKD1F8iGkJEULMj+lGefpfj2X6i2tbHtqnXlp1y+crrryimIQH3dOiAFEvHvOaCfNlFG8mHcvVC2P9tG1Sdrx65eptry3fKJ3PrxePeU2E9oDhGtnMgBkoNANhL6qnPs7KkwfCQf2Nx0gjKIQPEb8T8CKm6xZCLKgPtA7pj6TJJNxLeDHpA7O0+jFMK9AnILsYYKdv/ncmvSRp7p3pc/pR0ieTPobQNvoMWGhokXMLDS0S5936ZUCiwufBbmAloAFC3vQ2Wm/uTwB/y28oSHw0hYY5abMGv7Nmbf8c4ZlZvMjBVlTufLAQeDar6HWEE8FGIGR58lj4BbgM1F5rPXSvBRIEdgF5oWFb0norsCn4L4jGQzgVIDbJMnjwJnsDHuLJ9VmeglPBrmBe8CKQSYg4AywCYh5RW7kYSDtduqdESHxQPB/m4/W2aXtE3B7TtaF+w7V5+XTcHkNtqxdXXsyvF+bzasvWS1fx+S1BIiIvgNTmDZSO+8ZyteFA2/PlB1M2v5/iug/W5tVL1yvXTN5AZfLb8/F83fL5+Xi9MnF7DGNblM7nxXhtGI9Zm59P5+OxfE2YdzHnzDYzMGoMBIkH/2bALy+EOhb2IPMQti86eWNQ/+RaMD+YAOSVsDplbiXMLPyPyI/Jo18cLifOi5mub8Wt5B1HnGkJXetOznNsFBmw0NAi2eoQ2czAcDKwEAfTDXVhoBvqfUA3VUGd5dkzoPam8/jPI9RA8XnQiXYUjY4ig9r/E6ABvMSYIptcAa8DUWSQB4JU+T2BOoryZpgZvA6+AmpNYtQfwa+Afi8SGvJ2LgkhbyuTWARcmMtckrh+Ozfk8hS9BOwGVKe/A5n2U912B0cDWykZ6HqfagvvlLL6w1bpdNpNFCDqhWM4VT5f/5P5dG28dnu9tI4Z8xuFOm6jbf3l19vWKC+fr3htOtYhbsuH3Vn5fF6jeH/HabRPzK+iEAR1RTDdxtN7uSI1IkSv4KFtcXssE9NxW226Ublm8muPlU/n92907nz5wZaJ5Rudp9Gxm9kvlhnoGK2Uy+8zmvGl+G08CdTPiucl2huPeZTpulMbcjaO+BMUPZFwc/AmQBzoOp1Qthh4Oo1N/vO/LDofYbwv1JZRX0rbZYsC9QPzpmPIa7TGwqxk6IVKrHMMVS7G8+Fg87Vv7T616Xj82vzadD/Hsngosqpo6jTYzMBwMvAvDjYX2APIrf4NUM9UZlegAZ8GnXpz3Wm2Ig3WYDhvC5I4CByWzyxgfA3qdGyuXgtkcT28fw22BxIfHgRyNbwK5O1nJPSmUIr9NvkNdeLLk/dpsBO4H+TP+wLpTYDO/wyItlYWmTtmEKpDcD34SC7PUTNQUgbSjlkUXUrahqpXOxWDovDQKNTAo962evm1ebVpHSef1yjeqFy+fL5MzK8NY71r8xul4zEbbc/n5+NxvxjGbbVp5cdt9cJ8Xm28Nj3QsWvLN0rrONNk9VKZPPLnqLd/LJsvl4/H7TFsZVt/+1DdjjZ5Ps4zmYF0fYY5SR8KrgPqv2wJ6PME+jNdvyO+CHgF5O31LKF+r/q7sld7gt6/KkN/Jb1nLEy89hjjydP+tXY5GZqaWnKLGkTaDCUilBHjsZBCXZtl4fwtFbAVlwELDcW9NmWs2ZJUejkgkeHUARrwEtt/BGYE3wFSZDvNq+F42jw1qLUDyfgNeKp2Q0HSGtRLRHgyVx8JJDKU/XSRpEMIZwN7g8vAquAeIJOw8GUgb4Nm3kp/l3KfANOBXwA9YKLp2J8HeuCLt3fBIuAzQKbfV96eILFRPsNxM2AGzMDIMJCKQRJUBZsZKCED6cC3i4oL/YkSscxgyuX3Gc34/LRFfVB5gMTzEu2Nx7z/KbOO0Qfp2j7LP5dxsPpE3wS/A28DeRnkbfoskRcXxpI3MVdI/RvEBd0z0s9ja3vetD2/f9z2NSIrgljnGGp7jOfDweZr39p9atPx+LX5telmjtXsPi9TsJn+o45nMwNmoCIMbEc7pDQuMYj2fDTbp0iK7P9RJ2EkLXIlvurhrJE8+RCPLe8T1XnD3HG2yvL0IMw/IJfJ8v+aldXbAQkFh2ZpBTsAHW8VJeqYxBgdc0egfe8D0fTgughof227FLwPfgmUJ9Erb4eRUKc/Pvjz2xw3A2bADJgBM2AGzEADBgID3HB4343h6+RlfcZwFPEba7avQB79kYA3REAUSOML1ZR5gPy9evLCQ8QPqNl+JHlX9M1zahQZ0DWtua6jePYSn8oeDSW+eAWs+nNZndYkfLTJ+m1AOanKDzdZvtlia1BwnWYL15STm+NIvoGaluMfW3PO2qQG3yeD/9RuKEB60awOUpSjReX/L2TIqyCapk7cA1bOMo4jlDL/Gtgzy/twFsoLYXnwxywdA10L4Swg5V7eEhIl7gASEyRySFDYGMj98FPgWaDjS/jIm+qstzKLAB7sNjNgBsyAGTADZsAMNMXA3ZSS927eViehfNldYH+6JjPhUJCJD+nXxOib6HONYQLb3wTqn6pPg4V5+KNjPqIUpmNo+0+VyGw9wrg95jk0A2bADHQUA3rzrMHbG+ArYFbQyFB2U3d3DSAvblRoCPl/Z18NQltFfrA8hGrU3fU7TdbrNsppUFw024EKxQF+rNtcWd6xMSMX3kpckN0A+rsmT6WlekSDfbJ4Ptgm2/9LuczojpfLStf/0HmiKBK3HUNE+fPGDIdmwAyYATNgBsyAGRiYgbA5XQi8JgNTJwJ93rA1oM8bvtqzr76KFOjH6CsRaXwRQl68hf0mHzucQPpuQD8kdIMzwLUg6++FdYnTBw0fz465HXHEiVArcEw+pGMjzcCNnECwmQEz0GYGdCO8BmgwJ+WWm2lyJZDr/DlArl9SaycClbkfSHQYbpOr/UItQm/Kbx7uCmXH09xAqdlqezPYLduvSAEPwbTu36yplK77fUDcRxtHRO3kodvQonCxSq5EFAQ2yuUpKo8JHS+W5SGf/pZWJowm4UGChlBrF5DxHsge6LWbnTYDZsAMmAEzYAbMQCMGwlfohuCtEFiPIRUZfti3ZFiD/CfBWwDvzXAayPU59KWI8E/wPtBxrgZL1RzjQPIkNmj7o+CLfbc7NcoMWGgYZcJ9OjPQHwMa6O0FbgLyDMgPqOXB8Dy4EmwLcjdfUsWwkbyh/J4m5vkYKP4C5WcuBi29tZiWmMQSHp59bBNSmgZzLpAQsCXQb+BlsDRoZPWEhsUpPB7cA/Q7WQmcACQSnAmi6ffzELgGrA1WAOL4DaBj1JpcD/9dm+m0GTADZsAMmAEzYAaaY0DCQaCPIa+FRhbGsR2PhUYWZme7vEEbWOrtMK7BRmePLgMjOS4Y3Zb4bGagYgxMQ3vmBkuABUE/N2W2FsNG6oaCyt37ze+BBIb8dr3dL5qdR4VuqVOpzcl7Gqj+Eh3uBGuC/qye0KDyEg7uBZELiQzHgtqFHDcmL55TnjJXANWj1mYigzcIyTdqNzhtBsyAGTADZsAMmAEzYAbqMDBS44I6p6pWlt4828yAGejLgG4osoEGyD2lmv97GEU/0nzx3pLyCtkZ4EJXGFuHmlwL1J56goNEJQkD+hLEUG1+DoCrYboQkoSEejY1meOAFn/EVbGuaarHQUDTe16vW8KZZsAMmAEzYAbMgBkwA2ZgMgMjNS6YfAbHzIAZGBEG7uKoQpHMymVzV+MsimndjTKY3Bf/B/YsQ2VdRzNgBsyAGTADZsAMmIFCMOBxQSEugythBgbPQHSLH/yeI7eHbyjNcbswxbRWQ80iRs3tPMqlduV8Wu9Bng82M2AGzIAZMANmwAyYATPQDAMeFzTDUp0yZZgvX6fazqoQA1rkz1ZOBp6i2qyMnGjtiYcL3gRNl9gHaDFSmxkwA2bADJgBM2AGzIAZMANmwAyYgVFlwMrlqNLtk5kBM2AGzIAZMANmwAyYgUIy4HFBi5fFHg0tEufdmmZAX53Q53tmAHqbrE8WaiE+v1mGBJsZMANmwAyYATNgBsyAGTADZqBqDPAdWJsZGHYGJCp8BehrBG8BLcIn1/r/gpfBG+Aa8GlgMwNmwAyYATNgBsyAGTADZsAMmIEKMWCPhgpdzII0ZSHqoc8eaqHACeA+oE8OClqIb/YMaxGuB84De4Pngc0MmAEzYAbMgBkwA2bADJgBM2AGzIAZMAN9GJCw8DbYHczcZ0vfhKZTHAw0heKSvpvanvJcrLZfAlfADJgBM2AGzIAZMANmwAy0nQGPC9p+CVwBM5AkWtlfn6uUyNCsHUVB7TNvszuMQjnfUEaBZJ/CDJgBM2AGzIAZMANmwAwUnAGPC1q8QF6joUXivFtdBlbMcq+su7V+5lVZ9gL1NzvXDJgBM2AGzIAZMANmwAyYATNgBsrEgIWGMl2t4tf1uayKaw6iqhtQdhLQYpE2M2AGzIAZMANmwAyYATNgBsyAGTADZsAM9DKgxR4fAPqqhL46MStoZHOy4VtAazRc3KhQm/LtItUm4n1aM2AGzIAZMANmwAyYATNQIAY8LmjxYvirEy0S593qMiDRYBvwG3AKOB7IU+EVoM9ayoNmNjAHWA7o9ydh4gvAZgbMgBkwA//f3t3HynLWdQDn9pXa9Nqi1pdSrU1F2oaWFgIhEYtGSxSDUvAPU6pNTMWqGKkRfPtHgUAj2sRIMAKJIpFGEyAS2yqC0ShIkaJIC9JYxIrAFRvhprSX3t76/XF3wpzN7p4zZ+bsPTP7eZLv2dlnZ55n5rP7dO8+nZ0lQIAAAQIECBAgQIDAQoEDqb0x+UByJKmLPTapyYjPJnUdh2uS/fj1HTOXeWIUAgQIECBAgAABAhsu4HPBLl8AzmjYJZzNVgrUpMIbZjk1t3UWQ/3U5cNJTTIcTRQCBAgQIECAAAECBAgQmKCAiYYJPqn77JAeyf4cmmWf7ZrdIUCAAAECBAgQIECAAIGhBfbjaetDH6P2CBAgQIAAAQIECBAgQIAAgTUJmGhYE7RuCBAgQIAAAQIECBAgQIDAJgiYaNiEZ9kxEiBAgAABAgQIECBAgACBNQmYaFgTtG4IECBAgAABAgQIECBAgMAmCJho2IRn2TESIECAAAECBAgQIECAAIE1CZhoWBO0bggQIECAAAECBAgQIECAwCYImGjYhGfZMRIgQIAAAQIECBAgQIAAgTUJnLKmfnRDYGwCl2aH/3FNO10TfpcnR5Nja+pTNwQIfFXg8Vn8cmL8fdXEEoF1CdT4O5I8tq4O9UOAwFcEDuTv6cknksNfqfFnkUB9Jrh70QPqVguYaFjt49HNFHjHmg/71PR3WlL/yPJBZ834utt4gfqHVr0Xmujb+JcCgBMgUBPtzfirMagQILA+geb974x0aaJhuXtNMqz7s8HyvfEIAQIEOgicn3VrkuGmDttYlQCBYQSenGZq/P30MM1phQCBDgJXZt0af9d12MaqBAgMI/DsNFPj75phmtMKga0CrtGw1cM9AgQIECBAgAABAgQIECBAoIeAiYYeeDYlQIAAAQIECBAgQIAAAQIEtgqYaNjq4R4BAgQIECBAgAABAgQIECDQQ8BEQw88mxIgQIAAAQIECBAgQIAAAQJbBUw0bPVwjwABAgQIECBAgAABAgQIEOghYKKhB55NCRAgQIAAAQIECBAgQIAAga0CJhq2erhHgAABAgQIECBAgAABAgQI9BAw0dADz6YECBAgQIAAAQIECBAgQIDAVgETDVs93CNAgAABAgQIECBAgAABAgR6CJho6IFnUwIECBAgQIAAAQIECBAgQGCrgImGrR7uESBAgAABAgQIECBAgAABAj0ETDT0wLMpAQIECBAgQIAAAQIECBAgsFXARMNWD/cIECBAgAABAgQIECBAgAABAgQIjFrg9Oz9O5Nnjvoo7DyBcQqcmd1+V/LUce6+vSYwaoGzs/d/kVw86qOw8wTGKXBudvu25MJx7r69JkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhLObWNAAAR10lEQVQQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhsK3DytmtYgQCBnQjUWHpW8ozkaPJA0rU8MRtcldTtoeSRZFkZor9lbasnMDaBIcbDTsfftwbn7CV5MPXHxoZnfwn0FBhi/DW7cEEWnpf8a1Ox4HanY3XBpqoITE5gnePP+9/kXj4OiACB/S7wHdnBjyWPtXJ3ls9Pdlp+IyvWxELTxtEsv3zJxkP0t6Rp1QRGJzDEeNjp+Ds3Os0YXXT7pNHp2WEC/QSGGH/NHhzMwj3J4aZiwe1Ox+qCTVURmJzAOsef97/JvXwcEAEC+13gQHbw75IvJi9OLkpuSL6UfCo5M9mufH9WqA8tb0+uSJ6R3JFU3UuTdhmiv3Z7lgmMWWCI8dBl/F0drBqX705uWZBvSJ1CYFMEhhh/jdU5WWje95ZNNHQZq027bglMVWDd48/731RfSY6LAIF9K3Bj9qw+eLxkbg9rsmFR/dxqj/uaVHwy+a+kTn9rymlZqPr7k3Z93/6a9t0SmIJA3/HQdfy9Img1rq+aAp5jINBToO/4a7p/QRb+O6mxdSRZNNHQdaymGYXApAXWOf4K0vvfpF9ODo4Agf0o8IHs1MNJfWe7XeoU0IeSD7YrFyz/QOrqH1evXfDYq2eP1fdVm9K3v6YdtwSmINB3PHQdf28LWl2D4awp4DkGAj0F+o6/6r4Zg5/P8vOTu5JFEw3Nejt9r0wzCoFJC6xz/BWk979Jv5z25uBO2ptmtUpgIwROzVE+NflE8n9zR1xfpfh4cnlS6y0r9TWJKncev9nyt6l7+qx2iP62dOAOgRELDDEeuoy/omrGe51x9GPJy5LnJmckCoFNEhhi/JVXXY/oVcmTkj9PlpWuY3VZO+oJTEFg3eOvzLz/TeGVs+ZjOGXN/emOwJQE6jul9YHjf5cc1AOprzeD+t52nRa6qHzjrHJRG7V9lfOO3zxuiP5mTbkhMHqBIcZDl/FXp27Xh6H/ST6ZtM9quDf36xotzeRgFhUCkxYYYvwVUF3vpLJd6TJWt2vL4wTGLrDu8ef9b+yvmBO0/85oOEHwup2EwMHZUdQpn4tKM1Gw6oKQq9qY337VutX//PqL9kkdgakIDDEeVrUxP54uC1y9Z9Y/8F6ZXJJcmrwmuTB5V/KERCGwCQKrxk4d//z46Wuyqr+h++q7r7YnsNcCq8ZD9T30mLgsbXr/2+tndYLtO6Nhgk+qQ1qbQF2bocqyCbvmIo6PHl9t4d9Vbcxvv2rdanx+/YUdqiQwEYEhxsOqNubH031xq69L3J/8Q8vwV7Nc6748uSn59UQhMHWBVWOnjn1+/PT1WNXf0H313VfbE9hrgVXjofoeekx4/9vrZ3Si7S/7gDTRw3VYBAYV+Gxaqws5Lvu/mE39F1b02nylolm3vWpT12w/RH/t9i0TGLPAEOOhy/g7FKxbk/YkQ+P3ltnCFU2FWwITFxhi/HUh6jJWu7RrXQJjFFj3+PP+N8ZXyT7YZxMN++BJsAujFaiLWNV/fJsJgfkDqfovJfMXimyvt5N/PH16tsEQ/bX7tkxgzAJDjIcu42+VVV23oUpzOuvxe/4SmK7AEOOvi85QY7VLn9YlsF8F1j3+Vjl4/1uls+GPmWjY8BeAw+8t8LG0UN/V/vq5luoCkBcnH0pWfXWitq9y1fGbLX+bujtbtX37azVlkcDoBfqOhy7j72XR+rekvj4xX548q6jHFQKbItB3/HVx6jJWu7RrXQJjFVjn+PP+N9ZXif0mQGDUAtdk7+vrE/X97Hb55dyp+he1K5csfyT1n0na/zf0a3O/To37cNK+lsoQ/aVJhcAkBIYYDzsdfy+MWI3pjyYHWnq1fEdSj313q94igakLDDH+5o3uSsXh+crZ/Z2O1SWbqyYwKYF1jj/vf5N66TgYAgTGIlBnBd2T1FkLr0y+L3nV7P7bc9sul+VOfRj5l3Zlluv/kFZ9nf1QExM/mtQ/turUuCuTdunSX3s7ywSmKNBlPPQdf3VxrfcmNVb/JrkueUHyV0nVvTFRCGySwBDjb96r3vuWTTR0ea+cb9d9AlMTWOf48/43tVeP4yFAYDQC9bWJ25NjSX3gqPxl8k1Juyz7oFPrXJs8kDTb1/JPJovKTvtbtK06AlMT2Ol4GGL8nRO8NyQ1CdiM1fp5219KFAKbKDDE+Gu73ZU7yyYaar0u75Xtdi0TmKLAOsef978pvoIcEwECoxE4K3v6tGR+gmGnB1CnYF+UXJqcvoON+va3gy6sQmA0An3HQ5fx9/ioPCW5YDQ6dpTA3gr0HX9d9q7LWO3SrnUJjFVgnePP+99YXyX2mwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBCgZMW1qokQIAAAQIECBAgQIAAAQIECHQQeGHWvS15MHlPcn2iECBAgAABAgQIECBAgAABAgQ6C3xztng4+fHkYPIjyZHkxYlCgAABAgQIECBAgAABAgQIEOgk8PNZ+465Ld6R+2+dq3OXAAECBAgQIECAAAECBAgQINBZ4EC2uC+5ufOWNiBAgAABAhsicMqGHKfDJECAAAECBKYjUF9fuDe5u8MhXZh1L1+w/mOpq69GPJB8ZLacm6XlF/LI1yW/t3QNDxAgQIAAAQIECBAgQIAAAQKjEbghe1qTA7/YcY9fOtuutl2Wh/LYqmsv3JTHDydXJwoBAgQIECCwRMAZDUtgVBMgQIAAAQL7TuCHs0ev77lXb8r2t861cXbuX5n8XPLHs8fa12Cor0v8dnJ98tzkfYlCgAABAgQIECBAgAABAgQIjFSgvqpQH/ybrzn0OaPhFSsMfmbWx+2tdWqSoSYn/jO5tFVvkQABAgQIEFgicNKSetUECBAgQIAAgf0icFt25Nrkz5Kf2sOdekvaPpY8u9VH/azldUl99eLk5LJZvj23CgECBAgQILBAwFcnFqCoIkCAAAECBPaVwIeyN7+W/HXy/D3cs0vSdv1PmM+0+qhrQZyWvLNVV4t11sMPztW5S4AAAQIECETARIOXAQECBAgQILDfBeorDXtV6iyFJyZXJM31H36/1VmdwaAQIECAAAECBAgQIECAAAECExWoMxr6XKNh2S9OVP3R5NVJTT4oBAgQIECAwC4FnNGwSzibESBAgAABAvtO4GD2aP76Uw+nrtKUf87C3Uld5PGc5HuT05PfSW5ODiUKAQIECBAgQIAAAQIECBAgsCECq85o+PcYzJ+x8NqZS13MsR6b/9WJC1P3H8mjyU8kCgECBAgQINBTwBkNPQFtToAAAQIECOwbgfdmT+6Z25uPz92fv3tfKn4o+afkD5J7k/clCgECBAgQILBLARMNu4SzGQECBAgQILDvBG7Y5R59NNvVr1q8LvnT5JLki4lCgAABAgQI7EJg/nuMu2jCJgQIECBAgACB0QvckiN4f3JeUtdqUAgQIECAAIFdCpho2CWczQgQIECAAIFJCRzL0dQZEY8kL0m+K1EIECBAgACBXQiYaNgFmk0IECBAgACBSQrUr1HUxSPrFynemNSvUSgECBAgQIAAAQIECBAgQIAAgUEFasLhRcm1yVmzlq/K7QWz5bqpyYlVZSdtrNreYwQIECBAYDQCzmgYzVNlRwkQIECAAIETIHAwfdavUDwleXfys8m3zJa/M7d1Ye03J4eTzydvSr4neWbyR0mV7do4vpa/BAgQIECAAAECBAgQIECAwOQFfitHeKh1lHXmQv0M5peTM5MnJO9J6oyHX0k+mDw2y5/ktsp2bRxfy18CBAgQIDARAT9vOZEn0mEQIECAAAECeyLwnLT6/lbLNYlQF4z82+TB5KHk6uTRpMprkvOSo8nnkirPSVa1UeucljwrqTMjPpxUPwoBAgQIEBilgImGUT5tdpoAAQIECBBYk8AX0s85c31dlPtvndXVr1XMl0/PVWzXRq1eX8v4++T85EhSv4ChECBAgACBUQq4RsMonzY7TYAAAQIECKxJoL7+8LTk5Fl/5+b2jOTiWZqLQ84eXnizXRt1DYdvS+5MbkxenygECBAgQIAAAQIECBAgQIDABAXqmgy/mdQZB9cnv5vUZMPrkk8l9fh2ZSdtPD2N/GFyf3JLohAgQIAAAQIECBAgQIAAAQITFqgzGubPXji94/Eua6POkLh51tapuf1cUheaVAgQIECAwCgFXKNhlE+bnSZAgAABAgTWLFAXe6wLNbZLXUuhS1nVRl3r4fakJiNuTepCkwoBAgQIECBAgAABAgQIECBAYNcCdTZDnd2gECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQInFCB/weXXU3GvKq9UwAAAABJRU5ErkJggg==", - "text/plain": [ - "Plot with title “paste(\"PLM, |\", rho, \"| = 1\")”" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 7, repr.plot.height = 7)\n", - "\n", - "pdf(\"figures/401K-contour-plm-lower.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))\n", - "invisible(dev.off())\n", - "\n", - "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3f14d055", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:56:25.695962Z", - "iopub.status.busy": "2026-09-11T14:56:25.695610Z", - "iopub.status.idle": "2026-09-11T14:56:26.093514Z", - "shell.execute_reply": "2026-09-11T14:56:26.093039Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Plot with title “paste(\"PLM, |\", rho, \"| = 1/2\")”" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "rho2.half <- (1/2)^2\n", - "\n", - "pdf(\"figures/401K-contour-plm-lower-rho2.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))\n", - "invisible(dev.off())\n", - "\n", - "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))" - ] - }, - { - "cell_type": "markdown", - "id": "8c69bfb3", - "metadata": {}, - "source": [ - "Under worst-case alignment a confounder as strong as income crosses the zero contour, whereas the matching scenario and the one-quarter-income benchmark do not.\n", - "\n", - "The second panel makes a different assumption: errors in the outcome regression and in the Riesz representer are only half as aligned. The lower endpoint then rises substantially. This comparison separates the strength of a confounder from the degree of adversity with which its two effects align, and none of the observed covariates exhibits alignment close to the worst case.\n" - ] - }, - { - "cell_type": "markdown", - "id": "69a6db36", - "metadata": {}, - "source": [ - "### Nonparametric Model\n", - "\n", - "The gains are target-specific, so this specification needs its own benchmarks. Here `gain.D` is read as a relative gain in propensity-score precision rather than as a reduction in residual treatment variance.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "629fe818", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:56:26.095791Z", - "iopub.status.busy": "2026-09-11T14:56:26.095475Z", - "iopub.status.idle": "2026-09-11T14:57:52.635257Z", - "shell.execute_reply": "2026-09-11T14:57:52.634727Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13370.1324-0.221
pira0.02310.0000 0.000
twoearn0.00820.0000 0.000
\n" - ], - "text/latex": [ - "A matrix: 3 × 3 of type dbl\n", - "\\begin{tabular}{r|lll}\n", - " & gain.Y & gain.D & rho\\\\\n", - "\\hline\n", - "\tinc & 0.1337 & 0.1324 & -0.221\\\\\n", - "\tpira & 0.0231 & 0.0000 & 0.000\\\\\n", - "\ttwoearn & 0.0082 & 0.0000 & 0.000\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 3 × 3 of type dbl\n", - "\n", - "| | gain.Y | gain.D | rho |\n", - "|---|---|---|---|\n", - "| inc | 0.1337 | 0.1324 | -0.221 |\n", - "| pira | 0.0231 | 0.0000 | 0.000 |\n", - "| twoearn | 0.0082 | 0.0000 | 0.000 |\n", - "\n" - ], - "text/plain": [ - " gain.Y gain.D rho \n", - "inc 0.1337 0.1324 -0.221\n", - "pira 0.0231 0.0000 0.000\n", - "twoearn 0.0082 0.0000 0.000" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "set.seed(2)\n", - "invisible(capture.output(\n", - " bench.npm <- dml_benchmark(dml.401k.npm,\n", - " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", - "))\n", - "\n", - "out.npm <- summary(bench.npm)$benchmarks\n", - "round(out.npm[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2aacd3da", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:57:52.642707Z", - "iopub.status.busy": "2026-09-11T14:57:52.642270Z", - "iopub.status.idle": "2026-09-11T14:57:53.089117Z", - "shell.execute_reply": "2026-09-11T14:57:53.088690Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Plot with title “paste(\"NPM, |\", rho, \"| = 1\")”" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "pdf(\"figures/401K-contour-npm-lower.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", - "invisible(dev.off())\n", - "\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "5f1eab78", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-11T14:57:53.091609Z", - "iopub.status.busy": "2026-09-11T14:57:53.091253Z", - "iopub.status.idle": "2026-09-11T14:57:53.519948Z", - "shell.execute_reply": "2026-09-11T14:57:53.519500Z" - }, - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "image/png": 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IhTtJuhF36JwvXSHOdLiWN8brLCAAAQhAAAIQgAAEIAABCECgDwIXKq/FOna4oYe0J/uOGlPHYU371h6zb42mfUn+NMsHlC8JhW8u8l1N5flcBS4NqTtEvNxfSMfKTtrg+/zl2dO6fF2a2LF+2FL8n0tjG45uUfVC2vZ0vP/dLfb7prmkz0q9HR4R4I6gZvF5CZI2eo993cQnIU3sX62D8R5t42nd6dSNuJPhXGlSz1+1PrGbAkgLAQhAAAIQGPvQBBEIQAACEIAABNIR8CEUSVi8Rzjk8XI+TeX6C7M7AVaUunMjjfhL4jlxwmT4RHO+7eIfZ2npaesgb5GR88aGjjeswIeI/CpOs6yWS8frw7hInFJpzxln5JEzv5d6NIiLR0K8V+pDJxAIQAACEIBAagITUqckIQQgAAEIQAACzQQ8wsAdDD+VuuP+ZOkbpVOlaeVyJfToh7HiL8webfCwtFdHgPdkf0E6h/T90qRne2GtbyJ1+dnIohZ/l2+y8pGm9bGrHuL/0Xijh/6P55QYm6/Tbz+2O3VK1GL/Y9rW6hi3SJrZpjlV0lvj0tI6GpZU+t9J3xDnu1zLD0g9igSBAAQgAAEIdEUAR0NXuEgMAQhAAAIQmInA8fr1Ian3Hq8o9cgDD1lPK/7C/Me0ibtMd43S3yP1Xv1tpImjwe31+//tUn8pr4s0OxrGDolobkOzE2KZ5h19ri+n/D7vRbdypzIU7Wh4u+qcLL1Z+pC0k6ylBBdIF40TugNqR6lH1CAQgAAEIACBrgngaOgaGRkgAAEIQAACMxHYVb/8hc7D+j8vzXICQhXXl5yt3D5vw/ukPn+BR0o0D5vQz9qIh/Un8lqt/C/5MWbZ7GjwiRCzkhdUUJqX9rH1Ndszdl9evzeNC74oRQV+bvh54ueHy5HS/5P2GknjZSAQgAAEIDDkBHA0DPkJQPMhAAEIQKBvAj6EwocoeHSDD6E4SfpbaRXEoxjc0TCHdDPpn6Vvkbp4r3VdxR0NHinQSdKk6VRGst+P6euTHxVfvie2r5OjwZ0M50l9AsiXpLtL/TxGIAABCEAAAn0R8AciBAIQgAAEIACB/gicoOzJS53PC+BRDlWQG2TEP2NDfMjEB6V+779emuVLuIorVPzrEePJa+Idr2iZtH28tIO43YeLLC99XuqOpfHEv1rhX5dwJ4NHa2wpxckgCAgEIAABCPRPAEdD/wwpAQIQgAAEIOAE3LmQTJw3d4WQeFSDy3ulHwtr9Y5m8CbsELej1cIdPS6nSZ8Ka8P1Jxk2cZma/eI4TW9o+w+ls8f7t9fSP+WKQAACEIAABDIhwNCJTDBSCAQgAAEIQCB6UAx8CIVHN1RJ3NHwZak7P9aT+tj7xPmg1dRyi1KuHKdeVUv/XZYsp4o9OsN75JvFX6A/LvUeem/zMEriaLi4TeM/oX0bxvt96M86scabxl0crj3PjLuXHRCAAAQgAIGYAI4GTgUIQAACEIBAdgROVFE+RMGjB6oiPlHlrdLESfBnrfvLZV3FJ3i8T3qK9F7pddJEjtLKotJdpO74GTbxYRBvjxudDOVpxWDfpo2La33/pt/tVo/VThwN7QixDwIQgAAEAgGGTnAiQAACEIAABLIl0DyEItuSey+tOYKhzpNAOgH/2sQGUh8aMSVWjyLxCAt38LxFWvc2qgk9iUcpeOTKPdLx5qdwZ0QyvESrCAQgAAEIQAACEIAABCAAAQhAoDwCPjTEJ1mcPycTLlW5PrTj6BblnxPv82iGRPyl2YeDuINhoWRjm+VG2uflu87TJl1dd71Bhvt8CxvXtQHYDQEIQAACg0GAoRODcRxpBQQgAAEIQKAIAv5Sf4e0KpMsTpMtVxfR8JrUcZvsdEUgAAEIQAACpRJg6ESp+KkcAhCAAAQgUBsC28hS/wTi72tjMYZCAAIQgAAEIFAKARwNpWCnUghAAAIQgEDtCOwhi32yy31qZzkGQwACEIAABCBQKAGGThSKm8ogAAEIQAACtSWwhSzniwO1PXwYDgEIQAACECiOABENxbGmJghAAAIQgECdCeBkqPPRw3YIQAACEIBAgQSIaCgQNlVBAAIQgAAEINCWwFe0d2Hpv1qk+ou2+dciHm+xL+0m/wTmVnHiF9JmIh0EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwRAQaQ9RWmgqBtAT2V8Kt0iYmHQQgAAEIQAACEIAABCAwsATOU8uOHNjW5dSwV+VULsVCoM4E3MmwSp0bgO0QgAAEIAABCEAAAhCAQN8E/J2ADsgeME7oIQ9ZIDAMBG5RI9cfhobSRghAAAIQgAAEIAABCECgJYGrWm5lY0cCRDR0REQCCEAAAhCAAAQgAAEIQAACEIAABNISwNGQlhTpIAABCEAAAhCAAAQgAAEIQAACEOhIAEdDR0QkgAAEIAABCEAAAhCAAAQgAAEIQCAtARwNaUmRDgIQgAAEIAABCEAAAhCAAAQgAIGOBHA0dEREAghAAAIQgAAEIAABCEAAAhCAAATSEsDRkJYU6SAAAQhAAAIQgAAEIAABCEAAAhDoSABHQ0dEJIAABCAAAQhAAAIQgAAEIAABCEAgLQEcDWlJkQ4CEIAABCAAAQhAAAIQgAAEIACBjgRwNHRERAIIQAACEIAABCAAAQhAAAIQgAAE0hLA0ZCWFOkgAAEIQAACEIAABCAAAQhAAAIQ6EgAR0NHRCSAAAQgAAEIQAACEIAABCAAAQhAIC0BHA1pSZEOAhCAAAQgAAEIQAACEIAABCAAgY4EcDR0REQCCEAAAhCAAAQgAAEIQAACEIAABNISwNGQlhTpIAABCEAAAhCAAAQgAAEIQAACEOhIAEdDR0QkgAAEIAABCEAAAhCAAAQgAAEIQCAtARwNaUmRDgIQgAAEIAABCEAAAhCAAAQgAIGOBHA0dEREAghAAAIQgAAEIAABCEAAAhCAAATSEsDRkJYU6SAAAQhAAAIQgAAEIAABCEAAAhDoSABHQ0dEJIAABCAAAQhAAAIQgAAEIAABCEAgLQEcDWlJkQ4CEIAABCAAAQhAAAIQgAAEIACBjgRwNHRERAIIQAACEIAABCAAAQhAAAIQgAAE0hLA0ZCWFOkgAAEIQAACEIAABCAAAQhAAAIQ6EgAR0NHRCSAAAQgAAEIQAACEIAABCAAAQhAIC0BHA1pSZEOAhCAAAQgAAEIQAACEIAABCAAgY4EcDR0REQCCEAAAhCAAAQgAAEIQAACEIAABNISwNGQlhTpIFAIAZstimzO9lWlSdO+hPZ7bZ72+4vea/MWXWN29VWNZXYtm1GSTdA5O8eM36xBIA0Bm6TzZnKalKRpR8D0HGdzt0vBvn4JDMN1vF9GWeeHedZEuy+vzs9e3beWHPkQwNGQD1dKhUCvBL6pjNd3yPwt7b+uQ5oed9tEZXxID66f67GAjLPZZirwv7JnyYwLLqC4YPOTsn3zAiors4rvq/KryzSAumtJ4DRZ/adaWl4tow+UOXfrOjNftcwaFGtsK7XE70FrDkqLqt8O20g2PiXm762+rYNqob1bLXtCx8CfwRAI9EwAR0PP6MgIgVwILKRSF+5Q8vza/9oOaXrd/YoyziOtyou9Ijwi9ZhHy0nrJv+TwW7/unUzvEt7H1X61fRAktc52aU5JK8JgRtk53o6bzaoib1VNXOKDPN7xj5VNbDmdv1V9j8v/aHOVZ6ZizmYfm24T3qSmHd6HirGouGrxTsP7pKeqWOw0vA1nxZnRYCLZlYkKQcCxRF4SVX5y3cO0nhZhb4grUoo7sNxI2v4EttQb0DkL+FviNswqIsr4oZ5LxQCgbQEjlfCF6V7pM1AulYEGu5o+IN0b70QuKMayZRA4xEV92WpO4w/lWnRFDYOgYY76T8m9fP5xHESsTlXAo2nVfyWcRW/0rWFiKlceQ9u4TgaBvfY0rJ6EjCZ3ehg+nTt9yEOeYnf5D2qoQriD3kui44savf3Nlm8cu2s7s5g7/Fz59dbu8tG6uEm0PiP2n+2dBs9xC4y3Cz6bv1XVIJfs/fruyQKaEXgOG30IY2H61ylh70Vocy3Nfy+8nXp+8V898yLp8AUBBq3K5E7fDyi9Gc6DrwzpqBGEghAAAKdCFylBK4liJ2ii7n3hLcRO1ZpPPIgJ7G7VP5vciq8y2LDhHEazmE+L0UNxX4g26dJc4pAqQoSU5il/b0q1mBHXQiYD52Qc9V8ngGkLwL2a3F8XlpXp2xfrc8/s60rtrrv2sn510UNIwTCRMNyOITzetAjAyt80P36HK7TR1bYyLxNK/G9IO+mUT4EIFA0gRIvKP4QY0+2b7AdHV/0c/Iu240q/7L2NhS519TzaWcUWWN2ddln42O1UnZlVrEkdwSZO4QWqKJ12FRlAvY3nTf3S30+E6RnAvbG+H9QjmgkHwLm8zS4Y4xhYvkAblGqLSPez0j9uWRyiwRsKoSA/Vz8/dz/SCHVVa+SEt8LqgejG4tyelHpxgTSQgACXRLwoRMueQ2feFZlV2XohLfzEWlde+lu9QZIBn34xBS10Yf8bOiNRSDQBYHvK+3i0s27yEPSWQg09CIWnSvVPALmPJHsCXjkzeNSDaUIX2jKvgZKHEOgcbc2+Dwua0iPGLOTn8UR2ElV+TXmBJ37axZXLTXVnQCOhrofQewfNALyGHeco8HHw7vkFY5fpTkavJ0PS+vqaPA5GlwGPezzz2qjn7v09PnRRrohcJYS+3AxJoXshlrrtAdpszugv9J6N1v7I9DwaMMvSleR7tNfWeROT6BxmtLq6wfRXrrNbJo+HymzI9DQsKzoA9LnpOfrOCySXdmUBAEIQGC4CFyl5rqWIHaSLuBPta/Y9DAZQtjmb5+u172mCdrsoV5zZ5/P9JDRad6K7GvNrkQfClPXoR/dULCb1E6Np0Ug0C0BO0rnjg+9WbHbnKQfSyBcLxX1ZsuO3cPvrAjYpeKrFy5bKqsSKacTAZtXvO+WPiLlJbcTrtz228bi79cXRTEOVVRPie8FuR3MQgomoqEQzFQCgdQETCnTfHXCCxymoRMa+1/b8Zke1TDoEQ1+Pl4hXUvHaU7/MezymD4ROy2auM5l+UUeDRJijX0PETGfGaRGldSWQ+J65ZBGciKwu8r1iEJN9osUQ6CheRpGP3l5UjF1UsusBBp/0jZFloSvTH1v1v1sgQAEIACBTgRK9FzaiXrefrq9gbaf0sghYa9tn67XvfZdlf2ytJPDo9cKusxnX4jbu2SXGSuS3HxMo/d+VYRnXlh8kqhwXm6SVw11KVezG06cFk26c3o0WV0/k35RF7vLtdMu0Pmja5/NXa4dg1C7/VQc/Ro+DA7Okg6YfU18/T78oZIMGNJq7asx988NKYCKNDtcY/z8P7YiBuVtRonvBXk3Ld/yiWjIly+lQ6BbArpwlx7R8D/Z4NeGubo1Pqf0yTCO1+VUft7F3qoKvJd/6bwrKrn8KXH9G5VsR+nVrxxFr2lEjeVjQ7Z+fmSyw9LtqrgBPimkwqOjHSpuZx3MO1RGKrw58iWSD4HDVey/pHrRsvnyqYJSWxA4TNs8eu4ocX9ji/1sKoaAz6lzl9SfWREIjEsAR8O4aNgBgcoS8AdIl7yGTrijwaUqPYv/HjEnWixe1m3hjgYXvX8OsjTcIeQPHhsPcivTtE1epQctsjtH0jYaE6PJ26TJN+RpLlb7ndlnh5xDBs1v3K9CfiT9oN4D1s6gQIqYhUBjqjZ9WuqRhd+YZTcbciLQUKROGEKhKMHoLJ3fVekQyam9VS22MU19YstJfRgFAoFxCeBoGBcNOyBQCoGqRDR446vyiUt/gXWpa0TDLSPmD7qjIbTSx2+ur4e/SXGbh3ahf+Szk8bL6fDhZJ3leAQafu3zqAaF+9u7xkvF9tQEDlPKZ6Xe847kQqBxqYo9RbqbztkNcqmCQlsQaDygjbtIffJYv2YgEIBARQngaKjogcEsCLQhUMTnLb36qjgakoiGujoa/KHIH/j9k2iDLu5omF263qA3NEX7Rh0NGkax3gtRtESKPMOe5GQB8IiqPYcdRP/tbzyuMr4tldPG3t5/eZQwDoEvavt/pT8R54njpGFz5gQa+sRidJx0R3HX/EAIBCBQRQI4Gqp4VLAJAu0JFDV0oiKOhhCi+oSQLNYeS1X3hp5a//LEgA+dCPynxEdh6OdpmBxNu0mRDLcnZ+UEhk8kKNosG+5kOFm6mV4elmmTkF3pCHxLyfzaSVRDOl49pGr8R5n2ka4q3beHAsjSOwHnfpNUw4S4XvSOsd+ctpL4r9NvKeQfTAI4GgbzuNKq+hKowtAJ7313qYijIdjykP7WNaLBG+DDJzwkvOE/Blca96pt90s3Htw2pm+Z/pnPSVIzfCIh0XHpodD+f7JHx5Qk6EAgfBLwCCXy4UxbdEjM7p4JNE5V1kukXxFnjVtHiiEQOiG2VV3+qdGzxJ6IkmLAj63lS9pwgfgvNXbHjN/uiLD5Z/xmbVgI4GgYliNNOweJwJBFNIRD58Mn6uxo8Akh55IOQ/i8D594sx4q/OFv2KV5+MS6Gj6x1LAD6dz+xp1K4xND7qxzyP9nkP4IuOPGr5+HiSfPfP2xbJd7t3ineteR4gg0PFrQJyT0HnWd40gJBPzcf1B64azOBFMkqp2ifVdLz9U6ziCBGCbhpjNMR5u21oFAmoiGYZujwY/bQ9KaDp0Ip507GlxWGVkM9F93NPgL4toD3coUjdPwiZsVyeAPwkEYPpGQ6Lj8nlJ479f2HVOSoAOBhvxb0dekHtr/0Q6J2d0zgYZ/6vJQ6Tv0MrVDz8WQsQcCjeOVyZ26XxT79/RQAFn6ItB4Ttk3l3oU7O4jRdlkHYsDtX6HdHXpx6R+DfqxFIEABCAw1ASuUutdSxD7iS7OftFuI7aZ0sghYe9sk6iPXbZ4XL6Pf6yImB6UQ5tr+r1yDykM9utBaNDFlo/but+gtzRN+16MJh08PZpsrtOiSdekyUMaH2Jk/5TeAossCHh0kelF2O6S0qOYBdKWZThb05wB9rh0wZZJ2JgTAdOzgd0tfVT62pwqodi2BExRp8nwUPul1v059VvSuFPb1tP681J3QNRNSnwvqBuqme0lomFmHvyCQNkE0kQ0TI+NzOuB8X9x+XOXDaOpfg/9dVlsZFG7v/fJYncgDcGEkA29IEYPSzeWIk3zNOjrE+uoe3lpoHQiMPqpS/2/mHqIkf4INDwKTvMHRMtIP9VfWeQen0DD782flrqT4ejx07EnewKNp1XmdtIFpKfPeLnNviZKHI9A4yHtSZ7R9tf6l6QfksbOhoYPn/BoH+848ugGBAIQgMBQEijRc2kKK3OPbzvxT5UFT3FOk3vZbHH5FXpQMoXlhTbnFMXRjndW++xvaoPfaIdA7Ey1VQ9+fi4himS4JYlqmB5NOgAiaQjYvDp/npH+Ok1q0nQiEKJErhdP7/GtkhO5k+E13G/HiXGOUYc1RFKYybZPzP7gwqqkopiAfVTsH5E2RZ6GKAc5Om2XGZisjkNIS3wvmEGujmtENNTxqGHzIBOoQERD42UB9nG9Pt6uKuKecpfXjyxq+fdmWe09tAoLH3i5XC3Ui2K05sC3NEUDdcDPSpLpH9x7eJCOBMIXE05WMh8qtlzH5CToQCBEiXgv4yLSfTskZnd/BNyZ6Pcs7ziYs7+iyN0lgWOU/jdSRfAQDdUlu36Tb60CHtRHgzy6JJYQ5XCpfmyabNF+hsTNgDHwazgaBv4Q08ABJJD30AlH5sMnquRo0M0ryOviZR0XfnP1nsQl62h8lzZfHqd/W5f5BjL5y1FDjgZ7ZaRxjabenoFsbpaNOlaFuWNuzywLHd6yGn9Q212913fR4eWQd8uDk2wP1eJDVQ7NuzbKbyYQHGo7aos/M5zBed7MJvd1j8a9Y+ZaQvSUD5NoitQ1DXExOR/sXunF0uVnzsMvCEAAAoNNoMQQKfuhLrpT2+M19RKHsMzt26frZ2+YiO2CfkrINm8I+52mdiskta5i74uPm3poh0FM82rYb4ehpWnaOC2auIOGUJyl5fpp0pMmIWDqnQxDKDxCBumbgL1JPOX08nsNki8BcwejIgRt3XzrofRZCZius5p/d+SFlk7VWQHlsMU2Ee9npe8YKTx8eeJ4/fb/Ad+3qPQ86YvSH0h9SKxH/fgwy6p3IpX4XpDDoaJICECgVAIlXlBSORpW10XZx3/ulB8lu17l/ym/8nsp2e6VTb/qJWc18tgS8XHbrxr25G1FmKfhGbWZeRryRj3Q5ftDa7jefWGgm1lo4+x0MZ0uXaHQaoeuMltEjP8jvUk6aeiaX3qDTdeMcO04pHRThsaAwNznZLhN+pj0BeluUkX3mIYThU6stWbGYb/Q9sNm3la5XyW+F1SOBQZBAAJ9EijxghImkZK3t53YG3RRdkfDp9ql6m+fOxnc2VAlsStl03VVsqg7W0JUhr94n9pdvrqm9vMznKf05tX1EFbGbvu7zqV7pTitMjkmtpRYeq/iuZkURyFtCNjHxdnv1we3ScSu3Ah450ToUY972XOriIJHCZjPRaWvr9iuUu9gUbSC3SPVZNg2/2iy0ZXwKcxvjf6s5kqJ7wXVBIJVEKgigblk1DpSv+BXeVK/Ei8oqRwNy+li7Q8uPgY0JzENm/DvrldJ7Oey6ZEqWdS9LaZzq87Okm5a7OMuw3k6JBEc3bAhbXcEbMf4XNqmu3ykHp+AHRMz3WD8NOzJhoBdKNbu2PGx6kihBGwBcb9XqmcH5iUpFP1oZSG68UbxbzE/kSm6ITiC3j6avJorJb4XVBMIVkGgDAILqdIzpfLgzyQT9OvL0qlSvSCP6iVa1zCAykmJF5RUjoYlhdAdDXvnR85+pvL1GbQqSXgw9rHFE6tkVXe22Amy/3npq7rLV9fUIVRSD9kIBPoh4GHn9rD0r/2UQt5mArageD4lvaJ5K+t5ELDFxdmj2bxHd0iu/Xlw7LVMW0/cfb6Gy6RERfWKsad8tpiYvyR986zZ/TOX4XO7dRgSW+J7wazk6rSFC16djlb1bfXPOH1EOmb8VXSUth0qdUeDxmJFPgmVLviRezB9ubQUGSEgB0KYZb0dD42tDZLnC7ceiir11Qlv8EPShvS1/qOmcrPsnkM6LOf85WrrW/Qw4c5GBAI9EmjoJSE6Trq+ziUp0j+Bxn9VxhFS///cov/yKGF8Ao0HtO8A6brSHDsIxrdguPc05OCJ9pe+TXqQFCmOwPyqyp07185cpSXP/7dp+3Yz7+MXBCAAgdYENBYrRCt8u2n3ilrXjLORv2At2rTdVzeU/k96hf+okJTouQwz8fpDdRsJE0zJIWEHtknU5y7TeLkQNVGhF8TwSSRvdwvPeJ/NLSy7vTvmumVhVZZaURij6cfMH7ARCPRBwBQxFyYWO7uPQsg6EwGT09MelN4q9ZcBJDcCYY6eKeL8nHSZ3Kqh4DYE7Hyx1/OovbNNInZlSsAjUO1O6delfg13PUzqx8EjZ+ceqc5W0vo5Ur0rhPRbZ2pG/4WV+F7Qv/FllkBEQ5n0h6Nu733y82xf6SNjmnylfh8q9TGiHg2B6AorCN5r304SR4Qu4LmJO4Bc5hlZVOKvHoiDaGKh2spNseWr1bYF3Rl+eZzcey8QCPRBoPEfZT5NqgdQn8wQ6Z9A4wWV8VXpG6S79F8eJYxPoOH39k9K/XnoBJ3Dne7z4xfFnl4J7KSMD0jPEP/Fei2EfN0QaHgE7rbS90s1/C1Epu6s5Qf1qOsR0LoG2f5a3iB9tdTfCfT/EX1P2zWhNAIBCEBgBoFWEQ17arffYF8/I9lMa+5Z9v1rzrS13B8lei7NL64az9ZObC6lETP3EOclo5+F8mNaEfGXi9Duz1fEoB7NsMfVjiHqlTU92NlFPcIiGwSaCIRer1d0Pn2naSOrfRHwOQPsH1K9BCS9i30VSOa2BEydLuE+9pm2ydiZEwFbR/xflP5ZmmdnTU7217VYj5jy4RKmKOfkU69h2y/1e6p0j5lbZnJO+KcwKyMlvhdUhkFPhrhnFYFAngSuiwsfrwf3vfF+93Qi6Qi4h9glz5vkMyNVRPPGyyos/i0j2jmtqmBjGhv0UF/JSVDT2N5LmsuUyceB53m+9mJXqXmmRxM2nBZN/Jb0jaUaUqvKG7fL3Auku+h88rG/SN8EGnLchIjDRbXcr+/iKKATgWOU4BrpkTqHl+yUmP1ZE2hcqxL3lm4oPSrr0ilvPAINDZVo6FmgcYdUUbkhoudkpfbORmnjB2Ny+vupJgFGIAABCMwg4L3f/iL4P6m/XHxTup1U47OiKdLZpYn4ReTDUg+H9f1VkhI9l6kiGsQu9Ij4A0tOErzJOpbmw1oqJOETVT+vkEE9mGKawySMT5yjh8w1zGIKVw3nqz/YISJwj66F06NJ/5seTTbpw3/L12k4YMxto/h8OmDAGlZyc+xicX1OSkh57kfCVhbnqdI/5F4VFYxDwE4Xf3/G2WacBGzOlYBtJfbucHjzrNXYZG33uRqOm3VfaVtKfC8orc1UDIHKEfDeb78w/EX6rFQX8Zl0Z/1OxNP4fnk5o/clGyuyLPGCYscKizPpIOFFVcMs8hJTpEm4Cb8nrxp6K9cUIWNX9pa3KrlGX7zXropF+drhvXbhXPpyvvXUp/Rb1FMjR8PzsaNBbxyTN6+P9VWw1NQrGT6dSo9XZofDFHUY7isnZlYkBbUhYP8XXxd3bZOIXbkRsDnF/yapojc9nB8ploD9SdybJ46Pqzd1SNrvpI9LX1esTW1rK/G9oK1d7ITA0BLwiAW/eGucVfQN6UXSt0oT0QtjpIt8CJtKtlVlWeIFJbWjwXtDfpQfMPNQd/f2fyi/Onop2fS9Zbuvl5zVyWNyMAS2zY636piXiyV2t9p8aS5F17TQadGksxJHg9bPrGkzSjJ7NOLqEyUZMKDV2gn6P5Wj21Yf0AZWqFn+yV9TMJM9LV28QoYNkSm2gti7o+Fm6VxD1PAKNDUw/+jMhthrdBwukz4hjYcUhq+N6dnB7pV61NXyM+cp7FeJ7wWFtZGKIDBQBOapcGtKvKDYd3UhfaUzG9PwFDu+c7peU/iDZhVfhpPPf/oEZnWV8Em5l8T3O3VtQfd220/VXneODclwkc6EXowmbZk4GhTd8OwjUcSDbmdscYowidg9Op98vhMkMwI+bCIMn9ADPZI/gRBF8qKYX5R/XdTQmoB3poRnnTNa72drPgTsSHG/XOqRVO500xCWMDT2Ni3fJF1Uep7U/z9+IN1c+mOpO+bKiHQo8b0gnyNAqRCAQHkESrygpHY0uMf3lPwQ2dIq3yMa9sqvjl5KHg031U2ozmK3i+0Q9fCHXgk/n95V56OWpe3x8IknEmeDHA9jeneyrG0Qy/JrU7hGVWx4V91Z2yEx103r3pJ62G9fiXkPUYRb1Y6MHR0fgz2qZtng2mPqbLSTpepYs2el3vlyhHR26TLSh6T/lK41MwP7hbYdNvO2Qn6V+F5QSPuoBAIQKJBAiRcU7+X2h+dOYo8qXY7h1rbgiB3+EFQlse1juzT8oM7in7c0nwh1SMQWVnv9gUK9GEhCYFo0+aeJo0FRDb9JtrNMQ8A/xWhPSv+QJjVp0hIIn0/+t7h6OLk+SYfkS8C/xmPXSxlCkS/oNqWHHvUrdAy893y9NgnZlTkBHyJha0rnGynaoxXMo9Wuls4/a3X2S23/1qzbc99S4ntB7m3LtYIahx/nyoXCIVB1AtNloB5QchMNzQgir3Ol5MHYmtdXyqrujfGQb3fmKFR5GKShiZ3CnCzvGIbWpm3jy9HLP2tK+x4NFtY5gaQj0FAvWPQj6Tv1f7RGujyk6kyg8ZzSfFm6inTXzulJ0R+Bht/Ld5SqJzc6XooUTqCh3vTwFTQ5LqNzdD1ZqHAThrbCxo36tKUcbQ052oJ8U399/d3a9tTIpuRviG7YUr9+m2xhWX0CGheDQCAzAv5gsmmPpSl0DRGBFNEMgVPOjobwnWN596N5K3ZUBsnR4GhXl6r3cCjkj2rl3jrFX60HiCeGosUdGnlk9NLlX4pm8+Mvh1Nj4uzRRH3yd/oPO2Rj9wwC39PqF6RflCraCcmIwMkqZ0/p1/T/KmfY6EtARsVTzMwEGnI826HaJrVPifdPZt7Pr/wJNB4W++1Uj9+nztS6nmUbKebLyt+y4akhdLz4Z+83mvWaY/5+caH0Au27bHiY0FIIQKCZgHsa/UW5F20up+z1EkOk7BjhEz9rtIdgdyiNX3RzFHtMdeiGWyUJEyk6n5qH4I9+8nH/KtHN15bRT6Z+KN966lX6tGjit5LhE/r6xF/qZX0VrLWTdD2Q49WWqII1g2ODvU1M/VpbRpjy4GBM3ZIQvn+NeCua0OdIQsohYPvG5/03yql/mGu1lWP2Y6J17e3a7s+jl0vnKIlQie8FJbWYaiFQUQIeajlNOlW6g3SLlKpklZESLyipHQ0364L7h3yJ2b9Uh7zHVROf28DOqJpV3dsTxpdXzJHTfSvS5whjv30M7I/T5xn8lHI0rJE4GnypC+dyg9/qLFsYHk7V89jqm+xZ1jOMZdm54ur/s8sPY+uLb7O9QaxfkP5JytDm4g9AXKP58Am/pmxVmglDWXGYr+ROcf+6dKFYD9PyZakiq3xentKkxPeC0tpMxRCoLIEksuHsEi3UrLXR1j3q3cp3Szm2p3Y03KCL7uX52mhehx54qiamMX02pWpWdW9PeJgs6Tzr3tpscoRvZPv/F9JEQJEM/0icDfr6xCFNu1hNRcB+rWuC9wQvkCo5iVISCLO/y/flfJFiCNjnxdsjSb5QTH3UMiuBMNHsrToGmivAVpx1P1vyIxA+benPeB6l5k5OH9LygfzqS10yjobUqEgIgWIInK5qdLP0ibpKER9W4PX3qj4HQgky+pmlDr0Z5iGWV+ZrYHgRlrOhamK/UdsH4GXVjlU7NAmVf85pWMS+pPb6Q/Syw9LiNO2Uk+GLiaNBX6K4K00e0jQTsLfE55XOLyRbAuGTc/4/W9a9PNvmVL40j2QI916PbFCEA1IOAVtJ/J+RusOhapNil4OksFr9azdhuIScPDapsGrbV4SjoT0f9kKgcAILqsarpccVXvNIhYto8dYe9Tnle3akmKL/pnY0yMngzoY8JbzQV/ClxzRZXvB0N/Jsff5l2y5qhz/Ar5V/XVWpwdaN2/zpqlhUBTt0wVlMn7d8OXE2KKpBD7lIdwRM81uEz/4OkeOuO0K9pQ7fu39EbG+S6gUAyZ+Az9EQInSu1XJC/vVRQ2sCPnQiDKHwIUQ1f95o3UK2piaAoyE1KhJCAAKdCPxPCVxLEJ94K7x8dnig82ETPrQhT/F5EOzxPGvorWw7MGb0mt7yVyWXrR23Y+eqWJS/HaG3Tl+csF/kX1e9atDwiTNjR8PD+qejB63rw+fhteHauXvXWcnQgYDpGhXYfqZDQnZnRsB2jZl/NbMiKagHAmG+AO8Q0HMHMsQEcDQM8cGn6RDImkAdHA1/0I3v5qwbPnN5IXLAJ/WsmNgO8QOYXtTrLD5kIgyd+G6dW9G97e5kMHc2dBge1H3Jdc5xWRRNmB5N2EixugvWuR3l2e49jiHMWVFY9LxnexyCg/B6cZUPzObPtmxKG5+Af1nKNLm2rTl+GvbkSyCc+34cXpZumm9dlF5hAjgaejw4POj1CI5sEMiJgDznQTqF6fkcEhPjtHkt3OEyWTfXqoyRS9r5QLzy+mRDPZcNd+JohuVojXra37PVf1TOBaQ1dxT13P6WGd8eRS9NjF6aMm8U/bdlAjZ2INDwa+dR0mWkH+6QmN1dEWi8ouR7SxeSHtRVVhL3Q+CTyvys9FTdh3UvRoonEM79j6ree6T6ShTzCxV/DKixzgRwNNT56A2G7X9XM1yR7ggU4WhQ52oQvftUSh6MrVm8Ulb1Zoyf+6v3lrW2uRSNE4TJ5Wp7CCtruIZ7Re6IPKCyFtbWsMYUmX6OdA+9bGmSNiR/Ao1/qw7xjlaRHpp/fdTQmkDjKW3fSuqdLufp/J+zdTq2QgACYwngaBhLhN9FE/CXrGF70UrDuAoRDTga0hyp/tK4o2EBPbgs0V8xdcrd8AlGvXfoXXWyGlvrQKDhDtijpbqn2PvqYHHNbNxP9iqEPDqmZnbX2NzGz2S8z2mzj85pfV0FKYdA4ybVu7N0NemJ5dhArRCoHwEcDfU7ZoNmsYeND1voeLtj6OG/aUTjNoN3PU3aXtNU1NHQ0Ge/ov9IByGi4cb44Lyx14NU03we1fBmPTjPVVP7Mbu6BH4q0/z68KXqmlhXyxr3ynJ35MiJgyOnwKPoE5w+Jj1F3OcusF6qmolA42z9PEq6rY4DUVMzseEHBCAAAQikJeBzE7iWIKabWJjd20P02oj5A8cTbRJksMu2jm3ZKIPCMi7Crpdtf8640BKKs0Vjxl8tofISqxw9t95fohFUPbAETE6GcB3deGCbWFrD3DloGp5id0g73KdKM3IAK7bN4nP6hAFsXI2aFCaHvEjHQpE99t4aGY6p/RFgMsge+RHR0CM4sqUm4BMWLiZdXrqMdEHpbFKkNYG0EQ3Tld3Z5ikVjWgITfZx2AMQ0dB4RO14VDpsEQ2XqM16UIveLUUgkDWB76tAv34R1ZA12ajxnIr0IRQrSPfKvHgKHIdA47fa8SOpwvfN5wtASiEQJofcTlXfLdWwFvP/AwQCEIAABAok4OHQn5ZeK/UQf395blZ/UJki/ZC0ilJmRMORQiVWnWaYLuLTk7ZubMtHq3eQ7HuyTc6WQfiMnXnviD+0DJnYlWq3ekURCORBwA7X+eXX0nXyKJ0y7QqxlTPHo7KQYgj4JIQhkuRxLV9bTJ3U0pqArRyf/7dpOW/rNGwdIAJENAzQwaQp9Sbgvcz3Sd2x4GPpb5B67+U50l9KL5f+Q+o98p7Gt1XtQaVMR8MRwpLG0XCs0nmPcI7iM4sHW3bLsZIeizb1qAXbXtdjARXKZskxn69CRhVgih0UH8MlC6isllXconlYXGtpfOlG28I6v56Xnle6KQNpgL1JbHUP8mF8SHEEbG0xdye7O6gbxdVLTbMSsC11DF6R/ppjMSudAduCo2HADijNqS8BPRdHeriLdpW28/LqITB8gsxfli+WVkmq4GiYvT0Q04Rc4UU7x2Eo3mMS6tBLfdXEPhLbtkHVLOveHtsubstG3eetcw7TsQvnl18rkDEEpkUT150eTXpK+oTW9VKHdE/AvqtzzF8EVuk+Lzk6EwiRdc5XE7sixREYnYNkz+LqpKbWBOyr8X3s0Nb72TogBHA0DMiBpBn1JrC8zPcohW5eHL4e56lSVEOZjoZvxDetTo6GlOn6OaHCpF8eXXFYP6Xkk9c/8xVeUj+cT/lFlmorxW35XJG1ll+XD3uxJ6W/KN+W6lnwYjTpG9Ojyeq6dJ10dvUsrINF9nqdXy9Kz6iDtfWz0V4ttvrCR5iclzm/CjuA4dqpyZBNUaMewo+UR8CjSkyRueF5ZJvy7KDmnAngaOgRMDeGHsGRrSWB1eOtl7Xc23rj5fHmAQiBb93AHrd2Con0oScuE0cWefwNk355xEm7yJQ8Kk5T5gNxosXTJK54mjtln0cBDVmvdcPPrUuk79BDmpwOSDMBixpXNv3e4qkoWqDpN6upCDQeVLJTpf45umVTZSFRFwQaTyjxl6R+7fJ5mZBCCIRr5/aqyp8DTte5zfCqQri3qqThnWs7SG+SnqxjMWwTO6vZCATGJ4CjYXw27OmewMNxlvW7yLqx0ir0MvKXLUR3qZQQCnA0BEs8uqOKjoaHZJefNwPgaAizWPu8JcP4gHKx2j2/dB0p0kTgr9GLGoMdvkiiRWPynNFEDbFBeiCgCXaD7N9DXrJ0JvBTJblequhEW7BzclJkQ6Bxj8rxoRPu5Dk0mzIppTcCjWeVT/M1hA6DX+n/YJHeyiEXBCAAAQi0I+C9krdJn5F674a/QIwnC2mHj/33Xs3fjZeopO1lDp1IZkrX7NLtxA7QzUxOCXtNu1T977P7VEdFJ1Mz9Vbauf23sQolhLHOHuKdY4RKFdo51gZbUm328/igsXv47Z/smXh0MnxiWjTJQzeRngjYmTrH/P/rdT1lJ1MHAmG+FTl+7ccdErJ7VgK7adP7Z92cdotpou0wKefG4+RwR4SeK6IVx9nP5swI2Nt0LNQJFL7IQpRJZlwrURBDJypxGDACAlG0vCBMkerlIZoq9Z7ay6T+1Ql/KbxU+nep98h7mlul7nSokpTpaDhMWMTFP2PVTuwLcbqce/RNx88uaWdJefvsr7Lt2vLqz7Jmk2MuHPchjGqw29X2v2RJc1DK0jwNqyWOBl/qN+Oxezq4tqrOMX8R/m5P2cmUgoCdJL7qOOBzoilgJUn809H+HKT7fq8S5slwp/v90gValLKDtnkd72uxj02ZE7Ddhduf4U7MvGgKLJMAjoYy6VM3BMYQ8PkFdLGNrpaqFync5PxG56oHkegR6WXSraVVHL5TpqPBw0/9JjWX2LQRU8hkSLdMm0QZ7LI/q56/ZVBQDkXYz2XbozkUXEKR/nAejudOJVRecpX2HbX9JWmrh+SSbSu/ekUy/C1xNsjR8M3yLaqrBWHCNs2FYovWtQXVtjt8TtQnd5Xz16p4X68SvgkyRlGJoTPGn4v6cDR4s2wTqTt5zvFfYwRHwxgg+f+0H+hY+HPcPvnXRQ0FEcDRUBBoqoFAtwQmKoOPV1tOqhnAI7/BVl3q4GjYLb6R5RwOab9VPRWdP8P00hVu5rNX/YTqbJ+pDeFl+9jOaQcthW0aH8cPD1rLsmiPhk98JnE0aPmIPLR1uIZm0fSMy7A3xefZtzIumOJGCYz25npHA9KawFzafJ1UETaRnKypIhq882ZB6XzSsfJqbdAwVTsiPr93GZOglaNB6aMkatLLfbPUyxlPPI3+f8btGHLHkj/jbShtVU5zff48uLa0+Trmwwx8W7t7+eLa38lOJamCmNpmf5C682ezKliEDX0TuEoluCIQgAAE+iZQpqNBkzqFl+e527fCdo7TrdY+Xb977Weqp6JRA6NRHcv328pq5LebxPqKathSpBU2h9r9gpQTX9RZAABAAElEQVRQ0xbYn9LXJvR5y6mJs2FqNHmLFsnYlIqA/Ubn2bPSqg3XS2V99RN5JINHwJm+RuERDkgLAh5RM0W6sdRftj2i4evSTuJzWfmQU3/hT2R7rXj+fbVQp06IJvHze4UkgZatHA0arhZ9W3qG1PMn6tuaZVn98Ik+3SniaR6RHiNtlo304zZpUoYvz5K6cyKRpD4fPpuk8+csfaY62kb6XLxdl7tglxajsqrWbpQm+Xx5urSV00WbqyIeoWd3SJ+RrlIVq7CjZwI4GnpGR0YIQGAsAb8BupYgltbRoAeM4JBYM18j7UeqRy+BVRTbMmbwjipa171Ndqrao/POv8s9bGIXqd3/HrZWp22vhk+clTga5HQ4L20+0o0lYOvG14zDx+7hd1YEAmPvyT0pqxIHuJxuHA2LisPj0huknm9pqV5iI107o/ieYXK6B0eaO3vkeAgynqPBh7VeKH23dCXp+VJ/iU/up+4o+pfUoy/eKNWLc7SP1NNsLXV5nfRJ6dXSNaTzSLeVusNAPfqjcrvWpkn92rW69L3Sh6T/ld4t3VG6rPQUqZf/VqnL/NLHpLdIvdx5pZtL/yM9W1pxcYdPcLqpjTg3K36wOpmHo6ETIfZDAAKpCZTpaPiabki60ZrfsNuIbRenW69Nogx22VFxPZMyKCzjIkZDoXfKuOCSirO9Y9Z6OBk2GW27P6wiYwhMjSa8u8nRME0XKA8/RnoiYBfr/8x7Gf3FCcmFgH99Iky+uWEuxQ9OoYmj4bCUTfqA0vmL+JekV0oflo65FoxGOx6pfS7jORrcaaFoslHxF30v+//iLbofhTm13AnRLCfox+HxhuO0nCpdPP6dLPbQipeVOC3c0eAOiWS4hlajn0g9zX7+I5aVtfRtu8W/D4l/vzv+nSy+HG93p0XFxd6pJk2XKorFKvgcVXF81TEPR0OPx+JVPeYjGwQgUC4B7x1wyfvG5T0mLt6TUDW5PzZoiaoZ1qM9N8T5mkNjeyyqdtkuii3etHaWF2DwEdFLf9TT9wMjVTUmzh5N3r6Aage1ikPVMHfk+osUkg8Bf1n13mq9iNps+VQxlKWer1YfL/26dH2pXwcekzZJ40T9OEe6r9jrJXdcuVV7mqMV4+tLNHec441a+jZ3EjTLLvpxYLxhNS1vlCZ5483Rr+OV5nuZR0c8nyTQ8s54/YKmbe78cJlrZBHmbfA8y0nd+ZBo0hHjNlZcGn+UgXtJ3yr9YcWNxTwIZE4AR0PmSCkQAn0RcG++SxwKOfKjxV95yIMk4ZEtkmSy6em4lPkyKS3TQhr+IOsPIWN7UzKtpcDC/IHNj3/zw1mB1ZdZVcMfZu+T4mhocRgO1hhpi+yUZJfWP5Gss+yWQOPPynGZVA//Nn+3uUmfhkDjCaU6QOo9znumyUGa1ATOilM64+vGyfUpbXdH/GlRtGTyUj426ZNjNrwU/07eC1bQ7+TFf0zS0Z/u5H909NeMlcT50TwXikdftJLmOpLnnySd39vdns9LfdhGoj50wx0XiVNEq1WWxnGyznVnXXO8DQgEhoZAckEZmgbTUAgMCIGiHA1VjmjwQ3m/dEAiGhru1LlHuqZ0GOUiNXpDPYiN92A8jExG2/xKNE09lT6LuXshG68e3cFKLwQOViZ3nu7dS2bypCLgPet/lSr83V6bKgeJOhHwF2sNS4luls4u/ZG0hTSe0saPSTXPwp67tkjgmzp1ZvxbaRZpkdcdEGvH+T2SodWxXSzO53YmMtaJkGxvt3xIO6dKV5Mu30L95b0uIsdm5NENR+n/YbO6GI2dEOiXAI6GfgmSHwLZEkhuxp0eAooaOpFENFRx6IST9wedAXE0hBPpev0dwoiG0HZ3NHiEzibhF39mIqDB1Pe8EkKl7RwNnfaXCKRnAo0pynqZVI4Gc4cDkjmBht/LPiOdS3p05sUPZ4HfVbO9l/+j0v2lH5ZuL20hjSu1UU6ehddosTPNphuUyOtadUziM/T721I/vn+T+v3KhzY0yzbxDy+jH/HyPepoizGF+BCK26VjbRuTrEo/Gx4x4sfLIzH8a16rV8k6bIEABCAAgeIIaK610r46cbBuQLqBd3r4tbfG6TyEMEcxvfQFe8be6HOss5ui7XjZ93w3Oaqd1g6MefsD3pCJRzKYHGjGONYhO/LlNNc2jv/XDi6n/mGp1Y6NOb9jWFrcRTsnKK2/sB+WIs9WcdqvxGm9M+Jy6dPSpaQtxD83+p3btEN1fPBzTQn8Jf1XTb99VWmDLd+It3tk2SPSm6R63ghfpvAJJt3ejaQui0g9esIjF94m9fuWOwGek7pDIukwaVXfF7Xfy1LUxagspDXflgwvcCeDD814SOqOFY+m2En6rPRsaQ0lfBnkv2rm/dJW0SA1bNNQmHyVWumKQAACEOibQJmOhoN089GNttPYYVsvTrdd361tW4CtFdezfdtkpe00PXQFXs0PK6VZ03/F9p64PVv2X1YdS7BL1f776mg5NteRgF2i800vanyBIr+jZ/OKr8Lw7Q7p5PzqqWXJaR0N/kL6H+n1Us+TyLJa8Zf6KVJ3FLSQFffSRj1TnOvHQMciSKsXf8/vL/mJo8ETrij9i9S3u7pT/8vSZvGogqulSRp3Tsi5FM0mTaRVfWkcDZ5/eenlUgV0hTrc6fATqTs5aiomR429KFXEhs1Z00YMm9k4GobtiNNeCORIoA6OhjV1g9KN3XJ2AJhCIkM9n82Rdx9F2w6xfWv3UUiFspocJoH31ypkVIGmmB4+Q/v94RWBQM4EzOcE8evo13OuaMiLt21izocMOYiSmm/vE3+9qNtZPRown/KtJG3nKPI07vjIS3wIjp5HBkX82S1ce87Tchwn0aC0dSDagaNhIA4jjYBANQiU6Wj4anzzWaA9CtOLWLhJ7dI+Xb97R198FdJfRbG3xRxyHkJSZNtDSOUFRdZYnbpMD7PhvD6gOjZhyWATsIt1zj0jXXCw21l26+y3Yuy9uP7CihROwL4p9u5U27XwqqlwHAJ2cHxMmMNkHEIV2oyjoUIHA1MgUHcCdXA0KKQxPDTsli9smxTX42MzKyi2dGzf5ytoXI8m2flq08M9Zh6AbHaX2n/FADSEJtSCgK0fX0OOqIW5tTXSlhJnhfnb5bVtQq0Nt4liryEOPqeRd1Qg1SBgp+t4uANo92rYgxXjEMDRMA6YTpsJ1+lEiP0QKJaAbjhBGh2qnR7v18NDntLQ5HyReqHCp+DyrKjXsh9URoWEDtyXJxbVg8dre4VS83wezbGB2v/qmrcD82tBoOEPkBdKNTzMajzuu+qwG/fKwoOlG4vzTloihRJo+DPDtlK/p2siReYGKBT/+JV5VKo71r+nY7Lp+MnYA4F6EsDRUM/jhtUQ8IcFF0Uc5C6aLK2qjobw8OS9/0vmTqG4Cq6Lq9I8HEMpCrEOE4nx0DWUh7+URh+kWn0M+H6l1D48lX5bTf2H1MP4FxqeZlelpcHZ4y+2b5B+vypWDbcdDe/I2Up6t9QdQKsPNw9aP2gEcDQM2hGlPXUnULGIhoCzwo6GYN99+jtIjgafWdxlrZHF0P39k1r8rPT9Q9dyGlwSgcbfVPGvpZ/Rg76iiZB8CDReUrmfli4gZVx6PpA7lNo4VwmOkyqqxD7WITG7CyHQ+K+q2UzqUSeK6BvaaMZCaFMJBCAAgbIJlDlHgz4dFcbrLdgegn+OLaTT5JF5i12ruq7Mu5bey7efyb7He89fxZzhc3Caq2FYxXwm7ieksw0rgTTt1gfsF5gWTTpdetYzUUQPcRpo46axN+p80zAs+864SdiREQH7gTjLqW6bZFQgxXRFwGYX+xuletax5bvKSuIcCdhbdTwU4RA+ezlXjhVRdPcEfIidKwIBCECgbwJlOhq+pJuMP4B1eGmwueN0h/bd2o4F2B9V100dk5WWwI6IWcxZmgmZV2zeq/FA5sXWpkBTeG/4P9CDFzIegenRpP+bHk0212nRZL28If0RMPX22gvS1/VXDrnbEzB9CjE4U+/UcnL7tOzNh4CtKPbuaLiBY5AP4d5KtY/rePgz4PlSos57g5hHLhwNPVKd0GM+skEAAvkSaHQovsg5GtRZGq3QwZ4yd/vQCRcfPnFbWKv/n+vUBIVS+uR0jcfq35yuW3ChcuhhKwyfuKLr3EOSwSJ7sBGNXCoakX38Ec0xoLh/zeyP9EjgYOXz8dJy+PowCiQfAg0Nx7O9VPbZUkXxRV/Jp57alHqgLL1fqi8QtJXZtfcjUr8fy0kTXSL1fGNlDW14p1T3jzD2/zwtx9xHGndE0SM6Bn89IYrO84jFr0kvlqpHfRZZR1vc6bug1IcZ6SU4XJ+1aCnzaesWLfeMbPTnl5/H+xfXcqN4vXnhac6JNxyl5RSpHPCDLg2dA7acWnmQ9NtSHSMEAhCAAAQGiUCZEQ164Aje7IXbAzW9XYR0fiPKWexE1aUHw6qKvS9msWlVLezeLtNDWji+7+0+76DkCEN2bh2U1uTRjn9H0ZyKanhyRlTDxF3zqGe4ygxDsfSSY0sNV7vLaK39Rpyd9apl1F6ROjeQHT70T8Mh24qH0v9L6g7Ya6XeAeBOxbH3CI9y1BCgsM/T+fqT0ndLm2VN/XhW1Uo3VJmv8vkBLpPO05TIvZhHSL1Or/vueP0sLdvJKtrpecZTn5MgkS9opVW65meOjZRGl7uOjJIyB2BppwiLuNg+A9CYQWjCVWqEKwIBCECgbwJlOhr+L765eE9EB1HEdGTf75Aog90mZ0YYu/yqDArLoQjTQ024Ie+WQ+ElFemh26FN3ts3pGJfjRksO6QAUjVbQyaOneFomOSRMEhfBEy9xfaS9KS+iiFzCgKm3mzTC7P9VVrR+0uKZvSexF/k/yH1CJpO4vcC7+VPnDJzav0yqTsbkuEnHnXgL+1+7s4rdVlS6k6CR6WzS10mSDVHQ/RHfXFU6Xz4xFXPK3rOy/qmNJHNteLlqQMkiB+jvaW+zR0V44mX75EKY3V3bfO8n5ImcppW7pGOTat74Ezye/36wUxbBvqHTRQqtTk8e314oJtaj8bhaKjHccJKCNSCQF0cDXoosJ/kT9QOVj16OLD586+rlxpsrti+b/SSu7p5whjmX1XXvrwtC5Pz+Xn3+bxrqnP5L0aTVk4cDSNzNUxcp87tqYbtIYrLnQ0ax47kS8A+G1+/98y3nkqW/n5Z9bJ0sQ7Wzab9F0mPG5PuE/qta+To0AN3EvjvN0ibxXvFffu68caPxb/fNfLbQ/U9avGDHvng0QaJQ8KHVFwudYdIIm7Lj6UfSjakXC6gdB6V4I6FZrlJP85u3jDO+rbarvlTwnCQcZIM2mabR8dFDiGbKvWoDqQ8AjgaymNPzRAYOAJlOhoO0A1FDwT2ms5U7SmlO6lzun5T+ItesGnJfkvKL7/9RzaemV/5ZZRsv1KbHi6j5urUafeLwaXVsaealiiq4U+Js0FfoDihmlbWySrTtS7M/v7zOlldT1s9kiFENOi+6xEOQyUXq7VT+mjxecr7inSRuAy9mEatnGNHaLueK6KV43TusJgu9QiFWGxrTRHhaVw9amK+eP3TWrp42n46G05XfndiLCxNZLJW3I79pe7A8GEkcnrM5NjQzyBz6+/z0i+N/ByWvyYnVLgPPqHlWAfSsECoQjtxNFThKGADBAaEQF0cDY/pxnNG/sxtZ9Wjhw9bPf+6eq0hfA7qL73mrmY+0wNV4D5sD99Nh8P0QByGCPXzgNtU3mCuKqrhI4mjQXM2PKduSXj1fah9WFoIW16j76IooAOBMPxtmnhf0CHhoO1WZ0GYDLObdimkPvq41O/9irqJ9pO2kzm00x3W/5ImjoVfa/0R6Rg57lxt0L3+G9/R0p0S7nTYVOqRk26r/75N+jZpN/JOJfa8+4zJtFa8fYqWep4JaTydR1IsIx0rf9QGOeCHTcL/hy7rdp/0tcPW+oq0F0dDRQ4EZkBgEAiU6WiQZz+8XGry+E5iDynt2Z1S9b/fezqCTW/tv6y8SgifpROPQRJ7V8xd/IdVTA+54dz7yLASSNPuW6JokhwNjyXOBkU4fC5NPtK0I+AP9KYeVPOXMiR3Ava1+H9929yrqkYFr5MZ/lK9fZfmrKT0z8Z5H9Rywzb5J2ifO29eljaH3l+u37psjJV519cW2XSKnD7b7TGyHr5ucbPWd5ceKPU6p0tXk6aVXyqhRyP48Ilm+aR+OIPfS98i9TKPlj4n/bt0orRZjtePu5o3DM+6bSxUL0pvkM4zPO2uTEtxNFTmUGAIBOpPoC6Ohnt0wzk/f9z2DtWjhwHz8aQVFTtG9imE1CZV1MAezDI9lAXuR/SQeUCymEJrw7feB2xYTPaHR1ENR85wNExq8RKRfZ2DX6IdFf8Pvnnw21p2C8P/unrL7VHpq8u2poD636M6/CX7bU11eS/+FmN0kab9vvqq+PeKWvp8Bz50Yrt4W/PCX0YvlrqTYafmHVp358OdY7b5T3dayKaTntGXJT0Kwu17Qqrr8Ki4o8O3nzO6pf2Kd5q4Y8KdBGNlXm1YWzr2vv0jbfM6tpQ2y0H64e2Zs3nj8KybjnN4ztFxNXciIcURwNFQHGtqgsDAEyjT0aAwyPByqd60TmJ3KO2FnVL1v9/0IBBs+nj/ZeVVgn0utnG5vGoop1zTw6BdUk7dVanVfikGHjbKg1WbQzJVocYaNvFK4myYHk1o7sFsk5Nd4xPwF94wF86fxk/DnuwImHq1w4vUqdmVWdmSdpNl/jLt8yEk8nmt+LZmdYfEeDK/dnikwK/GJHDnhHq+w+SJW4/Z5z9/Iv1Pi+2ba5vq/uRXouh6f6F3O37YIt0/tO2fLba32nSANno5b2q1c5xtfu3yPAeO2Z9EWQzxXAW2r9CITRHzc42hP9w/cTT0ePwTz2iP2ckGAQiUSMB7CcaGFuZhztNxofPlUXhGZWrsYpAlMyqvKsVcK0Pc0dOoikEl2PFr1ekP1HoJQcYjMHv4xn3jD8l+i17lLzJIXwQa3pv7LalefHwYD5IvgcafVf5x0u2HgLecp0GWipe+8KGQ/pLdrNfotxxekf8/ryltFp834XbpGk0bF9f6FdLXSTeRylE7i/gwwwWk847Zs9TI7+PPUNDAMSPr72r1Uj9N+5LngpFk4//1DorrpO74GCsbakOrDgy3zcXb1yxLxT8Sds37hmS98U019PvSHfU/csiQNJpmQgACEBgoAmVGNCTe6sU6E7XrdaMpoKfN1DsSPOhjexc6m1hYitFPIe5SWJWFVGR7xexbPewVYkH5ldjCYqDeNR8eg7QjoOETW82IaJj04jNRtFC79OxLQyB8PtfD+f16O8wOvzSwMkgTPul3n1i7DvJYdHeceq/9F1NAm09pXpKOvd/7fcHL+JXUxYcU3CV9QNouum817fd8O0mbZYp+xMOu/GsgazyhL2W+omkZ3GGRyFJa8bzfSTa0WfqQCLfbnUetxMvwst41ZudZ+q16Z5kH4jfa5k4O2TbMEr7Ucp7QiZ3tOswkCmz7VarLFYEABCDQN4EyHQ166Ag3D++N6CCmi55/Fixv8XkPgk1H5V1T7+WbHsSCjYf2XkYVc9oGcbs+UUXrirPJrhQHf4BG2hC4LIomyNHw4AxnwwTvMUT6JmCfjf8PP9p3URSQgsDoJLCtwvZT5K9Fktll5bPS41Na+32l85fyI6QrS3eQ/l2qUVPR6lKXw6WexudP8I6Bsbq8tiVykVb+K91cupTU7+9y6EZvlsayxtZa0Qv/Zi9E0fvcGbCZ9GqpD7tYUZrIllq5XPr2ZEO8XFVLt2ePMduTnytpxRm4c2Mb6RLSL0inSVtx+ae2XyJFIptDaP2+KEeObQGQ3AnoeRtHQ+6UqQACQ0KgLo4G9T7YdcUcE9ODhv24mLp6rSWM4z+t19zVzGd6GDU9dPlnHodZRsel+oMr0obA1GjyZvrqxD+nRZNOP3vk2/RtUrMrHQGbqP/Bf0nl7BqkCWfTtb6cVHaiWOsl15p708sxJb9a1SsdXZuyeI8OOFrqL+H+8u56s/Qt0kTu10qyr9XSHQKJvForv5WKcchzk5a7ScfIRz+vURhJGl/eKF1/TKLd9dvr23bM9g/H2zces735p++7NU7nZfjz12HSsVEL82ibXqqjvaVIIBDmkBG78HUcnMr5nhU4GvLlS+kQGCoCZToa9tFNQzdbe31n4j5JoP2jc7osUtjDquvnWZSUXxnhs09X5Fd+WSWbHkSLciiV1cZO9doKYuD/F1/ulJL9EMiHQJjx3c/BPfMpn1JnJmDzi/VD0rulc828b2B++cuhv1yv00WL5HwOE0gu0kWedknn1c6l2iWQiYroeUB23vmj9un62ruocus6P4uDISn0i1p5XKrzAplBwBbX8dFQGdMwF8MRPwNM1ms4GrImSnkQGGICdXE0/E43ltuLOU52m+q6qJi6eq3F1DvkN9xBE1PIrE2XKlRymCWcg2l7/4YZFG3PhYDPz+AOP3tM6r2rSO4E/JPKwcH4vdyrKq+Cs1T1ueVVn7ZmU7RgOBYfSZsjw3QezSGnU6uIiwxrqW1RpqE0wdHwoJZL1LYZ1TYcR0O1jw/WQaBWBMp0NGh8YriZy0vdSUwTQHlvTxES5oPwC22Fxb4tHi9LFeY8SGLbx+fEkIdG2hHioNBde90gHV3aUicC9s74f/GQOlldb1vt1Pj/fqN6t2Nc6/3F0Ocp8N78CkuYE0DDJuw56WoFG7qL6rtJOlvB9daoOtPcGmEIhTqfbKEaGV4XU3E09Hikxo6B6rEYskEAAiUQUC93IZ+39KY9JZ3PVyos98o2v6b5g9sgyTVxY9YdpEb10JbzlUe9yhETX/UAjyxZEGj8UaX8QbqPHuZfk0WJlNGRwN5K8aj0BDGfs2Pq+iW4XybvK12v2qY3XpB9W0tflHr04PwF2ru86tpDqo4EpDWBxl+0fRvpstLf6vgM6nCj1s1nKwQgAIEaESgzokETL4WIhhQvy3am0voDWAHi8zP4PA1VFtsyZveOKlvZvW0hZPtJte2s7vMOUo7AwecKuXiQWkVb6kbA1tQ5+Ir0B3WzvL722gfE2+fH+HZ92zAoltt7dRw8cvACqTt+kUoRsB11XPx/RUNdBy26s1TQRDSUip/KITBYBOriaDhZNxK9gBYh/sUJ//JElcVWl41+g/1kla3szTZ/ubZ7ess7SLnsJ+KgHjXzCcwQCJREIDh5/Wswy5VkwBBWaz8Tb3/BVYg4Ui4B+4qOg99rDyrXDmpvTcD2i4/P6VriDGoNqdutOBq6JUZ6CEBgXAJlOhoUJhpu4EuOa93oDvup0vrYzgLEjort8kmZKio2d2zj4RU1sA+z7Gtx27KaabwPW8rMapvFHLYt0wrqHnYCtozOQ3c0KNILKYaAjzv3CD6fANk/+4uURyBEl/1ax8Eje3RNRqpHwI7WsXFn0DHVs62WFuFoqOVhw2gIVJNAXRwNCt31h90ixA6Mb1oVf9ENM8IP4MP/6Av25kUc7erWYZN1Hur/03uUEQiUScCO1XnoL1prl2nFcNVtHxJvf3mS4xspl4DNp+Nwp9SH9RHZU+7BaFF7cAadpmPj/y/7t0jApu4I4GjojhepIQCBNgTKdDTsFd8YlmpjX7wrfGXBbyIFhMbZZ2K7fFKmCov9VXZeW2EDezQt9Ob5sT6sxwIGKJv9QhyellY4uqZauKdHEzaYFk0648Vo0keqZVmdrTE5Xe0Z6WV1bkX9bLdzxNyHUGxQP9sHzWJbRcfBHb83SxVRiFSLgM/RYL+T+rPDAA4pLZQ2joZCcVMZBAabQJmOhs/FN4WlOyO2I+O0Bbxw2UfjutbpbFeZKczHJD5RpgX51W3/VNsuya/8upRsH4vPxU3rYnHZdk6LJt81PZps06NJL8nZsFLZ9gxO/fal+Fx8/+C0qeotsYXF/DHpHdI5qm7t4NtnH4z/B9wBXECnx+ATzbaF/qUWu1L6klTHCumRAI6GHsH5p+AQCECgngSSYRMFOBoi9SAHKfKTVr0clbuUaQHdUKUDJ36jk6PHhv26fYE4TJduNXBHOLcGJc63xmyNqCFnJpIRgW+rnIek7vSdLaMyKaYtgcbj2r2bdAXpEW2TsrMAAo1zVcnhUn+J1RBLpFoEGs/LHneE3io9U9epd1XLPqyBAAQgMHwEyoxo2FM3Ag9z02RjnWR05udXd0rZ/37bMLZrm/7LyrME2yG2cwDHTdsecdtWy5NgPcoOX+HwT10Ou9Ml1eGaFk3ceSSiIUQ1PKtB1VV3GKZqVzUS2c7x/+WnqmHPsFhhPv7c58jYZFhaXN12+nXYfiv1IS2bVdfOYbbMFtWx+Zf0WSnDjro/FYho6J5ZyMFDWo/gyAaBChDwXl2XSSOLXP8+FZde9RcUj2hwWXZkMVB//UbnwkNCFJ0nDnpwgoWfEJ3koWi6erKi/4yka8w1dzSZ8bqdoKXff7KS3iQ9RA/wc6fPRso+CcgpH/1bepK4z9tnWWTvi0BDDp/oY1K//56h47FCX8WROQcCjUdU6Dul/ix3oY7R6jlUQpEQmIUAjoZZkLABAqUSUDRDkDRjHZOhExMLsDiZ96CA6Im+WnN3nHsQHQ1/V9tekK7fF6HByHy+muEPt4w5TXE8NeHL1Fci+3GSVBeZPc6OIkL9EyB9LcNL1r4qwh1f+/VVFJm7INDwF6adpUtIv9NFRpLmQiAcjw+oaH+v+BXOn1wg91lo414V8G6pIk+i3+sYLa8lAgEIQAACBRMoc+jEZ3Xx96ETKV6UrYu0/RIMnxV0uzQWuepiz8nOE6puZW/22RS1zcdaIlFgcR8g0hHQP8VimgxyWjKEQpNCbp0uJ6nSEbCL9L/5vPT16dKTKhsCdpyY+71p82zKo5T+CNiWOhZyAttvpHRm9gczp9z+Sd7wxZx7teR6lY4yQyfScSIVBCCQgkCZjoZkHP5yne20T+km4Q9Yb+icNosU4QX+p1mUlG8ZdpOYXJ5vHWWV7t+PDw9x85dlQXXqtb3i83+d6thUbUv0icszE0eDvkTxp2pbWzfrbFWdjz6z+yl1s7ze9tpcYu5jzxUa7p8BRsonMDp/1OEF2qJ5pKL5Cqyv5lXZxvp/UYSk3SZduOaNKcJ8HA1FUKYOCAwJgbo4GnbUDcIdDWsUc1zsAdV1bjF19VOLnS87ZesgiqkXOhxzD38ccrHFxcJ7zo4YchCpm69JIddLHA2+1O83ps5MwhQEfHhKOCfXTJGYJJkRCJMVKxzczsmsSArqg4B/5tKPRbhXbdtHQWmzfl4JvdIbtKz68M60bSognb1f2DTXl10nZZ6T9sRxNLTnw14IQKALAmU6Gj6jC747D1KMnbOPxmkL6tE1zRFgl3XBsaSkdrTs9BfQ2UsyIMdqbbH4mB+UYyU1Ktp087d/1sjg0k1VVMNVibNB6yeVbtBAGWCv0fn4jPTygWpWLRrjw/rCvVP3RaR8AiHSxJ8ZNGrL3pSjPcHJsHrUeEXjNF7B2dAt6fAc6U46H5Y5R7e5hyg9joYhOtg0FQJ5EyjT0bC7LvbuaFihcyPtQ3FaDxksQOxS1fePAirqswpLGK7cZ0EVzW736ThoPDgiDvvG/wP0zKc8HTQ3w0cSR4PmbJiqb53p5RjJjoAdGJ+TW2dXJiV1JhDmEfJhc09KFe2ElE/AltKxeFx6vzSP60xwMmwiJ8Mz0SQ7PZrgk0LgbOj6wI8+M/1Ox2lS19mHIwOOhuE4zrQSAoUQqIujYQvdFNwp8fZCqET2C9X1YDF19VOLvSvmIj6DKHaW2ucP0+q8GXYJD7L+P3DYsJNI2/6/RdFEORoeTJwNcjwckjYv6dIQ8Egqu0d6l3RymhykyYqADyO0F6WXSLk+ZoW1r3LCXADTdDz+Is3y/2EmJ0NyPcPZ0OvBMn0xJzxP/lJLvkg0K0YcDbMyYQsEINAjgTIdDbvFF/sVO9tum8Zp39M5bRYpwvjjF7IoKd8ybJmYiz+IDKDY5+L2rTKAjeuhSXaNeNzZQ8ahzaJIhv2TB3MtH3Pnw9DCyKXhtk38P7p/LsVTaBsCo1FOX2iTiF2FErBd4/+HUzOqtqWTIbmm4WzolbJ9LT5Op2mp4BCkiQCOhiYYrEIAAv0RKNPR8On4Qr9S5ybYJnHagj7rZd+I66v4OD73xmueu8h+0JlhHVOET1N5L74e3hBxSIZPqDcTSUPgqShaQM6GZ5MHc32TkU+cpQHXVZow5tnna8gjZLwrS4Yrsb8g+RwZNlW66nC1vcqtte/oePh9Sz3nfUlbJ0NyTcPZ0CtjOyY+Tj/RkqigGRhxNMxgwRoEINAngbo4Gt4S3xA+2Gd7U2a3L8b11eClxHu47eKUDatZMlPvs+nd0E6smeE5mWtLx+fl13OqYCCL1ZCJj2kyyFu0hFsuR9jW1Hnpk6wdn0vxFNqGgC0p7k9Lb5Qy5rwNqeJ2hQ4A3ZPD/0SvnSOpnAw4G/o9qvYjHSd3Ch3bb0kDlB9HwwAdTJoCgbIJlOlo+FR8gX9DZwi2bpz2I53TZpHCdo7rWz2L0vItwy6UrRojPahif1L7bh/U1nXfLrtWPO7oPh85IJAnATtB56U7G96UZy2U3YqAbS/u/rJ0VKu9bCuDgM2v46H7VvgyS7fRJvPK4pfn1mSPj2rix8SZ0Gn56ehVOgci1wPLaHE96/RIBjtZ6v8/R9azDZlbjaMhc6QUCIHhJVAXR8Mb4xvBJ4o5VLZlXN/bi6mvn1rcE28vSSf0U0p1844OY1mwujYWadnoRFYMnygSO3V1IGCL6hrkwyfkGESKJ2DniL07ejYuvm5qbE3AP91tT0jvli7UOs24W/fSHntb/JWJTk6GU2d8heI65Vtg3FLZ0YJAiEA5S7jd2XBwiwTDtglHw7AdcdoLgRwJlOloSCZNSvFpRlOacBMoaKy+vTWu74M5ss+oaNMDSWCzXEYFVqwY2yJu32YVM6wkcxg+URJ4qu1IwA6I/1f1OWKkWAL2arF/SHqfVL3pSDUI2Dt1PKZLp0i7HdqSytmAkyGLI+0dNXa+1J0N/c6tkYVBZZaBo6FM+tQNgQEjUBdHg16iww1gj2L42ypxfQU5NvpplekFPLDZtJ9SqpvXFo7bd1h1bSzaMtPHExg+UTR16utEwD/pF3pvvQdX60ixBMJL7Sti/7Ni66W29gTsMzom/gJ7Uvt0Lfe2dTbgZGjJrMeN7giy38XHas8eCxmEbDgaBuEo0gYIVIRAmY6GT8YXdL3UdxJbIk7rEyQVICEM2B8M1ENXdbEVYzafrbqlvdsXJry8rPf8g5bT9CnB8OBagzlEBo097WlPwBQFFs7N/2ufjr35ELCjY/6atwGpDgH7Xnxceuktb+lswMmQx9E1fWnMLpW6w+6redRQgzJxNNTgIGEiBOpCoExHwy7xjTfFREmjL/4Ffas9eLbd0fDN6h/IYKuPzf1O9W3t1cIwWdOzaqPCGxFxWEbq5+fXoQGB6hGwy3Vu6t5ir62ebYNuUbgf3Cj2T0t1nUCqQSDMA3CRjonfq7fqwaaZnA04GXogmDqLzaVjdJfUnQ2fSJ1tcBLiaBicY0lLIFA6gbo4GjS5UXixKtDDHCY2O7H0I5TKgBCu/NtUSWuZyDSEJRz/tWtpfi5G2zVi8s9ciqZQCPRFwNbQuekvVKf2VQyZeyRg+pJT+CywXhhwzvYIMYdsNp+Ox83S56Tr9FBBcDasqgkiX6UvUuhzCUz82APEdFlC55b//xyeLv1ApcLRMFCHk8ZAoFwCZToadtZF3HtlV+uMIHiYC+7BtXtk268621aFFPZ72XpnFSzJx4bRyUD9QQsJBGyf+P9nLYBAoHoE7Ic6P71HcIPq2TYMFlny+ehvDENr69NGW1L/E49IH5Yu0YPdwdmAk6EHcmRJSwBHQ1pSpIMABDoSqIujYaJuyu5oOKpjizJLYOotsD9nVlyuBdkPZOs06YAOLQjfuvbPhJ2dK8ZaFW6Li4e/yBX4P1ErQG2NnRZNXH9aNOka6SmaWVPXFyRbAragzs3/Sv06qg5YpHgCdq7Ye2TJJsXXTY3jE7B1dUyel94knXf8dOPuceddL/nGLZAdEGgigKOhCQarEIBAfwTKdDTspJusOw9STmgXXqoKnIcgRAnc1h/eonLb3jHLZYuqsfh67AK18aHi661yje4Is3ul6uBCuiGg79JflHybXk6Hz3STl7RpCdhu8XVJS6R4AuZDDu+X6rppCxVfPzWOTyBMmuqOYp+3YUA7CMZvPXsqTQBHQ4+HB496j+DIBoGcCMjJECTtS5K+RR11+x3quIqeFuqNi/Rt8lpIMlZ/+VpY25uRVyrbYnooW6q37AOZ6+dqlUJxo/UHsnU5NsqiV26ZUfyr9lGozGwzfrOWEYGfqJwbpIfp/1YRDkixBBpPqr6PS18jPbHYuqmtPYGGok2iA6TvkX6vfVr2lkvA9Bzo0ZSm5w8EAhCAAAS6IVBmRMOOunB7RMMa6QwOs5ifkC5tFqns+7JNzo069BbbCjHLPbNoeTXLsI3iNn6smvaVYVWYsMpDowuM9CmjndnX+UIULTE9mjQ9iWp4MZq0bfa1UKLOTYV5h2i0H0GjLAJ2SHztHOBPIJfFtt967afxsdmn35LInxcBk6MuTOKpDh0fsjjwQkTDwB9iGgiB4giU6Wj4RHyDfWO65tp/lP60dGmzSDX6cDZfFqXlW4ZNFJuXpMfmW0+ZpYfvW/s8FMeVaUX16g7f/H5IXIja6/LgaH6G0xJHg9av6zI7yVMTsJN1frpDbK3UWUiYIYHwaUUfZiX/WprJlzOsmqI6EPBhE/YHqf9/fKBDYnaXRsAW1vH5u/RuaQdng09eXmvB0dDj4eMhrEdwZINARQjoJbPwoRPe9AUr0v42ZjSma+d90uXaJKr5roYekqPrpW+peUOyNt+HT3hI51uzLnjQy1M41TeTNjaixppTownvTH6zzJTA/irNndoeJZZ2qFymBgx3YQ29xEYfk06VnqVjMMdw86hS6xvqIIg+JL1deoaOzdpVsg5bEgKNx7W2ifRS6dMjW8Mk1cvomG0lPVh6vvQe7dO1DqfRCKPh+oujYbiON62tPgE95wdJ++CJo6H9Mf2ndi/fPknt916hFqyqm7gmOUNiAj7W1x9Wt4VIdwQmR9P+oRy/S3LNFs22X7LOMksCjUdV2sHS9aU7SpHCCTTcEa1PXkYrS48pvHoqbEOg4S+um0mflf5G97cl2iRmV2kEGv9V1btK99Yx8meRp6TXSveQziW9Q+rH7iLphVIEAhCAwNAT8F4m1xLEdtDFWs4GWzNd5Xan0hZ48bZNY/u0rIPY92SvIht8GMWgim0eHxMtkRkEwszlj4mNwnCRbghMjya8LRk+4Ut9gSLl9aibWkg7cm6Gz/nJ6WDzQ6QsAnZ8fA3duiwLqHc8Araejs3z0pv5HxmPUdnbbUUdGz0zhy8+LTnDGpMDz/we/Hvp7DO213LtKlntikAAAhDom0CZjobtdUF2R8Na6VoRHlL/mC5tFqk8hDHY9/EsSsu/DNsztlcTQw6qhM+1KQzYvjWoLeytXbZjfOx9BnOkSwKan+HqGc6GSed0mZ3kqQnYxvF5elzqLCTMmICPH7dbpfoihS2dceEU1zeBEIbv97jLpZP7Lo4CciAQJrhVFIrFkUHB+fCIfmtYxUAMS8LRkMNZQ5EQGFYCZToa9ALflaPhOqX3cLWCxB/Cgn17FVRhn9WYXjKDve/vs6CKZ7cb1c5rK25kwebZvGLygvTkgiseiOr0xYkPNDkaXp4aTVavFZIPATtV56m/SDEWPR/AKUo1H37mPefXSIv8ZHQK20iiY/JZqXfC/Eyadmgp4AolYOvq2Liz7mTpQ9IpUh8+EYtPzuzzNHinSPiyyErJnhoscTTU4CBhIgTqQqBMR4Mmpwo305QPnPb/7H0FmCbF8f58cLeL2+HuDkGChuDuBIK7Q9CQAAkJEgiaIL9AkODuEDzo4X8Iboe7HA4XONk9rv5v9c63u7e3MtIz3T3z1vPUfXvf111d9dZIT0119RNo/1R5wMqUsX7HlzdmnpFk3ljf3+aR4n9fOQt2jgXDP6QuBOQmYKJvWSbp+o5/JUSggayGV5vBBvx9ccJ+bJYaAZkRx6hO0BEs5E4pqeGz1kH2AP76MHumNZEUZBEBOSX2Dx5USX4iIFh+ZM6hL/GJYH+TZFb8/3GwLmW9Anwq+Cvwds0Wnn8y0OC5g6geEQgJgZACDUNxoX6+XHCxZDuY7RTNFmZjoO955WJU9miyJWzUCfL6ZY/s93iyVYwL8CGlRQC1GXZqBhrao5Y2vO6dI60Mtk+KgOwfH6v7Je3BdkUgIFfGfsCbV5JfCJgdDa6O/XOQX7pRG/gFRR/lffBzYK3N8KcOVEwm7Hv4v/KiXUjJZvg/AhJBEAMNQbiJShKBMBBwGWjYHhdefWBcLhlUpsjOq8na2moln0K/621JK16OWXv7QPHjuBzB7Getx81JLrXwb2zNZDAZDTf7p5v/Gj0URYPaotb3msEGLKc4wX+tQ9XQpBU/g+P1GzAyHEhuEJApgP/rsR/mdqMDR+0bAV3WYtb961IjFu/sGygHv8gF8Iku550GvAh4CHgy8Ivgl8Azj6+UKRap8xa08Z4YaPDeRVSQCISDQEiBhttxkX67XGhNAcqAHtzNPs4flYuRi9EEASddSkMaHwFByr9oVsu043/P/yVBAFkN+3QLNASyZCqJZT620QCzqdVwqY/a1UcnWRJ+QAKP4OGiyjsWhepRmRp+eRmsNXh+EaoV1dNbC3X2vM+aZRL6cqqXbDg5Ed8PCwQHBhoCcRTVJAIhIOAy0ID1aiajYflkQJk16B8ma2urlQyFjohQh0JyGvQdBw4hap4DVPknbNRlLRW3My1EsiYw0bcme6ftyfYGAdRqGLwflk4ciWjdpMSkaATk3Ph4/WXRI1F+fwjIXrEfTu+vFX9zhYDMDv98DP4azEK1rtzQ77gaXDBzr7UnbCa4vpkXAPtO+JuX3zDQ4KVbqBQRCBOBkAINWoF5eLkwy40YEzf4UKhzwvizUDTOpqdsC7/oA/Va2fpXtZdJSdcJ6SNVtZB2VQkBs13tFzhe9Y3toCpZFp4tchV8oNfUTcPTvQ4ayxLwzXfg98A90vLrYL/vNpq6M69NqKVZVqGFIK/r+E2mx994ESCXgP8InnLCPs6/YaAhowsmytiP3YgAESgGAUxqDCXdvgkp4RHS1UolvEGIhpQ6Yr7B3oy7L5BPjPe9H441XM17TUtVsIFsluhq8CqYwMxV6tAcjAikRqDxLbocDl4cfEjq7uxgE4F9IEzvH5fi2oFCdyS/EGggGBdpnYZZwXfCR6ivQfIIAdSbiVrgl27PmoL7cPQYGHVQot3w2wb41OUTuixPC0MuBn4L3zNwBCBIRIAIVBMBlxkNzbfSKyaD1hTf+TFZW1ut5K+4CSAg0n1/ZFuyi5Ajs8T6IlJedRJMiqUZcKi6sSnsE2SzmGO2BsdACljY1FMETHV9ZODID2AGx5x6yVw7tBbAk+DBTlXh4H0gINvDN7o88m4ws4D6QKn8r03NBq0ddQV4S7AuC8PLMfOpAQj1m253eQO425JPs/ML2npFzGjwyh1UhgiEjYDLQMM2uODqQ3zSQMM/0HZsuXDLobGOAU2AZQR0vqxcnFyMJv+CnaPBk7gY3e8xTdXrYX7rSO2IQBMBU5FdJ+V3NL/hpysETFq33pfPdKUBxx0IAUEWkJk76UNtY6DW/L0sBGQ6+OME8BtgLJWQrTtG1jmuCTroHLaHv+QYfOfbdY+BhrIOGY5DBGqAgMtAAy7C5ma5UjKc5e9x+26pacl6Zm8lO8VjLptdRtk95RnorDeKipN5Q6AT4jUqbmgG8+R38XGLyv4kIhACAvKX+JiNJ+ch6FxVHc1bWb22blVVC8O3S1C408yfWMDTa2eabS8/ga9uAvcMMmAua4ISuPZ5RQw0eOUOKkMEwkYgpEDDifGNtcRq8LqmztzM1wvHzaaol64XrDjJrLFvfLtJe4C7WUKD7B/BGxQSEQgBAZN6rG8CPwNjb3qSOwR0qaC8Av4evIA7PThy3wiYJUea0aABoSP7bsdf3CJgsmI/ho+6LZdoaiR/w/c/gFETxasikQw0NF2U8hORIxIRIAIeIYAbpKEeUd4+NWyLf8F6t9Loq3ik6UsbMf9AmKxH0+LGNUN+UT5LaHwK7d4Er+6zlm50a+BhLbofvB2OA661duMEjpoKgcYYNN8HPDP41FRd2dgyAg2thaTZDDpvvhHXkBKD+5ZNqay4hs6fdgffDT4JPtK/Sf4hsClUuiiKGiPHV01+g/8fBt4NrEUhdakji0QChJBJL5gkIkAE/EEgbaBBJ6JKrR0fpfwbYqBBH76VFur4qPS/Q2HdCpwI9+rjy/HtEPCGvf7KLxMj8FEUTTo6GrQ29pZDAI9UHAKNoZB9MXhPnNNasZ3kDIHG6xh6L/CS4POdqcGB+0GggeKCJiCkb6AvwDmzWT+N+ZMbBHRnnUnGH1rrMkSabXgUWF8E3AYeCp4HAYnDwTvgb31RoO1IRIAIEIGgEfgftFd2QLr+06T9/SLZ4HJY3H72ZO1ttNI9jo2OGmkOhGTpWOc9AlE4h5qybWzrWjmEVLSrvoU0hUGxNpSUB4H2qPU+sLRFre8ijaaXFNg80tl3fARMQbXPcey+Bi4ze218Nfi/GAE5K77G7ktIfEXAnDO648Eo8C991bKeesny8Mn34N+DdwQ/Asac28x/V8QnXqDpEscJ6jcgyOCsSCSXTtTzYKXVRKAQBDTIoOyAzBZAyGpI+uZKDkBbbT9fuco2tygqd9Tso5n1tbr9VQ3Sj3X/aXNMoNIzaUIEBG+HzfGLatikrAggyPCFBhpi/m1WOeyXFIHOQq9HJ+3BdkUhoEuv5PH4OoKHJpKfCAhewMiHYCReiWahkLxBQJaCT24GPw8+B7woeAj4E7CPRSIZaPDm2KEiRCB8BFwGGn6Fi6wGDhJG4Du33VqkXNjNzQB7H4dE8hFwvTUkjbPrKljbKI9l71/lnrJ6fI7tV2Uri7ZtTNRyWrdAw3CcXMgWIRWLgNyDY3c0uA5LwIqFMrd0U3hXs0w+AIdUryi35WEJkIXhHyz3FCReCdLwSf4ikLRIpBMLGGhwAjsHJQLVRCCkQMMuuHlqYALR4TJJXsSYD5U5Yv6xBOv79AG8DiTnwlYUCu2tqnMd7O/PRlOZ/H1g82R/rfhb/wjgIjlje9TyYzPYgCUUh/Tfg7/mR0AflARFCWUouJFfHiXkQ0C3EZax4HvBrHmWD8wCe5tU/R/go7fAMxY4EEXnQkDnlHLchCK0SKSZ5/56wt9K+4aBhoxQ88KYETh2IwIFIYCggaGkk0g8TBoqe92uFoQM7S3Om9AZS0xkUAdklf53KKzTgkoJa31UGosexpnK5FfiS10LumCPH/nfhAigUMsXEsl5zea4YB3+3gQFvpq/8tMOAg1AHB0LXg28O5jkFIHGQxj+j+B1wL08IDlVjoN3ItB4Gn9uCZ4LfDeu+1N1/sQ/fEJggCKRjcCyaH2ClroQASLgEwIuMxo2jyO3OpFMQJJyqUUCkYmayHXQ87NETb1pJAfF2Nbg4VJmim090Rv4vVJEAwzmDclfvVIrMGXwinBmZDWMZFZDmY7TQKlZ14xJudZjIblFwGRI3QJfaA2gTd3qwtH7R0C2i/00FJ+T9N+Wv5aPQH9FIsvXpseIzGjoAQj/SwSIQHYEQgo0bIQbpoDXzm5ulp6meE97lp7u+si6MVY1mQzKK7D3KXd4+z6yKeamdTuY2ZfDVW3R4NObgQZ8Dv+UO1DkQDNpV1kGx62m7HP3lKSQFdpO35DL62CtpI8gJslfBDrT8G+HrzTrj+QVAr0VifRCQQYavHADlSAC1UDAZaBhM9z8NHCwejIoBSmbpj0CDmWSHBuPO02Zo+YbS+aMdT48n5xQepst2PRhBJNg0oQIyJ7x8bDehL/xm6QIIKthpu61GhBs+F3SvmyXBwE5JT5+kdVGco+AKTo4Aj55FTyFe32oQd8IyFHxuXMNPhlo7hso/tKFAAMNXVjwLyJABHIi4DLQgLftJnCwRjIbZNW4/RbJ2ttq1bmt5gK2JBYvx6S4aiG1i4ofy4cRpBm02sQHbfzTwbyF1OMBk01SHgSwA8Wp3bIavhgeRZPnkce+SRCQSXHsvgXGEjYJKOCbxLZQ2wjuw2YJxY2hWlAfvXWrazPXuqA+NtPSHAgw0JADPHYlAkRgfARCCjSsEN8stx3fhKL/J9vE465U9Eh25Zu1zY/blemrNH34kJ/AZ/iqoXu95ArgMwrMB7UczsAFcwZkNfzQFWxoOSKHOHZNjIDZqlVrA9QkeJoYGIcN5a/wh2YlHulQCQ6dCAEtZmt89bdEzdmozggw0FBn79N2ImAZAZeBBrx9Nje+NZPZZNaz6aRml2TtbbUS6Gf0DKzegb69lq9toeC/HPkv7H3Jfz1dadh5HO/rSoOqjIushpO6BRq++jKKpqyKbX7b0fmwhBo0JPcIaCq+3APWIC994t4h/WhgfHUV/KRzqKP7acifiAADDTwGiAARsIZASIGGReOb5F7WrE8kSJaMx909UXNvGskxsd4zeKNSoYrIybBX33jWxN60YJrlNO8DHxbNTAtdj/ZYnD4EWQ0jugUbdNs/UuEICAI68iH4AzCDO4XjnWQAmQ6+eBeMoLbMk6QH27hCwOzi8m/4SYMNB7vSguN6jwADDRldhMgriQgQAY8QwM3OUCOhTmPidq0J29tqhheWhkJ7gH091nvh+LPqHw/CQD2W1qi6odnsa+j5dhl4eUwyEbQjZUUAFUe/RkTrrK7+jVW6/uZfxSHQ0MD43uA5wSgQSXKPQOMb6KB1kyYB34prC2uWuHdKHxo0UDA52hr8ABjLDCWwlyd9mMWviQARIAJEwFsEXGY0bIQbnUbWE25XKbPH7Q8rF03dFsroGdjaRvlZrHfJGSDleqdrNJkM9iIYpenVpN4RkHmBD56RtTgYKQ8CX0TRFMhoeBD88ehoENPG84CZuq9cEh/Hq6fuyg4FIdBZy+i6ggagWGsIaDBIngTrTk2/tiaWgqqCADMaquJJ2kEEPEAgpEDDjLgpamDiD+XjJt9h3EvLHzfPiKZSuz5U/j2PlLD6yiOwF9XpSX0jIEOBkVbvn7jvNvyFCPiMgCn++imO4bfBCDCS/ECgcxvSI/3Qh1r0jYBMi3PnBXAbeIO+2/GXGiLAQENGp3PpREbg2I0IFISApnIr+b50QnX8ChzY0okGdhiIPgDXZekETDUpofNj4qSp1aTeEcDb4GhmMCeXvePDb71HoIHAb7QfeD7wSd6rWx8F9SXAf8B/xTUYGYskfxFofAvdNBPrPfBN8BeXHPrrLGpGBIgAEQgWAZcZDXjQMRkKCdOOBWtATXsHE0t5AmM/HZ6X5W7o/W54emfVWH4RHyNce9onhCZtFvUM5ZY+m/AHIhAEAnIljmPN2lotCHVroaTJNnkTPvkevEgtTA7aSJkDfkKwQTAXlJWDNoXK20KAGQ0ZkWRGQ0bg2I0IFISAxHJDyGj4EroGltFg0B2Gf+fCBGLSGOuqfzwFA38Ar1V1Q7Pb1/gRfa8Gb4zjYpbsctiTCDhH4EBoMBx8CY7lKZxrQwWAgMk22QR/6P39NnwgRZ/kLwKNj6Cb3i8RGIr0xcRy/upKzYiA3wgw0OC3f6gdERgAAVM1vx2NWgdoWMTPIQca9Nq3YBGg+CfTVNUeCr0wcdLtHEl9IPAvfD8IvFsfv/NrIhAAAib9W4vdzgNmgVNvPNZ4A6psB54XfD2uxRN7H0az9wAAQABJREFUoxoV6QWBhmY9arBhJBhLX2TpXhrxKyJABAZAgIGGAQDiz0SgZATSZjSoemPArgINWqk5tMwAzWhQqlMKq27dNRN4cTWc1BsCjWfx7fPgPRmQ6Q0ffhcOAo07oeul4H1xLK8djt5V17SBt+PR4WD1yelVtzZ8+0xwSIMNugXmfTiXeP8M36m0oGQEGGgoGXAORwQKQMBloEHNwc4XQVEdAw33xx7iQ0f/h+oF+FnfBBOn/nHir/4jcAhU/AR8ER6QpvRf3bpo2Pg7LL0cfBD8smddrA7XzsZr0F3vB5oNiIA9a2yE60tq7gIBBhpcoM4xiUDfCISW0aCWBFanofE1dP4CvJgqXw9qvAI7dd02H6D7d7jWadBUWU09J1lE4JkoGvxqFLVYFElR/SLQ0PXl+iCru83w7Xm/WJX+494YUYvLnYMH11VKH50DpkSg8RI6rAPW65cGGxZIKYDNiQARIAJEgAh0IuBy1wnczMwuEht2ajPgH7qDgvx7wGbWG8j6sa4bWBdduEB5ELo3MxsKH82PAUw1ehSFlMF+6OOrFnIxMEKWkAQWQPMVT13b1bJke9TyNfi79mjQSv5qWkXNBFk65p6C6zXJHwQE2+nKx+DPwRoMInmPgKwAXyGAJx+BNfONVB8ENDCoTEqJADMaUgLG5kTAQwTaoJOLGg2aFaAU4gOZvuGfH5OFOr1hvRc2o6ZGhO0uSf0goEUh9bjYtZ82/CkFAphobIbM4+nAU0s0Ed7imjTkFBLYNAcCh6HvB+ALcb2bJoccdrWKQGM4xG0O1mUteFGgW+yS/Eag8RT005dA04Ifgs8YIPLbYdTOAwQYaPDACVSBCHRDIOvSCRcPzF/GeocYaHgVug8CL9wN+6r/iWJWhtatuqH57Gs8if4aiNK0c5IFBCSSOzEpN9e2RtRYGhkOW1sQSxGJEGhoht7u4FnBZybqwkYlIdDAiqJoD/BS4MtxjmgdAJLXCDQeh3obg7U2lWZGzua1ulSOCBABIkAEvEPA5dKJtTsm5KI3soQkT6OP3vxKJpkk1vXkkge2MJyuizXpxNtZEBaQCHkRdj8bkMKOVBUt1IYHY1ndkQKVGxbLJq5vj1pFuS1qfUtrNlTOSK8NEmSSmGNa36KTvEJAToh9c6JXalGZfhCQdeCzUeA3wLP005A/VQOB/wczlElEgAgQgdwIuAw0YCslMxlME2h4BH0cPTzKCIyNNe2hkaYQG5z/Gprm+fSVU2H3OPBM+eRUvbcgNVZQFFKurbqlZdk3OmpdEMGG9q5gw+DflDU2x1EEZDLwm2BkovH89+uY0EwGuQGswc1d/dKN2vSNgKA+lYwGvw5GzQ1ShRFgoKHCzqVpRKBsBFwGGtbEDUsnG5skN1qQEi+a6u2A5B2MfYeDgS0MaQo63WZBUEAiZI34+No5IKUdqSqXAKsxYE2RJVlAAJkM5zYDDfj8/IsomsKCWIpIjIAsj+O5HYylLCS/EJBJ4RfUADDXnNX80o3a9I2AbBj7DMWlGWzoG6fgf2GgIXgX0gAi4A8CLgMNzQfBTZPDoQ/68lby9jZbCtaz69KNEEnuhu7vhqh5dp21+KXg+Jars8uoS09TYVyDfkfWxeKi7fwhimZGVsMPzWADajUcV/SYlN8TATkGx7Qe1/v2/IX/d42A2YniA/jma/ACrrXh+EkRkI3gLw1Ka2YDl1EkhS2sdgw0hOUvaksEvEYgtEDDTbi5fegGUd1WU95zM3beUTuXEdTsrarxmaZPT5QXwer3l+eAE4JRxMqWrxFc+Esz0KBBBwQfuIzHFriJ5AiK4AomzfIjeMFEXdioRARkCfhFlyTqMhfs1EIKAwGT2TAaPnsDrIVXSdVCgIGGjP7kRDMjcOxGBApCAG+aDDVSyEck3cn2lqoisp9N9WX9OzR6BQorzouFpnhOfe9B/+nBP88ppw7dz4OR84DXq4OxZdg4Imo7DePodQPUmLwlaj2u42/+Ww4CjbEYZ0ew3muuwAcCDyR/EGi8DF22Ac8LvgX+QRYayX8EGndBxy3Ac4GHwm/cjQJAkIgAESACRGBCBFxmNKyGGxQmgJKiMrhZS/7dhGaU8Y2cGOs7eRmj2R1Dlo5139OuXN+lCR6czTF2tO+autdPkO1i3i4ic4dkC4G2aPAB3bIaxiLLYRFbsiknKQKyd3wdYKAnKWSltpPfxP65tNRhOVhOBGR9+G0UGMtZZfacwtjdHwSY0eCPL6gJEQgegdACDXjrqjc2FySHYGwNjOhb38DIbM+Jt3tyZmCKW1DXrCV9woKgGoiQf+IY0eOEk0ZL3n4GW1u2RS1vdAs23G5JNMWkQkCAuzm2V0zVjY1LQkDOgn/0/npUSQNyGCsIyLrwmQYb3gbzvmEFU+dCGGhw7gIqQASqg4DLQMOquDHpxEJT8BKSmYyMS9jYcjPZPtYX1cxDJPPA/UCImufTWYMr5gGDa4AHBFKWjI9xvvkdEKvkDZDFsHlXoKEVWyEMWi15b7a0g4DuqCJYxmLevgaYlWYHBX+lyMTwzR1g3N9la3/1pGYTIiDrwGcjwboz1xwT/s5vAkOAgYaMDmONhozAsRsRKAgBBBkMNVLI1xoNaC+DU/Sx1TReax10nYbFbYERkByt04BJbITJEKl/BBov4XfsrhJhiQ3Xs/ePVfJfW6O2W3Gxe7TZY1w00drNv/lZFgINvX7r0rH5wWeUNSrHSYpA4ye03Bb8MvgyXH9WSNqT7Vwj0LgPGujuYboLxcPw3ZyuNeL4RMAFAgw0uECdYxIBuwhooEGpteOj1H+/jEfDm7EgSQtC6lu9GYLUPrvSmPhEo8FYT0pKgMC5aKOVxFNsO5tAas2bSCT7IdjwAfh9QHFVzeFwZH7jNgx8IXgvXAc3caQEh+0TgcYP+Glj8Ldg+ErmxicpCAQa90NNPadmBg+l74ACiQgQASJABCKXSydWwc0I827ZMrkfdP2m6TN98j62Wpp9v1XfI21JLFeO4mywW6vccX0YTe6G7Z+BkQ1D6h8BU8/jK2ClE0cSEagYAqboqRavQ+BYr+kk/xCQn8M3uiXpMDCXvPnnoH40kjVi332Az/n6acif/EWASycy+oYZDRmBYzciUBACeGg3lObhz2VGAx6+zDZpM8V6h/ahafFKWIdfO7oLFutDxTK1szy1wY3R6HIxGAEpWTh1d3YgAl4jYN6ao95ONDX4Mhzjae4/XltWHeUaz8CWbcELgLELjgY/SWEg0HgIemr2oAaIHoHvFgpDb2pJBPIjwEBDfgwpgQjYRCCwQIPZk/0bABDq0gkUaopGgpew6cRAZN0Z67lhIPq6VlOXT6AoW7S/a0U4PhGwj0Djv5B5DBgV86ND7cunxPwING6HjAPAyHyMrmBAKD+i5UloPIqx9NzSoqsabKjjnKM8uDkSESACRMBjBFwunVgZNyAEG+TXyfHp3A990eR9bLaUV6GvFj4KlOQp6K9vi2pIZtcNTQkkJULAVID/HseLThZJRKBiCAhePslQMLLkZKmKGVchc+RE+EfnCadXyKiamCLLwm/IBJWvwfibFAgCOk/iXCmDs5jRkAE0diECBSKQJaNB07qVXBSD1HG1cnmoSydU/5fBi+GmP7H+p2akyyeWg+0O6nsEifQ50Hoq8I5Bak+liUC/CDQ0Y0ePbS1AeC2uCwyo9YuXsx9Rlym6EozMEznEmRYcOAMCjWfRaXVwO/gB+A8vl0hEoLoIMNBQXd/SsjARyBJocFmjQVH+HBzq0gnVX+s0TALWta91ozthsN4H1q+b4RntvQf9dLnNbzL2Zzci4DkCjY+h4B5gXUf+D8+Vral6DZ0n7A5+EPx3PKxuVVMgAjW78QoUXw2sAb174b81AjWEahOBARFgoGFAiNiACHiPQDPQoA/LLkgzGqbHzTLU60mzIGQd10zqulGd7Gzo4sAJb0wzwf8n9MaxIquHpz81JgJJEGjcilZ6nO+G41wLEJK8Q6Chb8S3AGPpoqnXoHUbSMEg0HgDqv4S/CUYmYWyQTCqU1EiQASIABHIhcD/0FvZAckKuOHgbUWayZ3gbbTps54DhTGk/CkefwY34+cdVbcKM/gdn1dSmP3lFtj/NbiOS0cyuEymAVYIzsjNGTqzS0IEcAGeYXTUWscso4QIFd3MbOmKIKxoTZJ5ih6N8rMiILPDPx+B9Rq+cFYp7OcKAeM/BB1MXRQNHJH8ROD/QS1lUkoEQn0DmdJMNicCwSAQ6tIJBTjQ5RMN3TXjU/CSakQNSZdPINgSrVhD2zOY3PgOna4Ab4rJ4VwZBLDLAAggwLDgJFHLO4h8vdkWtR48QHP+XAgCZktXzWYYDL4Gx/qgQoah0JwImKUumpGmgeK74aeZcwpk91IRMP7TZRRvga+H/7YrdXgORgQKRoCBhoIBpngiUAICPiydUDNDLgipyyfqGmjA5NQQl0/EQCT40LXrOrHfP0FbNkmJQCOSZaKoMaV2w98n/BhFs6YUweZWEGi8BjFabBCZdtHxVkRSSAEINF6G0F+B9Ty5Aw+rUxQwCEUWhkBjOERrsEHnISjyKbsXNhQFEwEiQASIgHMEdNmEsgOS5XCTQVaD7JB8cJ2Umz47Je9js6XgTbgZX99+BUpyCmxAxXWZKlADcqotz8N2neSQEiOgW7rKN+DJEndhw0QIIAd8qvao9QuwKLdFLZcn6shGBSEgN+I41+ujo+V5BZlVObE6bzB++g8+NROFFBQCZlnek7EPmcnll++4dMIvf1AbIhA0Ai4DDT/HTSZtoGHRuM9eblDX9btGZ33zFShpuqKxoaYFteQvsf1zBupAB2rLRjFm+zkYvPJDtkWD924GGtqjlnH4PwKaJDcIyNQ41t8FfwFmdokbJyQcVX4fX5f0zXgjYSc28wYBzUaRB2IfHuONWlSEgQYeA0SACFhDwGWgYdn4BrNjcmtkvrjPAcn72Gypb3TNQ/pJNqWWK0uawZqablsoy8c+rKn9WY42ncTLG+DXwZzQZ4Gwnz7HYttVZDI81ww24O+n9bt+uvCnQhEw2XZtONYfAuuyIZK3CMjp8JG+sDjDWxWpWD8ISCt8d2vswzPxyftLP2iV9BMDDSUBzWGIQB0QcBloyLAMQmaLb0i/c+ccAWZysbvx846sE2cZBf5XXklh9jcPzVgnKs16DWGaUbrWsj8w0wn9hqUPXYMB26NBv2wGGjqWUAzeswZme2yiHBof78d5rCRVMw+mckXsqz8QkBAR0OKrclnsQ/3E/0kOEWCgwSH4HJoIVA2B0AIN08c3o6PcOULehg66e0HAJM/AhqcDNiCn6nIR7B8NnjynoBp1V6xMnYZ7a2R0qaYik+GqbsGGL7+LomlLVYCD9UBA/o1j/ifwWj1+4H+9QsA8qOKebAKhDNB55ZukypgXAGfFPtQMB2Q6kBwhwECDI+A5LBGoIgIuAw1LxzeVnZMDa9b06VvV45P3sd1SHsf4eFAPmcyD9kjYUdP0bNk8PvbwSUqOgCkkquff4sn7sGVSBH5EJX3UaPhfM9iA7S7PTtqX7YpAQBDokQ/An4FnKmIEyrSFgFnW+AT8NBbM67otWEuXI8fAf3qPeRBsduMpXQUOyEADjwEiQASsIeAy0LBUfEPZJbk1Wl3a3IROTd7Hdku5GTp8ZFtqufLkoBjHhcsd15fRzNt5zWhAZgMpOQIyBzBrB9d02U1ypLK2RJDh981AA4IOY1EY8mdZZbGfDQRkpfiYvw+fNQ3M2sCxDBkmMPQK/KRLA1crY0SOUQQCciD8Nw78X/CQIkagzH4RYKChX3j4IxEgAmkQCCzQoKaZVNb/S2Ok3bZyLnQYY1dm2dJ0EmYCNtuUPbI/42mNBvOmsuGPTiFoItcCN53IYxkTyTYCSJUajCUUw5rBBmQ1PGp7DMpLi4AcHl8v/5y2J9uXjYCp4/Q+/PU9GC8zSGEiIDvBfxrUfg08W5g2BKs1Aw3Buo6KEwH/EHAZaMCbOvOwu2s6WERT/s9P18dmazk21htptaGS2cNa0xNPDNWC/HoLdp0wx9/y+WXVSYJg60WD25/qZHWZto6OBq3TDDTo55ioJcXOPGVqWpexzPrxO3Dc/wResy5Wh2unLAQ/fQkeDp4vXDvqrrlsAv9pUPt98AJ1R6NE+xloKBFsDkUEqo6Ay0DDkrh56MPubulANgXpUJnYFcl+sd4Lu9LAzrjm5h14Ucs8SMhcsR//kkdKPfvKk8DuU3BLPe0v3mosm7ipGWxAVsOHGJGZN8XD3s8IMh2O9w/A+vA6Sz8N+ZMXCMjP4SfMb+Qd8MxeqEQlMiAgq8N/I8B63jFDJQOCGbow0JABNO3CtXUZgWM3IuAZArpswWVFYtzwDIU+eXkRVtT4xt3AQ0P0MhhvTUgpETgT7fVha7uU/dg8IQJjo7bfYmKN7C2NMMjUxzLQkBC5opo1voHkrcEIOETXwTfcgq8oqK3IbWjB5i3As4P/A38FnIFoBZBAhTSGQvE1wBOD8TdrbwAHEhEgAkQgGARcZjQsgZuGZjTsng4teQ99bk3Xx2ZrUxxM9Q68voEcF+M/g010wpIlJ8QYzBmW3q61FUz6TEbMS641qfL4o6JBa6Bew0Xt0aDVq2xnWLbJAfE14+9h6V1XbWUr+Et3ongCPHldUQjfbrMcRjOKRoO3DN8ery1gRoPX7qFyRCAsBFwGGrBFngk07JEOMhmGfvek62Oztcwb632wTanly+rc4nHd8sf2ZURZLvblgb5oFI4eckiMXY2Pn3C8RU1tIiBXxcc+HmJJ/iMgmGOYuca9+GzxX19q2DsCptAngtumVsr+vbfhtxYQYKDBAogUQQSIQAcCLgMNi8U3/z3TOUOeR7+h6frYbG3269aMhpNsSi1flswd439E+WP7MqIp8vYRcHjIF43C0UOmAG7fgjF5JxGBOiFgtsd9Bcf+CPDCdbI8XFvlMPhK79u3gLnsJVhHmkLWD8e+PD5YM/xWnIEGv/1D7YhAUAj4EGjYKx1igougsksyE8xLXGpgZ2xTWPMaO7JClSL/wPGkqbVDQrXAnd5ycjzhQ2FXEhGoEwKyII7978GvgpmSH4TrO3eMuhI+Y922IHzWm5KCGl1yE1gDRxeCJ+6tFb/LjAADDZmhY0ciQAR6IuAy0LBofKNIG2gYin7IanBJ8iZ0uMulBnbGlgdhx+t2ZIUqRberMxOW3UK1wJ3eJpW1Dfhd5k4HjkwEXCEgv4qvHTUP1rrCP8u4cnrss/Oy9GYfXxDQQJGcE/vydnxO5otmFdCDgYYKOJEmEAFfEHAZaFgkvknsnQ4Mrc+gdRpckjwKHZ5zqYGdsc2kS/eGr/EbOU2jla/At9nBtG5SNMggGmyYrW6W014igOP+NLC+WT2IaISCgFwQ+wy+I4WNgPwp9uUT+GRWoh1nMtBgB0dKIQJEAAi4DDQsHN8g9knnCd1xQneecElyA3T41KUGdsaWnWIfrGRHXqhSBMtgZBQYdQdI6RAQLJswD1qnpOvH1kSgCgiYQOVQnAMabFu5ChZV3wbzNvzq+Lp1dPXtrbqFptinLn/UQuHcQSq/uxloyI8hJRABIhAj4EOgYd903hDdw/yzdH1stzbr+tuhR+DrPKW5xeh+thEKS55sCl/qW8lfh6W3L9pqQUhTGJKBGl9cQj1KREBmwvGPwLN8AsbfJP8RMAGif8Nfet0/1H99qWH/CMjG8ONIsJ6DmNeQciDAQENG8AJ/IMhoNbsRAX8RwA0+E41GLxQDckrDMbpWrg49VQ9vAKIx4KXBdSY8KEc/greoMwg5bNcU5GnAe+eQwa4JEfg+iqZri1quAV+HbQ+mT9iNzQpDoPE5RG8NnhF8LR50WJyuMKxtCW7gDbjx2f34PB0+S1krypYelGMHgcYdkLMWeBKwLm1dzY5cSiECRIAIEIE8CLjMaFgQNwN9m5Dybbqcjz6IXLsk2TPWvQKRc3kGtvzXJZp+jC03Agc8w3Gf9Wz+0Jol8jHxy4Zeml7tUctR7VGrdHALlpKR/EBADsHxr/c0rv33wyEJtDBblT4On2mtoh0SdGATrxHQ7WblAzBeSMk2Xqvqr3L/D6opk4gAESACuRFwGWhYADcCnZTtn84KOQt9xqXrY7u1bBTrvq5tyeXLM4Wx9KasGRo1Jtku9il8S0qPgOCNrjmfEYQjFYnAmKjl112BhlbB/7cscjzKToOAYAcKcx5sn6YX27pEQKaGzzTgruv8t3KpCce2gYDMCj9iZzKdJ8oRNiTWTAYDDTVzOM0lAkUi4DLQMD9uAhpo+E06A+WUuF9Lun42W8uysQ4725TqRpagGKfxw8/cjO/LqDIlcNCAyyW+aBSWHqbA2pvAT5lLFQt2HgIND3QLNnz6TRThYYnkHgHdZs885CDrTpZ2rw81SIaATAd/vQhuA2+SrA9b+YuAuZ/fDV/qHPM8MJczJXcWAw3JsWJLIkAEBkAgxEDDcfHNY6oBbCvwZxMx1xtYBaLlslyM524FAhaIaN3iUr4G1zy7I6u7TPVvPS+YspoVwoT9EBGbH0soRjWDDW1RKybTJD8QkLlwDnwJ1hTuGfzQiVoMjICgxoa8BtaA8/oDt2cLvxEwBT8vgC/1nnQneAq/9fVGOwYavHEFFSEC4SPgMtAwX3wDOCAdjPKHuB8mBa7I3MB0TecZrjSwN65MAjt0B41/2JMZqiRBhoqZlFRgSYwLH2h9C/kIjLRVUtEIINBwZDPQgL/HtUeDfln0mJSfFAFZA+eBXleHghm4TAqb83YyC/ylWVmjwOs4V4cKWEBA/ghf6jKKZ8HwL2kABBhoGAAg/kwEiEByBFwGGubFRV8jzQcmV1db6lZUpt+c6frZbi2oNC7X2pbqRp5JGUVBrLqTYOcEkzp7ft2RyG5/Z0G8DbPLYM8kCDyEnW+w88QLzWAD/h72lvsdeZKoXpM2clB8r2IQNyiPy2zw29tgXf6yZlCqU9k+EJDt4csxYM0yWqyPRvy6AwEGGngkEAEiYA0Bl4GGeXDB10ADJmNpSHepMP0WTNPLflvzcD7UvlwXErUugfwA5tr6SHRd5xdgrunMdCiaKu5fAb9HM3Vnp1QItEWDl0M2w0/NYAMKQx6XSgAbF4yAubbqfW73ggeieKsIyBzw2btgbHvMrRKtQutMmPpRUM5GvgMzgNS3Hxho6Bsb/kIEiEBKBEIMNOyGm4RO3JZMaavl5nIPdHjDslBH4jSrxGC6iCMFPBq2s87A6h4pFZgq8uf4eGIqfwmeQ7DhjGagAUGHMQg2LFrCsBwiEQLSinPhKbCu+18hURc28gQBU2vjffhNg/CreKIU1ciFgNn+8j34sw2MpZKkXhBgoKEXUPgVESAC2RBwGWiYGxd6DRgcnE71zm0Il0/Xz3ZruRS6f29bqht58ovYFzu4Gd+nUWV6YKHbnJ3tk1Zh6SLTAr8R4LvC0jtMbYdH0eQoBvl+M9iAJRS6DKoRpjVV1Nqk4n+G8+ETMNeIB+Vi0czLD8F6PVspKNWpbB8IyEzw5dNgnX8e3UejOn/NQEOdvU/biYBlBFwGGuaKL/SHpLNJtoj7rZqun+3WclKsx2S2JZcvz6S7/wR7Ti9/bB9HlPuBxadgPqxldo+cGp8fS2UWwY6JERgdtW7YDDToJ7Ic9k/cmQ1LQEBWxvmga8SfAKNoKikcBES34v4YjBcL4vgFRzio+a2p2Yb23/CnBhsuA/Oc7HIYAw1dWPAvIkAEciLgMtAwZ3yRPzSdDbJB3G/ddP1st9ZMDHOTmte2ZDfyZBjsGepmbN9G7awDgkwPUjYEZGYcT5oufn22/uyVFgFkMlzTFWxo+R6vYIeklcH2RSIge8X3jIuKHIWyi0BAFoTvPgV/C8aW0KTwEdA6THIWWIMNmm2EbEYSEGCggYcBESAC1hBwGWiYI77A/zadNWbbML0xbJqun+3WsnWsf0UeRuUK2KPpoRPZRio8eSa1UjM8MAkhZUdA/gkMFUfW/sgOYuKeuJjPiBoNX3cFGwYx1TsxemU1NOeE3r9SZvKVpR/H6RsBs75fl8Aw2NA3SAH+IhfCp7pc8n3wwgEaYFtlBhpsI0p5RKDGCLgMNMyOi7pOuA5Lh7+ukzT9tknXz3ZrLQ5l9Pi1bclu5HUWhOTWT8YB8iD8q285GHjJfECKZi1p0a0rM4tgx1QIjI4GrYvMhudRs+H8Y6OIx24q9MpoLINwPui1RR9sHGfllWFv1cboDDbozgVcRlEZ9+ouFGbL8j0qY1J2QxhoyI4dexIBItADgRADDVjzbR7wd+lhS8n/lfliPQ4qeeCChtOK6AbX3QoaIDCxsk+Mx6qBKe6ZuvIv4KgPVVjnTCICRADnwnTgt8H6ZpxvUIM7JEywQZdRaLAB900SEagUAgw0ZHQnI/sZgWM3IlAQAshmMJS24B7WfRuaJP509YEUSkOzuFLA8rgvQB7ePkd8S9MB7M34QNp/hCUypBwIoGhqpOf6UTlksCsRqBACjW9gjC7903vfHTg9hlTIuBqY0ngdRq4BHgm+F/5DoU8SESACdUeAgYa6HwG03zcEAg80NHSSgZoGUUUCDY0xsOUlMAMNAAHPAF/in4fAW2IiyfuHQpKJGu+i21XgHYHj3JlEsBMRqBwCjddgki7/mxt8E86NwfgkBYNA4w2ouhpY5wD/gf+Y+RaM76goESgGAU4Ui8GVUolAVgTyBhomzTqwxX7Y8iqa3aI816KehgJLYNLkOlvENQ7N8XXHhJnBOqEkZUfgRHTVe/AfsotgTyJQNQQaeECNDgHr9eW8qllXfXsab8W+0wyVu3Hf1CwHUiUQkKn6NkPmgq//Av4/MOus9A0UfyECRIAIRC5rNMyCizSCDXJ4Oj/I1HG/Y9P1K6K16JsMTaOsCMnOMbYrVcSgnGZoSrO0g8/NKYjdI7kaOI4Bz0EwiAAR6I6AnIPzQu+Fv+/+Lf8OBQHN1JJ3wchylHVC0Zp69oWAqaGiu4v8sfcW8hB+Gw6+Afw9+Jje2wX7LWs0BOs6Kk4E/EPAZaABb4rN5OqIdLBIa9zv5HT9imgtF0OXH4qQ7EamKXKlE96D3Yzv46hyD/D4HIw9t0nZEZBFgeE48NnZZbAnEagiAmYnCg1a61awWruBFBwCGkA1BT5H4XOD4NSnwj0QkP3hR33JgKxGmXz8H00gIs6o1a2bTQB96fHbBP0/BhqCdh+VJwJ+IeAy0DATLtD6UHtkekjMA8uZ6fvZ7mHS59SGaWxLdiNPUJzMROh1TT3JICC7x8fpWgQkLwLmDZBOxGfJK4n9iUC1EDCZesNwbuCeLD+rlm11sUZmg+/eAGvm1iZ1sbq6dspq8OMX4AGybk02y94VwoGBhgo5k6YQAdcIuAw0zIgLuD6k/yE9CKIPK+en72e7h+DmYmxY3LZkd/LkftiErddIHQjItMADu3HIBUQkLwL6AOVLkDCvLeH1Hx21btoWtTzRHrUcFZ72ddDYbJn8Fc6RD8HI+COFh4AGUeU1sN4zfhWe/tR4fARM8GhQj++2g2/1BcT04N+CdR77y/HbBP0/BhqCdh+VJwJ+IRBqoAHFl+Qy91DKhvFNZn33utjSQE6MbRpiS2L4cuROYKIPAKwMn9uZohX2NVCIt3+kMhFoi1rfa49akQ/cileuLTuUOTbHSoqA7l5g3oj/F5+TJe3Fdj4hIPoS5WVwO3h7nzSjLlkQkP3gx9W7eqpPzTInDTDocsC/d/tNl9DsD16567vg/mKgITiXUWEi4C8CLgMNM+BirBfqP6aHRz5Bv+vT97PdQ3SHBrVhL9uS3cnTNcLGpo3c6eDbyLJTjAnX3uZ2jSD7x0zSzs0tigJSIYBMhtuagQb8/c3IKGKwJxWCZTXuvN7cinNlorJG5Tg2ETCFhJ+Nr3V72JRMWWUjIB/Aj+eNP6pmOJpdJ1AzTEmmAGNOagIPj+JTl0E9Be5R36Gjtef/MtDguYOoHhEICQGXgQZNO9OH9KPSAybvoN9t6fvZ7qG1GYwNf7Et2Z088zZG/VIhm/KiKVMCDzyXyRV5JbG/ImB2oGjD59zEozwEkEYyJwIM33cFG1pR6JTkJwJyLM4PvQ6f4ad+1GpgBEzdjSfgQ33rfeDA7dnCTwTMiy1kNXQnU58rXmIqk8K/j4O/AK/e0crUu8LvQZ6/DDR0dzX/JgJEIBcCLgMNQ3AR1onUn9JbIK+g333p+xXRwxTvurgIye5kmuJG97ob38eR5Voccz+AQ3xD4RmgsiBwHAuu2HnjGcy9qNMWDd61W6ABC8kH95hA99KJXzlCQAOb5h75G0cKcNjcCJg33Q/GfsyQvZlbAQrIjYD8A/57GYysBSVZAKwvu+Llu3IN/v4O3GPnCTkO3yG7IThioCE4l1FhIuAvAi4DDdPhIqyBhj+nh0ee8ecCbgo/eRL0SI9k7z3MG2e9cTZ6/72O32oVcXO87lBH6+3bLJcATw02YNJGKhMBZDXc2hVsaPlhdBTNV+b4HCspAtKC8+Ph+DzZOGkvtvMNAZkEPrwDrPOdk33TjvoMhIDJWLgfvsOlUh4Bazbep+BFwFuA1a/rTyhFbsT3V034vfffMNDgvYuoIBEIBwGXgQat5q8X6KPTw6VRYg02+ECCN//yug+a2NNBDop9s6g9maFL0kKQpiDkXaFb4of+Mg/w1AlbiBMxPyDMqAUu+jMi0PBFM9iAnSgePzaKWAsgI57FdhMNyOP+YrKpfl7sWJReHALm/nEd/Khznn+CGcQvDuyCJJuXDcfBd8gwkpnAE4M14PCPCQeU5fF9O3jzjt/MUooV8f8DwLNP2N6rbxho8ModVIYIhI1AqIEGZBDo8gkfSC6CLkiprxLJCrBJJ0S7V8mq/LaYCaJOHmbML4sSgON54J/AixGNchHArhO/agYaOj5bjihXA46WHAETlBuO80R57uT92NIvBLSwp1wI1nvrFWA8qJLCRUA0cNDLfECXV8qbYOxWpSRLgd8CjwDfDf4afHzHb17+y0CDl26hUkQgTARcBhqmwcVWb7jHpIdOC0HqGjkfSI6N7UCGRlXIpOyOhl0XVMUiO3bollXmmGVhLyuA6psdk46KFFNS2Qggk+HyrmBDyxgEH5YoWweOlxQBQTaD/AgeBkaWAylMBMybbRT4NPeRW/CJ5TGkMBGQXeC/z8HdssHM3OlefKfBhNnAy4A1wHAfeJYOO2VZ/K1z3194ajcDDZ46hmoRgRARcBlomDq+2B6bHjizjdAn6fsV0UP2iO1Ysgjp7mTKk7DrJXfj+zqyKZT5lK/ahaeXnIXjbBx46fB0D1vjb6NoGgQaPmoGGxB4eOHVKOKDj7dulY1xnowFPwKOt9XzVlkq1i8CplCgPmzqQ+lk/Tblj54iIAvBd3o+btShoFlOcQf+rwHBlcDTgzULSc/XScc3wtT28vWFBQMN4zuL/yMCRCAHAi4DDVPh4qs3Wqx5S0ta7Ve+SdurmPayXmxHfLMpZpTypeq2TCatfYryx/Z5RE15NMftAj5rGY5uMjPw1InZ7eHoXB1NR0eD1kFxyHHNYAOyGk6qjnVVtET2i68/1+KT6/yDdrEcFvvyMXzixQspPARkf/hOC2fj5YN8D34TjOwjJVNU+wN8Inu3O5mdSPSet1n3bz36m4EGj5xBVYhA6Ai4DDRMiQutBhr+kh5EOR/9RqXvV0QPQcFEY8e+RUh3J1O2ie1a050OPo4sC8e4ZAiQ+WiPDzrJqTGmWJpCKhuBtqj17GagAUGHn3BhnadsHTheGgTklPh8OT1NL7b1EQHZC77UOjXPg2fyUUPqNBACGiSSncHLdrWU1fB/zdRbveu75l+CYK58C0YtByWZA6wBC1/ufww0dDiG/xIBImABAZeBBrwpNw/oGYriyJnoqxdxD97omOi0BkxOtOAPj0SYm5/a9SePlPJEFXkauLzrx/HnCSS51DCV9fWt0MO5xLBzJgRQNn0yLJsY1hFsaBk3OmpdMJMgdioJAbPO/0qcL3p9xltxUtgImKB+G3z5FnjusG2h9h0IyN/hy/snRENWxfcaWNoVrHPg68E6l30UPAysmRFxAGLC3iV9w0BDSUBzGCJQBwRcBhpwMTUTpRPSA20iwjrJmiR93yJ66DIOwcSvaiQfw647q2ZVfnvM9lZ6/GHSQLKDgBwFPBXTDezIo5Q0CIyMojmwbOJk8OZp+rGtKwTMdon34nzRh5TtXWnBcW0hYJZgajo9ak/J4rakUo4rBHTepLtUdSdZBN99Bb4NPCn4cfAX4NU7WpkA4tv4/xndezn4m4EGB6BzSCJQVQRCDTQcjYuxPpR4stODvABdKvg21kTbNYjiQeaIT6egDAEmY8AX+aRV2LqYLcGGA1NNIebxFrYzqX0pCIguP3wWrG/D1y5lSA5SIAKmgODX8KXec33dkaBA+6skWv4AH2pQAQEFJVkFrC9ungHreXsNWLP4ehRBNkVCkd3glBhocAo/BycC1ULAZaBhMlxkNVjw1/SQyu/jvrOm71tEDy1kJ+8VIdmtTDkkxhmReNL4CMjNwEaLP8UTifF/5f+yINCZKbJtlt7sQwTqh4CpdP8OrkO6hV6Ph5b6oRG+xabm00fwJZKMZJPw7amrBSbj6N/wIVamyXPgseAbwVOBtwDr3Hf9CdExba6a8PtSv2GgoVS4ORgRSI7AYDTVB98FwPOC8dYzmhjsM7kMNGjqmF5sM9Q2kAPjvvP5Aa6cA330rdJEfuhjSwtZPsZ5D1sSqyNHkGJujt/tqmOTa0vM5Oxd4KprlQe51objE4EwEBDMOUwK9mf4nCcMnall3wiY+ki6Xr8dvGvf7fiL/wiYLTB3gx9n6dBV8Exggg//mFB3M99Sn2Nu4ZQYaHAKPwcnAuMjoEVb9gH/F4wHzQgPzuMx1txFj4C3AvtILgMNkwAqDTSgAm9akj3jvoul7VlMezki1seTDAtbVpoHv1Gw7UJbEqsjx2CjqZH3VMcmHyyRHeNzaV8ftKEORCAMBGQ5nDc/gDVIN2MYOlPLvhEwy/PwwGfmSIf33Y6/hIWArAifajChxzlqlg6+ie/v9MAeBho8cAJVIAKKwBzgD8AaXMDDWIS1xdED4BvASKuOhoJfAuOiYtrod9gz3ityGWhoBSwaaDg5PSKyQ9w33q84vQS7PbQYl7EFN5GqkamG/FrVrLJjj+CthEmJjN9W2JFabymaFSS4bpq3PlyWUu+DgdanQkDWxXmDFx4mVRsp2qSwETAPn3fDnzpP+juYtWvCdii0l13An4O7Zb9KC/5/L/hr8GwemMhAgwdOoApEQBF4FTwSvBe4v5v6DPj9SPBP4P+AfaJQAw2/wgVZb76/9ANMU+hH9dnaD31samH2bNfK5tPalFoNWeYtovr9d9WwxxcrZGNgqrjqdZNEBIhAYgQES7nMThQP4hPBfFLYCJjMuSvj6+EV+OSSsqAdapZSaL2GjTrMMDVW7sD/fwSv5IlpDDR44giqUW8EtA4DJsImyJAUiRPiPj5lNbgMNGgUVx8mTkkKYFc73QLP9MUbHB9I5oz1OcwHbezqIJvFtm1oV25VpAmyPeTlqljjjx3yGHD9FjydPzrVV5P2aNAq4JXri0BIlstB8TVbi891e3Makg3UtQsBs+3hGbFP78InCmmTwkVA9ocPdceJp8Dfg98E95KdazJapnRgJwMNDkDnkESgJwJb4gsNNMzf84d+/r923GfZftqU/dP/MKCyAzKReg00nJp+cFkD/bQvHoJ9IH3LYFLoz/JBG7s66FpCg/UJduVWRZq+dTf4LFMVi/yww2z1puc4JtgklwiMiVq2b49asbC4FXn5g/d2qQvHToqA4HptrksXJO3Bdr4j0HmvwYOgTO+7ttSvPwRkavhwZ3A/zwNme/HhaLNmf5IK+I2BhgJApUgikBYBfbOjgQYULktMx6OlLp9wEaHsS0mXgQZ9ONcHidP6Uq7v701BHe27bd9tyv5FPoQ+t5Y9ajnjmYj70HLGCm0UXVNpgkxnh6a5//oK6t3IGPB8/utaXQ3bo5ajm4EG/D0KgYclq2ttlSyTc3Hu6H0yQzC/SjhUyRbZFf5E3S9zT563SpbRlp4IaJaDoA6c8Xd/y7N7dsz7fwYa8iLI/kTAAgLYoiYaBh4B1l0npgH3RRp5PhysQQYU9vGKXAYagKGZBP0tPSKCia7pu1v6vkX1MEUTny9Kulu5cgnwHgke7FYPX0cXTWfVQk5cE23VRRpgMIGGm6yKpbBUCOAmMSMCDZ81gw1tUcuwT6OI6dupUHTR2BRWvQbnkAYb/uhCA45ZBAJm6ShOS1NUcPkiRqBMXxDQpYPNeg6l6cRAQ2lQcyAi0D8CWqfhETBu4tFo8Evgh8C664ROjFGMKXoRjOizafMaPn1Ld3MZaJgIsOgE6O/AJSWZfcO1L9a6+UJyJfT5xhdt7Oohe8S+WsGu3KpIk61ifLapikX+2CGnx9j+wh+d6qfJ6GjQ2shmGNct2HBx/VAI0WKzRBFb5pl77X4hWkCde0NAsFRPPgNrEcFNe2vB74hARgQYaMgIHLsRgSIQ0O2G9OaNoi4RUnxNQEEDD8qawTAc/BD4V2A8WHtHLgMNwM5MfvAgkZZk9rjv79L2LK69nBjrNEVxY7iSbCola2DnMFca+D2u2Z7qK+Bzj996hqid7naiATxTOEuvtyRHCGDJxInNQIN+ol4D1hiT/EdAJsX58zAYcxLdiplUDQRkLvgTL7DM0j2PXrpUA90aW8FAQ42dT9P9RkDTymcEzw/Gg3CEGgTekw+BhgzF3mQIbq764PtnfxAWLKExOi3mj042NZEvYd/NNiVWS5agEKiZyOu5T7KKgBwSn1vYuo/kCgFEzAe1Ra2PdQUbWn5A8GERV/pw3DQIyFQ4h54F6/r+jdP0ZFufETCBWA0i6XzoFDCDsT67KwzdGGgIw0/UkggEgYDDQIPiY26OWQINWB9s+iKLwBeS9WOdNvRFI7t6aKFLXRNK6h0BWSr2/1G9/85vsyNgMkbeBr7vg1kHIzuQuXuORBAdSyi+agYbUK/hFdZryA1rSQJkBpw/qC0lo8CrlTQohykcAb0myjVgDTboJ6+RhWNe6QEYaKi0e2kcESgXAR8CDWemN9nsK6031QxBivSjJeshi8Y3+n2TtQ+tlWCZipnILBia5uXpK88BIzwQk+wjIFvGx9/h9mVTYhoERketG/ao13BJmv5s6xIBs+zwfZxLI8DLu9SEY9tEwMyJTo2vkZrhgCVnJCKQCQEGGjLBxk5EgAj0hoDrQMM43BCRcp6FBAU45bwsPYvpI5PHN/mTipHvWmrnlqK7udbE3/HlwPgYWNVfHUPWTB4Dvt+BfSuqGzKomXTHkomTm1kNcb2GXTIJYicHCAiWdwoSUUztkyUdKMAhC0NAC2Sbmg2v4XPuwoah4CojwEBDRu9y3VJG4NitVwR0Hf76vf4y8JcZdlkYWGjGFhpoUJqy46Psf3VNe3ROFDUOSj+yfIs+t6GvRxNcQUHA6F7oVMGCW5q+Hn0Pvhr2YRcK0oQI6FZU0WdgpK82dp3wd36TDwHzBlYnQf8Evgfkk8XeeRB4CPUafhG1DsXEKt4NREYiarxca9SGBxyS/wiIzmHw5tsUrUZgtPGG/zpTw2QImF0ocA8y269vBN8i045EBBIjoPdYpRU7PvgvESACLhDYDINKRnahb19jus5oGAsY/9GXcv1/L5+g7/X9tyn7V5M6/3jZo5Y3nqlczglpv4DLdTgudcuxqfttxh8zImDWIGtBu4UzCmA3SwiMnLBewzOWRFNMKQjIsjiPEDyWj8DzlDIkBykJAQ3Kak0l+QHM4p8loV6RYTTQ0Aw2VMQkmkEEwkRgL6jdBkYKf7QzeNOEjGbekOtAgz4wnJ0NDXkLfe/I1reoXrorg3xclHT3cuUE2IcAm8zkXhdfNZC1Yoz4xr0QF5kt3bSY3V2FiKfQVAiMX6+hBev+SWEhIMhIMQ+j7+FzzrB0p7b9IyDzwqevg3Vb00P7b8tfiUAnAgw0dEKR7o9B6ZqzNREYEIF/ocUXYFTjjzRivDXYBS2CQX+ecWA9L3T5givSrJCsy5rwsBFN6krxPsZ9H98j20WXGTQ0CFU1ejQ2aBV83lQ14yzZ8yDkIAgW7QPOGESzpEklxTQ+wPn1N5j2J3xugPPs7kqaGYhRk0Rj7mqLBu/ewHKxcVF0biBqU81OBBrIwDNvvDVwh2uXrIpzCvUbSOEj0HgX/lwJdtwIPh1/L4RPBMAbyCQlEQEiQASIQCgIXAlF9YF5bUcK3xmPrzpk4XZHequ6Y8D/zDa+PIW+T2brW1QvOQg6wQdabKuKJKjlYQpNZSzgWUVMerOpc4cOvC0k2UfAFF7VpVPDwBosJREBIpALAVkH5xKyM80b8JlyiWJnzxCQwfArXoyZucl9+OSyPs885Jk6zGjwzCFUhwgMAQR46NUCZU5oGoy6TEbGWvJIl084IjOxyfgWTB7CDfMFR4r3MawWYTI3c1dBpz70svm1PAMbn7MpsXqydFcEc2xfXj3bfLFIdorPtYN90Yh6EIGwEZANcU5p8P8V8Axh20LtJ0TABMB1GcVrYCyrIBGBXhFgoKFXWPglESACWRBwXaNhFG5452VRHP2Q6imeFSaUn0EnzWio8K4Mcgbs08kK34r0e+DKVcBIj2/diYJkHQGzb7xmNX0L1mAviQgQgdwIyOY4n5DlqEF8Xrtyw+mdANkMfv0B/CVYl0CSiEBPBBho6IlIwv9PlLAdmxEBIhAGAniIiybzTNX3Y33m9kwvm+oMhTC9nmItL6kfBM7Hb5OAd+mnDX/KjEADAb3oYLAGvI7PLIYdiQAR6IZA41b8R7dnXhx8Lx5G9fwiVQaBxr9hyi/ByFyJ7od/kRlGIgJEgAgQASJQDAKuMxpG4kanD2QZSK5A368ydCy4i3nDCt2qSjItcNeMhr9X1UJ7dpkUVVT9JhWHgLkOoLiZZhORiAARsIOA7BBf5/F2U6ayI5NS/EFAZoVfdRmkZmAiUKsZYiQiYBBgRgMPBCIQKAIvQm9ln8h1oAE1IuSCbIBoP9EaE56RPA+9HvVMKcvqaI0G1mkYGFQ5GDjpRG71gduyRTYEzIQZ1zF5JFt/9iICRKB3BGRXnFfjwE+AUQiYVC0EBBmhgt2jzD3qOnxOWi37aE1GBBhoyAgcuxEB1whoqq+yT+Q60KBrBVENOQvJmeiLSZBvJEg9lQ9908quPprNYLIakN1A6hsBk/2hWTvX9N2Gv+RHQA4HxhrQ2T6/LEqwjcDoqHUDbIG520NRxB1CbINbuDytN2SCDY/hc4rCh+MAJSNgat2cHF8/tebNLCUrwOH8Q4CBBv98Qo2IQCIElkQrZZ/IdaBB30RemA0QOSm+OU6SrX9RvUwARJcWYEupqpLuu24e7DarqoX27JJLgRXWw7KKuz1Me0qSFuD7BvhjMB+GesLj8P+jo0Frt0etqC7YKm1RCwNuDn2RfWjZC+eVZjYgU4/nV3Ycfe4pu8G3ep/6CLysz5pSt8IRYKChcIg5ABGoDwKuAw0jcFO7KBvccjT66ltMz6r6yyGxXvNlsyuEXlogTHRd/FkhaOtWR1khPh7+4FaPqo8u68U4/63qloZk35ioZatmoKHjs+WIkPSnrk0EZG+cXxpseBzMZRRNWCr1qbtQyBdgzcLbrlKm0Zg0CDDQkAYttiUCJSKgb7BnBS8Anhc8BDwx2GdyHWj4Hje0i7MBZPaE1kDD7Nn6F9XLbB+leq1V1Ah+yBXcjHS/ddLACMizwOoDsO/Xg4FN8bqF3AiMdWu+xbxWs0bKXY97IAIM93UFG1p+QpbD+jWCoEKmyp44tzTY8CQYwWZS9RCQueDbF8A6h9GsUd1hilQvBBhoqJe/aa3nCEwO/fYB/xfcBsbFeTzWYoVapGwrsI/kOtDwHeC6JBswsj/66s1QAzsekWB5jNELk7Iqk5wY2zlTla20Y5tJS9VjlUtN7ADahxSZAxhr3ZeH+2jArx0ggGjydG1R6zvdgg3fom6DZ9dtB8AEOaS5lunSwKfB0wRpApUeAAFTJBIxQjOPuQOfUw3QgT9XCwEGGqrlT1oTMAKY1EZ4S2kCC6Pw+Tz4AfAN4JvBQ8EvgfGGzbTR72YG+0SuAw3fAppLswEiu6KvPrz9LFv/onppWqnR669FjeCHXM3YMHYyxXJAh2g1b/kafN+ATdkgJwKdhSF3yimI3S0igCUUi7dHLf9rBhtQr+HVL6OIKfgWMS5PlOyMa5kGG54BsyBwecCXPJL8Gf7VDJZhYAYGS0bf4XAMNDgEn0MTge4IvIr/YC1btBe4v4jvDPj9SDBuzNF/wD6R60DDN7iBXZYNENkafTXQsFK2/kX2kq+g19VFjuBetkwCG0eDL3SvSwgayGnASidtC4Wgbbg6ahFWwbVZhoOnDteO6mmOYMOWCDaMawYb8PetsLJRPUvrYJHsgPNrLPg58JA6WFxPG81SUK2lpS+F1q0nBrWzmoGG2rmcBvuIgEZ38ZBrggxJ9Tsh7uNTVoPrQIO+5b08KYDjt5NN0FcDDR7WQhAspdF1rFUneRB2vld1K+3YJ6jbYt4CsoCmHUD7kSKrA2u9NpzdTyP+5AABBBuO7wo0tKLMfctxDtTgkFYQkG1xjmlNlBfB01sRSSEeIqA1b+QdsAaWfuuhglTJLgIMNNjFk9KIQCYEtkQvDTTMn6L32nGfZVP0Kbqp60CDvvm/IpuRnan7G2frX2Qv0fWNeKNadZKjYKc+0M1TdUvt2Cd3AiutSzK5HXmU0jcCciVwRhaZLNN3G/7iAIEGMhlu6wo2tIxDsGELB3pwSCsIyK9xjmmw4WXwjFZEUoiHCOjuXnI/WO/3yELVjEZSRRFgoKGijqVZYSGwMtTVQMOOKdQ+Hm11+YRP61JDDjRgyYS56WEJhW8kp8S6TeabZnb1kRVjO/e0K7eq0mTDGC8tIEsqFAFB5pgJ6mDSJEzPLxTrdMKRxjYVajQM6xZs+B+CDXhrSgoTAfkVzjEUwzZr+WcN0wZqPTACMgg+RkaemXdp1ubsA/dhiwARYKAhQKdR5eohMDFMQoGcaARYHxr6q76sKYWHgzXIcDfYJ3IdaPgSNyu8ecxCWgTS3PB2zdK72D6yb6zbosWO41q6mXh8D1uvca1JGOPrA69JQX0pDH1D11IOjM/DvUO3pGr6Y9eJBZHZ8F0z2IBdKd7GIvD+7qNVg6Bi9ggyC03NnrfxOWfFjKM54yEgu8DHKIAun4N/Od5P/E8VEGCgoQpepA2VQEDrNOjWlZrZMBqsDw8PgW8A3wTG+vUIaxc7d514DX/7to7RdaDhC8B3FXDJQLIg+gJ73ebSN5L1Yt028k0z+/rI7bAVEw5SMgTksPjY4AQtGWA5WgkCwvI8GC/RuYY8B5CFdEWwYUMEG35qBhvw9xGFDEShJSEg6+A8Gwl+H4yaNKTqIiDLwccfgTWT5TfVtbOWljHQUEu302hfEdCU3P3AT4HHgDXo0GTNYBgO1uADUgujicC+ketAg0bEM74N17Q9E2j4nW+gQq9mEOQA/3SzrZEcEvthCduSqynPrHXVyfj11bTPN6vM8h7d7eMS3zSjPhqFbzm8GWjA8gm9T5KCRkBWw7mGeYV8Aq54Rl/QjrKgvMwEH+Nlm5mHXYrPSS0IpQj3CDDQ4N4H1IAI9IrAYHw7I3h+sK5dw3o278l1oOEz3JyuzYaSeWBDUEeOzta/yF7SAr0QaJIzihzFD9mmIrX64bd+6BOCFnIB8NICalzjWoq75DxgrccoHoJIviHQHg1aFbyyb3pRn6wImOCebl2tSyOXzSqF/UJAwGwn/H/x9VW3OooyMDQAAEAASURBVJ07BK2pY78IMNDQLzz8kQgQgTQIuA40fIob03VpFO5qK63xze3kru98+ks+hH7/9kmj4nQxb6/uKU5+1SQLsj/Mg++JVbPMT3sEa/91FxhTrA5BQBIRIALFImBqKGnGotbwWaXYsSjdPQKyA/z8I1iXqWHpKClgBBhoCNh5VJ0I+IZAwIEGhdJkDSCa7iPJUOj3so+a2dfJbHelkwwEf0jJEBDUcBHd3pXppskAy9lKtgPWmtVwXE5B7E4EiEAiBMwSQg24671h3URd2ChgBExw6R34WrM5detr7vYTpjcZaAjTb9SaCHiJgOtAg67jzLFW3awFvdBLZCO5GLb94KdutrWSHWGrPsStaVtydeXJpjFm+1TXRt8skzuBOWrp6HIfEhEgAsUjIHPhfHsrPu+2KH48juAWAZkWvtbrrM4HkNEpU7vVh6NnQICBhgygsQsRIAK9I+A60PAxbkQ39K5akm/N9kpXJ2lZfhsT0debLQomVZ1MUSi19aSqW2rPPpkIeL0JHgbmmx97wPYjSbfdM8HJJ/AJ/ElEgAgUj4DMjPMN2X0yFrxT8eNxBLcImHvbsfD1OLAGmZZ0qw9HT4kAAw0pAWNzIkAE+kbAdaBBt0e6sW/1BvpF3kP/Wwdq5eb3zlTtldyMX/ao8iJ88UzZo4Y9nm7Nat78bBS2HSFpLwfFmNdgR5iQ/EJdq42AKd78NM49ffj8TbVtpXUdCMiG8LXWbNBdlnYhKsEgwEBDMK6iokTAfwRcBxp0/eZN2WGSV9H/vuz9i+wpK0A3fcu/Q5Gj+CNb/gZbdW3mEH908l0TmQx46UTsAd81rY5+5m0bJlIyAjxHdeyqriW4Sc2ARf6zVtfCulgmU+KcGwrW++Kf62J1ve2UueFrvIAwPj8fn6zj5P8BwUCD/z6ihkQgGARcBxo+wI3n5uxoyX/R//Hs/YvsKTPEN9eaTKi00rSZTGxdJKrVky0nxrj9rHq2+WqRLA7M28C3+6oh9epAoC0avHx71DIKPHZM1IJaMKSwEZBJcN7dFl/zsP0zl42F7c8k2psdwi6Ifa5ztrmS9GIbZwgw0OAMeg5MBKqHgOtAw/u46dySHVZ5GP2fz96/6J5mPfilRY/ih3zdPUFGg//lhz6haCF4U2seei8LReNq6CnHA3d9s7pNNeypphXtUevvwdLBLWPao0GrVtPSOlklg3DeXR6ff5fic+I6WV9fW2VX+FqXUWgW3wb1xcF7yxlo8N5FVJAIhIOA60DDe7jh5KixIHeh/xv+wm3qFjzir362NdMlAIIsFVI6BMykewywY3p4OuBytDZv2bQQ5+fgITkEsWuBCOiSCQQZhncLNnyNzIaFCxySoktBQDMZ5CywBvs0w4Hb/JaCu+tBzBaYb8PfWqtDg70MMrl2yYTjM9AwISb8hggQgYwIuA40vIsbDbZAykpaSFI+ytq7+H5af0I+LX4cX0aQ38NenTgu4otGYeihVbkNbqeGoW9VtJRfAPefwJ7uXFMVnPPZgeUTK2DpxMhmsKEtan0P+wZjJwNS+AhorQZz7XsMn9OGbw8tGBgB3fJSM1mN3x/CJ8/lgUErswUDDWWizbGIQMURcB1oeAc3GbzNyEqCdHP5Jmvv4vsJHhzNzRRF/+pAnQ/Mh9bBWrs2yt04Vr4HT2VXLqX1j4CcEZ+jm/Xfjr+6RABZDFsi2PBTV7Ch5bkvomgKlzpxbFsIyN44B3Xry1fAs9mSSjm+IyCYJ5hlg5/hc03fta2Rfgw01MjZNJUIFI2A60CDptDdnt1IORf9URfAVzITKH3Dj+JzdSH5BPbeUxdr7dkpawA3PVYOtyeTkgZGwOz8odchZB7xjerAeLlrgUyGg5uBhvjzP3gdivX+pPARkC1w/o0CY+mdcGlM+A5NaIGsGPtcM8uOA0+UsCObFYcAAw3FYUvJRKB2CLgONLyFG8sd2VE3Wyrqw5mn6/xkLeim+m2e3cbQesrFsFcnjFxzm9p1ZhswDdS0pO7KDjkQkNWAua4ZRoYUyWcEkNlwWvdgQ1vUcqnP+lK3NAjIqjgHvwN/BV4hTU+2DRkBmQ7+bu5EgtihzBKyNRXQnYGGCjiRJhABXxBwHWh4EzeVO7ODIX9Bf32Qx/7cPpLMHet3mI/aFaOTVvE3Plm/GPlVlirYGtRgt1uVrfTTNjk7xn5DP/WjVjECDQQXrukebEDw4QSiUxUEzPI7zS5CHVDhuVgVtyayo3MphRboXTdRFzYqAgEGGopAlTKJQE0RcB1owI4RunNEVpIj0F8DDZ4WE9I0QNHdBM7NamF4/czbCU2DxNp3UjoENDNHtG7Ja+BGur5snQ8BwXp/eQ/8EXjqfLLYu0gEXo2iFgQaHuoebEDByH2KHJOyy0TABOh1btAO3r3MkTmWawRkefj8XfA48Ilg3BNJJSPAQEPJgHM4IlBlBFwHGl7HjeTu7ADLgeivgYZ5s8souqduvyn3Fj2KX/LlSdg8zC+dQtFG9o+P6U1C0bg6enYudbqwOjZV0xJUAJ4amQ0vdwUbWsaOjlr5Brwy7pbpcR3U+4je3/9cGbNoSAIEZBr4XHfsUt8/Cp49QSc2sYcAAw32sKQkIlB7BFwHGvAwmqdwoL7tMDejJfz1pGZs6FvqOpEcG/tlrjpZbcdWrW0hX4IxwSKVj4BcEB+7TN0tH/xUI46MotkRaPioGWxA4OGZVALY2HMETKHW5tr983Fe8u225x6zq54cAJ+PBmvNjo3syqa0fhBgoKEfcPgTESAC6RBwHWjQFPH/pFO5e2vZFv016r1C92/9+lv+D/qNBQ/2S68itTGVpNUvTGfOBLMcA+wUv5UydWenHAjo9qKm8v1H+OQSihxIltEV9RkWx7aXX2uwAbtSIEhEqhYCZjmZBhn0eqhBBxYZrpaDB7BGloHPtWi4LqU4DVyjedQA0BT3MwMNxWFLyUSgdgi4DjRguW2eZQWC9HIzAVnTX8/JwbGO8/uro23NTG2Kr2H3zbYl10OeDAF2WgztlnrY65uVsjaw14ktd6HwzTW96PNDFM3UHg1a7dgomqiXn/lVJRCQo3E+arABD0EyQyVMohEJETDB32u7+d/jpbIJTfK7GQMNfvuH2hGBoBBwHWh4GTeP+7MjJggwmMkHAg6+kqb8GR038FXDYvSSa2D3CDDfQGQCWM4Cdvqwu2im7uyUE4HOXSi2zCmI3YkAEbCCgFkqqQUitWDuglZEUkhACMje8DtWTJl5xY4BKR6aqgw0hOYx6ksEPEbAdaDhJdw0HsiOjy6ZMA/xWELhK+mEyOh4oK8aFqOX7Bzb7XG2STGW25EqcwC/NvDlduRRSjoETK0MLVar64NnTteXrYkAESgGAVkH5+P3YM2YW6WYMSjVXwRkEfj9BbBmt1wJxlI3kmUEGGiwDCjFEYE6I+A60PAibhQPZneALBHfcPbILqPonvpG39Ro+L+iR/JLvqa3yk/gM/zSKyRt5GLgp2/w5g5J6+roKj+P8UdBVxIRIAJ+IGDu+1pDZTR4Gz90ohblISCt8PuZ4HHgd8Erljd2LUZioKEWbqaRRKAcBFwHGjQy/VB2U3VbSxPZPii7jDJ6mlTPGj6syOPwz9tlIFzNMWQh4KfBmnOqaV8IVunWeuYas18I2lJHIlAPBGQ2nJfPg/Vh88h62Ewrx0dAsBxVPgdrMP4o8ETj/87/ZUSAgYaMwLEbESACEyLgOtCgE4WhE6qV9BuZCf01he4PSXu4aac7a2jl5LqRHBH7h3UGMrtebgCGo8BM38+MYZ6Opur9k8Bfi3NyXXgeKNmXCFhFQKbEOXk3WOcAF4JZD8gqviEIM3PA5jEwFMcAlhySciLAQENOANmdCBCBLgRcBxqew43h4S510v4lU6C/TjKOT9uz3PamsN9Y6NlS7riuR9NChsY/CDiQsiEgS8cY/i1bf/bKj4BgxxjB5gbyFHhQfnmUUCYCiBDNgm0wTwZvU+a4HKsMBEwg8J/xNfJ+fE5TxqgcwycEpAG/HwrWpTTfgLf0SbsAdWGgIUCnUWUi4CsCrgMNz+Km8Eh2cMw2ihpoOD27jDJ6CtKuzQP3YmWM5tcYunRCl1CQsiMgtwNDfdBF3QuSGwRMxXO91hzrZnyOmhWB9qjltvaoFfnVhn+bVQ77+YyAedDUZWbDwFhSSaofArJU7H+9Tv8LPHn9MLBiMQMNVmCkECJABBQB14GGZ3AzeDSfK8x2R+fnk1F0b1kDdurNr4aRdi0GaeoM8CE582Emy8fHz0mZRbCjBQTkDvihHQx/kEJBAIGG67sCDS3j2qLBu4WiO/VMg4BsinNTA7JfgFdO05Ntq4KATAbfYz5o5ltv4nO5qlhWoh0MNJQINociAlVHwHWg4b+4ETyWD2T5EjKwzZHPJLPEN76jfNayGN06gyy7FiO/LlLlHhxDI8DT1cVi/+w064H1evMGGBNaUggI6NKJtqj1nW7BhrFYRrFFCLpTx7QIyDI4Nz8BjwJvm7Y321cFAdkM/tdrtQaGMe9iocgUnmWgIQVYbEoEiED/CLgONDyNG0DOtHp5HzJu6d9MH341e39f4YMm5eqga9rlW/BN5Y5btdH0DZ15S+N5PZKq4d7THtk89sO5PX/h//1FAE+d8yDQ8Gm3YMPo0dGgdf3VmJplR0Bmxzn6PFh3pMCuMaR6ImBe8GiAXrNJkTkrc9cTh9RWM9CQGjJ2IAJEoC8EXAcatLjaE30pl+x7eRUy7k3W1mUrU0gOGRx1JLkKPtKU1knqaL09m+U+YMisBnuAZpQkF8EPOnlFqjYpFASQxbA4llF83S3Y8GN7NGjVUPSnnmkQMIWib4/P06vxyXtPGvgq09YUijwY/tcMF7137l4Z04ozhIGG4rClZCJQOwRcBxpwQRNsHZeHxEJWRJ7xk/aVS2GrPmyjQnLdSH4Nu/XBbJO6WW7X3s6shhPtyqW0dAiYhxhd/6upuVgWRQoFAdRnWB7BhhHdgg0j9LtQ9KeeaRAwxaJPi+89+lJj5jS92bZKCJgdsLT4uM5DbgXPUCXrLNvCQINlQCmuPARaMNTPwfqg9Tswoozmb3yQHCHgOtCg+9PrRS0HyUOQ8WIOASV1lcOhp97k5ixpQI+GMQ9mo2H7xR4pFagqplYDzluZPlADKqK24F4mbWDdUm+iihhVCzM0iwHBhpHdgg3fINiAbWRJ1URAUPxTxoA/Ai9bTRtp1cAIyGD4/wTwWPDnYGak9Q4aAw2948JvA0BAK6YfB94K/AD4fDBuACSHCLgONGDZhC4pyEOmEjy2UPSd9G2+CTSs57umxehn/KRvgCcuRn5dpHbuQHFqXSz21075fXxO/9FfHalZbwigPsN6CDaM6RZs+ApLK5borS2/qwIC8kucq1+AR4K3rYJFtCErArISjoG3wPriR7Nqp8kqqaL9GGioqGPrYBbe/ET6kIH1ctEGYL2pnw4muUPAdaABhSB16UMekmsh47M8EsrpK/NBT72xHVrOeL6NInvG9nNNdG7XmKDNj8BzxtyiKCAHAmb9793wg1Y2/0UOQezqAAEEFjZDsKGtK9jQ+jm+W8SBKhyyFARkLpynL4D1PozlZ8xEKgV2LweRyeH/O+Nj4VN8ru2lmm6UYqDBDe4c1QICl0AG0sej98EaQdT/bw0muUPAdaDhMVzg/5vPfPkXZOChy3cy60W1INEFvmtajH76UCw/gc8oRn6dpGr6r5ksM1Dr3O3muEagUz4AT+tcHSqQCgEEFrZCsGFsM9jQFrXkzLBLNTwbl46AecC8Ib5+3obPKUtXgQN6hIAGGORd8B0eKeVaFQYaXHuA42dGQCdhfwAvD9a/9wIzjRogOCTXgYZHcYF/Jp/98jfI0DcUg/LJKaO31pIQBFfqSmaLqffqar1du01Bq5E4nmaxK5fS0iMga8EPGkS7JX1f9nCNAIIN2yHY8JMGGxBoyHk/cm0Nxx8YAZOJdDTO13HgV8HINiTVFwFTu6GlvvZPYDkDDRNAwi+IABHIioDrQMMjuMk/m1X5jn6iEwYNNEyXT04ZveUa6Pl1GSP5OYYcFvtqKT/1C0krAYZmonxWSFpXV1dTZEyvQ7+pro3VtWxUNGhNBBz+ioq181bXSlo2PgKyBc5XzIH0nqzBQhIRGAgBzYCR7WLWzOwqEgMNVfRqBW1aETa90Qe/hO/vA+8EJrlFwHWg4WFcsJ/LB4HoHsk6wZ8nn5wyencGRbCMoI4kmMQbXx1bR+vt2yw3Ak/dzWM2+7IpMR0CWuRUs5WMP36Wri9bEwEi4AYBWQLnLLLszE4EB7nRgaOGgYAsjuNEl8l9D/4GPAK8Yxi6p9KSgYZUcHU15vZTXViU8Rf2GI++AiOdNLoZfDJYi/7NBMZb7OhJ8Nlg7joBEGpMSF2M8p6buOgbmjr+9PljWKzcoj4rWZxujXch+2Xw5sWNUSvJx8JaTfnkrgfO3d7Qe932YCxnia7DBBTFxkhEgAj4jUBD70fLgXVJI7LD5EIw0+j9dlqB2ukSXM106ZU0G1VrOSwJHgLeA3wZ2q+GTxIRIAIlI6AXbkT8IqQZjUe7439PxN9sic8HxvuV/ykbgf9hQGVHJA/iIv1ivsFN+qNmNARwsZfFoKfqun8+m0PuLcfFGHBdrBU3ml1XxgDTua2Io5CcCAiCaOYcvzKnIHYnAkSgNATMOv1/xucuXoTJrKUNzYE8QsAUh0TQWHQ3oTkHVkzuQbtLB24XVAtmNATlrvoqq2927u3F/IXwHdKNDOmDxgfx3/xwg4DrQAMCTaJLaXKQrAkZ+vC+aQ4hJXU1k5k26HpOSQN6OIzgbYDx1+EeKhegSoJrqkn7vTRA5Suqsu6sYo7xvStqIM0iAhVFQFCkXDRwOxy8akWNpFn9IiArwfevgXVpxIZ9NzU7iSFLVS7pu02QvzDQEKTb6qf07DBZU0nXBzdT46fF35eDHwYrHQq+2vzFf1wh4DrQcD8u0i/nM16WgQwNNOyUT05ZveUV6Dq0rNH8HEfeBAa6lIpkBQG5CHiOBS9iRRyF5ETABBT1regoMIp2kogAEQgHAVkB5+1H4HbwIeHoTU3tISCt8P2xYH2W6YVMgF8zcvUYWbmrgdZLMnPSRtd3wf3FQENwLquvwnqBxuQ3+hL8LFgDD5+BdTK8NhgnqAlE4IPkCAHXgYb7cFHGg3ce0q2pTKDhwDxSyusrun5bz4kak5wU+yxBamKNYUpsuqZ4miKEKA5J8gMB4xOs6ZW3wFP5oRO1yIFAA1tgHtEWtZ6HIhxz5JDDrkEgIDPivNUHSX2JcQ2YNVeC8JttJc1WqMvD/8hOk3+A/wPWIJQeF5r1sHHHiDIEf2O5nAku6++Y48kmtrUpSR4DDSUBzWHsILAQxGhdBqzLNrtMTBaLnQWfc8V/88MdAq4DDVheo/tY5yGZHjL0ov+nPFLK61v3nScUaVk29plmNZGsICBnxpgCW5IfCGjardmClAEgPxySWYvRUeuG7VErXl+2CoINH2CrFwS4SdVGwOwk87f4uvoyPuevtr20bnwEZA34XP0+DowlvnID+GQwnmm6Z6rJFPj/C+B3wEt3yNBtjk3wP8SgJAMN4x8I/F8gCEwDPScORNc6qfk/GKvsiEx0GFHhPGTSlDXQcFoeKeX1lS2hq+qLm1idyWwp9nidEbBru3kDh3NZ7rcrl9LyIdCZvYNteEmhItAWDV4OGQ3jmsEGfH6M4MOCodpDvdMgIL/GdVWvrd+BQ31LncZgtjUIyLHwN7KyTWbLTH2DIpfFx0a3QJSZlyKDO8jtLxlo6NvZ/MVjBJ6Ebot5rF9dVdMgg7IjMhV7UUwnL8mPuKBfkFdKOf1lYeiqgYYDyhnP11FE3xTpm4JZfdUwPL3kmPjYWi883auqsXkr+jD80gZeuapW1sEuBBv26xFs+GxM1MK6KHVwfiSL4vx9A6z3rL+AJ6qF2bU3Un4OX2s2gy6D23lCOGQVfK/zua3G/023SJWR4G6BqWDqNzDQML4z+b9AEGCgwU9HuQ403IULMW7eeUk+hZzr80opp7/Zp3kM9D23nPF8HUVWBAYMuFh1j0nhHA5cnwc3rIqmsBwIyMzwh16jPgbPmEMQuzpGAMGGPRBs+KlbZsPnCDYs4VgtDl8KAlprRW4B631L5y5a4JxUeQRMdsKf4W+dt90NnrPLZHkI/0etsZ4ku8TtkQkRXP0GBhp6upP/DwIBBhr8dJPrQMOduAi/mR8aQVaELsMIhUyE/JFQtC1GT1NkSYsm4UZNsoeA7A9MdSK8oz2ZlJQfAfPmq73jeNcsB1KoCCDYsPP4wYaWr/DdUqHaQ73TIGDuW3/EefwT+F0w/Z4GvqDbCrKyBQ/hugWqktl5Qu+1y3X8v/mvTIrvdG5zNjjE+g0MNDRdyc+gEGCgwU93uQ403IELMaqy5yVz8deLYyBkqlgjFa/uJGfB/zph41tea4eCyZhB8E7eB7daE0tBFhDQrfJMEOhUC8IowiECyGLYDsGGsV2ZDS3fINjQ44HDoYIcumAEZF2cy1+BR4F3K3gwivcGAV0y08wWlC3w9+cTqibn4HvMrWUWcIj1GxhomNCp/CYABBho8NNJrgMNt+NC/HZ+aEyth9fzyylLgu6QYR44cCOqM8kvYxz2qTMK9m03xcv0Tcvv7MumxHwImO1t1Tc91vTmk8re5SOAYMNWCDa0dws2jGiPBq1aviYc0Q0CMhfO46fje9jF+JzMjR4c1Q0CsgF83gaeu2t8zSQ0c7ud8blK/HePa31v9Ru6JHjwFwMNHjiBKqRHgIGG9JiV0cN1oOHfuBC/m99QuRZyhueXU5YE2Ty+AeGtSJ3JvB34BFg8WGcUirFdngCu34KHFCOfUrMhIJPDJy+DfwBzbX82EL3phWDD5gg2jOkWbBg5Ohq0njcKUpGCEdCsMfMGW4OHel4vXPCAFO8NAqZ2wwvwOZbtysbgZoHrv3aomKR+Q9MYrwpFMtDQdAs/g0JgM2jLwjn+ucx1oOFWXJzfyw+LnAc5o/PLKUuCzAd9dWLy27JG9HeczuUTKJpEsodAZ7HNf9iTSUl2EDDn/zc4/98B875oB1RnUjSwgGDDyG7BhjGjokFrOVOIAztAQLbGuTwCrAHEHRwowCGdICDTwd8ngZ8Ba4HQtTvUSFK/QVt6WSiSgQYnxxIHJQLVRMB1oEErOL+fH1o5GXL0wR1vF0IgU1BKJySXhKBtsTrKL2LfoYghyS4CphaIFiBcyK5cSsuPgKwHv2h9En0bxq3y8gPqVIIumUCwAUsnWqWDW25wqhAHd4CALIBzWd9w61zkAvAkDpTgkF4gkKh+wxTx8aIB56U71Jbf4G+8NJM5HJrBQIND8Dl0cgRWRNM3+mDsSRvdB94JTHKLgOtAw824oH6QHwL5A+TozT2gt+LyFPRFFLzuZIIuHwKLx+qOhH37de2ombTcZl82JeZHQA6Hf/S6dUp+WZTgGgEUg1weQYbPEHAYiyUV27rWh+O7QECDC3J+fF5r0AHBB1L9EBiofoMi4m2hSAYa6nfABmkx0omix8GvgZFWFGlV3ivA34HPBv8F/D2Y1XoBgkNyHWi4CRdbPGTmJdkvvrEH9OZWLoTOI8F8m2ketGQcsJg775HA/j0R6Mz2WaPnL/y/Dwh0FofcxgdtqEM+BN6KolYURpkmnxT2Dh8B2R73M8yvzHIKntvhOzSlBQPWb/C5UCQDDSm9zeZuENBtnrAGNZqyx/C74/8oUmZoS/z7QPw3P9wg4DrQcCNuxNhvOC/JdpCjbwZXyCupvP5ycKzzguWN6etIsmSMBTJTSHYRkKmA7Rfg58EMatkF14I0rVQvL4J/BP/MgkCKIAJEwAsEtDCkKRCpc5N/glu9UItKlIRAX/UbdPg0hSJLUrdrGAYaurDgXx4jgGhudG8v+ukb5xHx9yiIF1lIm+9lFH6VFAHXgQasY5WPkyrbdzuTpqY38/X6buPbL4JiYSY48ivfNHOjj7wCPMAk+wh0Zvzsal82JeZHQObBsf81+F3wkPzyKIEIEAE/EJBJcU5fBNb5iS6l4IsFPxzjUIukhSKdqchAgzPoOXAaBGZH45/A64Obb9G0uvbl4IfBSoeCrzZ/8R9XCLgONFyHG++n+Y2XleIbeUApijJjrPMx+e2vgoTOOhvIbiDZRUAmxrGGZWzyCRjbK5L8Q0DWgW/GglG/SP1FIgJEoDoI6E4UZikF5lzC+mTVcWwGS5IUiswg1l4XBhrsYUlJBSNwCORj4hR9CX4WrIGHz8CLgHULmHawBiJI7hBwHWi4FjddPSZykuCYMm8M9skpqOTu8jn0xvIREnCYG6x1Gk4hGkUgIBsCW32rdmwR0inTBgLyu9hHZ9qQRhlEgAj4hIBmM5glbHodvgTMoK9P7ilNlySFIktTpreBGGjoDRV+5y0CC0EzrctwHFijuFiPamgW/DtX/Dc/3CHgOtBwDW62w/ObLziezEPUEflllSlB7ofeb5Q5ot9j6c4TWhxUd6Ig2UdAsJzN1AKYzb5sSrSDgCDrz1zL9rIjj1J8QwC7UyzXFrUehDWkQ3zTjfoUjYDWaRAURDfn+DB8LlX0iJTvGwIDFYrsrq8ciWNE50TNZ6fuPxb1NwMNRSFLuYUg8EdI3a4QyRRqAwHXgQYsndG3+nnJrIPUtwQn5ZVUbn85HToj06fUm0i5JqYaTfYFFurHNVJ1Y+OECMgSwFbT869M2IHNSkfAPIg8AR8h40/ruJCqhAAqfs6C7S/HYBtMQbDh3dFRK95yk+qHgKA2k3wDHg3WwtAMrtfqIOivUGR3IGQZHBungctcTsdAQ3cX8G/vEdgSGo4ClxmN8x4UjxR0HWjAA4/o0hoLZG7Y51kQVKII2QX264P18iUO6vFQgjouxo+XeKxk4KrJOcBYl6isFLghFVZfZoB/3gN/C164wobWzjQ8Vc6HQMM4DTTE/CUyHFasHRA0GAjIHOCHwToHuBOMuk0kIuAcAQYanLuACqRBYHE0xkU0mj9NJ7YtDQHXgYYrcHh8ZcdarfUg19qRVZYUWRo66ySDadKdkGvNClM0i8HJTkxs/qG7Gpg3ac/gcyKbkinLJgKyKPzzPfhtMHxGqgoCCCwcOH6woWUkMhs2rYp9tCMNAnoNlj+DNdNM5zDrpunNtkSgAAQYaCgAVIosDoE/QTQuntGg4oag5BwIuA406HpkbOtmg0TXO/7HhqTyZJg0aU2RxppNUgcCggm3Cb7sQESKQkD2jzHet6gRKNcGAvrQYZZQPILPFhsSKcMPBMZELdsg2DC6K7OhZSwCEDwf/XCPAy1kZZzj74M120yXVLY6UIJDEgFFgIEGHgdBIYCHv+isoDSul7KuAw2X4YaKdYo2SJ6ArKdsSCpXhrwMvR8td0yfRzOFkrCcJrSgkc+Y9tTNvEV7DhgjyMe35T3R8ev/sh98pFlPCMqSqoRAezRoNQQbvu0KNrQKAhAnVMlG2pIGAZka5/nV8fn+Ij4XS9ObbYmAJQQYaLAEJMUUj4AWt8Hb2mir4ofiCBkRcB1ouBQ3U6xDtkFyF2S9aUNSuTK0MJ98V+6Yvo8m/wdMtEjmrL5rGq5+WqPBvD27IFwb6qK5YLtLE2zQDEFShRBAYGFxBBo+6h5saItaLsW6psEVMvP/s3ce4JIU1dvvheVecpAgCMJKFkEREJE/OUhQRCVIFFCSAQXJGckSFATMgoiBaAQJkhSBDwElSEaysuQgYXeBPd/v1PbcnTt3bpzu6eru9zzPe3ump7vq1Ft1u6vfPlWlooyKAduB/3cfNsX8ZrbXqE7VwWKgcwYkNHTOoVLoIgP+hvmULuanrEbHQNFCw9nTbqijc7r90fZL0spoYsn2OeSz1w7Ab39jOSGf9MuYqq2ScrJ/Gb0vj8/m/38u6HykPD7X0dMQgXIp9eRh1VvXkYEql/mNJFkYceGuZrGBz1dyM5ujyuVW2YZiwPsDHukY+gaXs333UEfrNzGQIQMSGjIkU0nlz8BSZOET3Wl8af5cjyWHooWGn3IDZTnxLCzMpu8RNCUz2yjtTGxeMsdzdtfuhReGlcjyY8BnOQ8rG/ydLZOSyeJlwGanjjyc2t9yfjReP+XZWBhg/OBciAvXNosNiA//eC1JFhxLejqnCgz4koZGFFOYp+VZtptVoVQqQ/QMSGiIvork4GAMuNjAm8rEh1TsB76efmYjK4iBooWGn3DzdB8yMGNsa1D/6ZCXyYyOZPD7iDJ5nb+vdlDKy4fzz6vOOdhXU55dEJZFzUBYDs9npp8IFo3aVTk3agbu4YUM4sKvW8SGB59MkllGnZhOqBADvvy1PZRep7/PdtYKFU5FiY8BCQ3x1Yk8GiEDJ3DcN8GW4BrwQ7ALkBXHQNFCw4+5afLSJgszxKvwwL5IFql1Nw17Bt8v7m6esedm1GMI62d8uiw/BsJbszvg+nkwb375KOVsGPBhLka0vd0F5swmTaUSEQPjmLfh5Gaxge8rROSfXCmEgRDR5BGgPszyPrByIW4o0zowIKGhDrVc0TJeTbkIBUuYVTfZBPjN89tAVhwDRQsNiE3eac7C7IvpTbiEnTK7Ct8fzoKFaqURePEVKDT0KteKDUur+fh/OrKy+BmwLagrr69r9b8Rf22NxUOWuvzSlKT3CZ8YkvM9ClQmBmAg/O+7KMwwUTsSaOl4tYusGZDQkDWjSq9rDJxDTgeAx8DcwL9vDWTFMVC00PADbpSMOc7C7DOk5Wr/2lmk1t007Fv47Q8OekPZj3jbJq1THqxk+TJgrD4R2uCa+eaj1LNhwBh6GK5357PVg2g2pCoVMVACBmwh/ucvS///b2O7XAmclovlYUBCQ3nqSp62MDAP3w8GqwL/7GOCPcJBVhwDRQsNPt5wUjbFt3XSG++ns0mvm6n0PVCv1c1c48/LZqZOmSfNO1WyfBkwrslhCA9DxW2mfPNS6tkwEARKF1e/k016SkUMiIHyMGD0oX0ybe9D2b5AE/qWp/Ji9lRCQ8y1I9/EQMkYKFpo+B43x8nZcGYfIi3vdJdw3g9bJvXdJ0iV9WPAzoSbt8HC/XbrSw4MhPXb/X/okBwSV5KZM+CRDHYe8DpjjhqZGBAD9WLAJvC/f316DbiB7eL1Kr9KmwMDEhpyIFVJZs/AaiT5wCBgEqvkz2BHICuWgaKFBn+IZKxhFhZmZPcON8p+2czfRPikmPazsnmev7+2Erzo4Td/otMcjLl0wmSD6rB2jfNOMvLoE7sS+NCr7TtJSeeKATFQRgaC4Lh3et32fsSeZSyFfI6GAQkN0VSFHBmKgXfx443gXuCrTfhbZt68JC8DHi6To8EroIRvn/G6Ola00HAGN0XeVmdhYVZmfyBlmcsymt2E73eW0fP8fbY74Oah/PNRDvC8FPBQ3CvERlkYCNe+26mzKWDDsngtPzNhYBwXxt5MUlIiJWcgREbewjXA+0EuPi5c8gLJ/WIYkNBQDO/KdZQMsARXwtjqZI6W877Adx6ogm3B32vSz9oUw0DRQsN3uRm+k13RQ0f7rOzS62ZKht/Bf3UaB9BuX4Mb7zxpDosB3OSxI8xm7nx/Lo/UlWYeDNi7qa9/g1fBh/PIQWnGxQA37/lZmeKht5KeSSyH+Y24vJM3xTAQlis+lGuAi44vgR2K8UO5lpgBCQ0lrrw6ub4dhb2qTYEZi57QEQq2BH8fTz9rUwwDRQsNp3MjJOQ3KwuT2f06q9S6m47tChf+cLdSd/MtQ242L7xMBj8rg7fl99EQu+x+8DSYq/zlqUsJbEnq61kwEbyvLqWuazknJz1bIjAw9nAaEB1+zBIEDKWRiQFbkWvAXcD7FL8FCJEyMTAiBiQ0jIgmHVQ0A4vggL+p3hgw/jzYPPz9OfjLtK/JPmx/lX7WphgGihYavjPtRphV4cPDUUlDvm3ltFPwxazYqFY6dgH8+PjT1iipahUzmtLYuml7ZMJWWXkYMKIJw//Jg2znK4/f8nS0DLySJO9CXHi4ITSk22t9/2jT0vFVZMB6uAYcDxieas+DbapYSpUpcwYkNGROqRLMiwEmp0l8/P1zgPGjQXjgDVnyfrABeAu4ECErjoGihYZvc/NzxX1cNhTYzaTFGMUyWniLzP+Er7IgG8iAbZS2FSI/ZN1hwM6Fc59kcPXu5KdcsmHANqHO/FrCtdBmyyZNpRIjA4SHzofY8LdmscHFB6Idlo/RX/lUBANBfLyHa4H3tS4Bim4oohrKk6eEhvLUlTyFgWWAz8twIvg8mBW4LQQWC5/0p0gGihYaTk1vfo2olw65sMtI76EOEynwdJ8M0nwSVdkABsLKHE/AT0mFpAEFKsGOMGTFQ/F9GMXMJXBYLvYxYDtRZ/5g4ZPC8WZTVlUGuOH1Tkl6ft4sNjBvw/8QGz5b1TKrXKNlILzIoB8eohteYLvjaFPQ8bVhQEJDbaq6WgXlTXPygWoVqRKlKVpoOJkbnneGx2fDZlhTnptoWc3OgYvXwYxlLUG+ftvhaXthmImsOwzYVinnJ3UnP+WSHQO2X1p3F+makh2rsaaEuHAgeGe64NAzFbHhaPzNKGIw1pLLr5EzEKIb7k6vC/5ixoc5y8RAMwMSGprZ0OfSMCChIc6qKlpo4OElCA0ZTWBlp5Mec4P42+8ymn015UNhr22rzxaEnyngp21/1s6cGLCL4dzH+a6aUwZKNjcG7DjqzcVcFzH1wJkbz3EkPCnp3QSx4aXpYoNPFNnzB9T3OePwUF4Uz4DR37KjgN9LmdLD9gC6NhRfMbF4IKEhlpqQH6NiQELDqOjq2sFFCw3f4gbnneCMQnvtiDS9kk6GFd42OB+aEHLQf4EwKeQbcDTPoIfoh4wZsAXg+3lwD+jNOHEllzsDdgb15tcVtrKqM4DYsBRDKe5tFhv8O/uXrnrZVb7RMGArcE24Nb02XMfWV4KTiQEJDWoDpWRAQkOc1Va00HBCepObORt6+iICWOatjBZmiZ4EJz8so/fd8dnWTtvMPt3JT7lMY8C2TXlnFnNZuRjwt5UhosHFBtVfuSpvTN4+lyRzEMnw+2axge8vvpkkE8aUoE6qKAM+TNMOADQNcwF/f6ChmxWt7REWS0LDCInSYXExIKEhrvpoeFO00ECnN7xpm6XhUGdb2y5Nb9XO0inybOMib3cU6UH8eYc36w/Ak8I9u1pZYT12H0KxSlezVWYZMBAeKC5Mr48HZ5CgkoifgXHM0fBNBIapDcFhSjKTT8otEwMtDBjRLnZ9en24ne2HWw7Q1/owIKGhPnVdqZJKaIizOosWGhrjh2fNhh7bOL1Rsi2rhXkm/GEuI07KysNQfttX0nrecKij9FvWDIQ5MhjubXeDnqxTV3p5MxDGZvsEcB7ZwHwwsjowgNjwGZa8fATc8HKSaMhZHSp9TGUMkU+7cW2gmYTlcX1oa0YvgcbkkE4qhgEJDcXwXrtcB3tT6J3LmcfAxuacoxvcGIjL+ZSihYZjuJF5p3f2bMrpk9WF9IhsKKv1RWWsVdYS5O+3MbGZ0XbtN/nnpRz6M2A7pP9j/O/KyseAL1Nq14GpYOfy+S+PxYAYyJcBW4hrwyXA+2asnmrr5ZufUo+MAQkNkVVI1dxZmwLdByaDv4IVQbN555Lwy2HtoxzxfbDasEfqgCIZKFpoYOmtcDObIxsSjLkZQnolflvXVwaWppMNzoD9gLr2yI9FBj9Gv+TDgP0R3t8CCq/Nh+CcU3VhNwzR8v+fLXPOTMmLATFQSgbsM1wf/gtccGClJ03AXMpqHL3TEhpGz1k4o6TL3Y2xtGM/zcWBG8GO4HngDW4sauaTnOeh31cDFy4OAgsDmRhoZoAbWLDBImiajx3J5xfTg0q66oR7P+5h/hCenrhYJxucge/x04xg98EP0S85MbAH6b4GzqHzOVNOeSjZ3BgY53W3CWAVkeRX1OGmuWWlhMWAGCgpA+N+i+PvBz8Cu4B7uVZsxVYmBsSAGBgTAx698EDLmV/i+6vgI+n+kUY0NJKZjQ8uWvwZsGZvcgXYFhC+KYuAgaIjGo7kxuVq+dzZcGEIivYOOC2b9IpKxf5EGZ4oKvfy5Gs3wNNE4EO6ZF1lwHaGd//fPbqr2SqzDBkIy5Zyzw8zzm+QYcJKSgyIgUoxEFZ78muFX/MvBRMqVTwVppkBRTQ0s6HPmTLgkQsezdBqh7GD1ZKSpcBohYZGWjwAJp8GjwMuVCFa4ni2EhwgoUArWmg4Ir1xZTh/h/lkdecVyGkGWVuDF0UBDcmmbZ22HxczZV1nwP4A/z6EYtWuZ60MM2LAhx7Zv8HrYJ2MElUyJWTAl8T0VSmYPHK5Erovl3NnIMzvwjOATU6vF0QqK6Itd9q7n4GEhu5zXpscZ6SkLih4p71VADiDfY+Ai8BI5mjgsGAf4u9J4CnwEvChGd4p9QiJG8AFQFYcA0ULDYhYQSGfNzsK7EHSJCKgzGa8XQy8aPz0kNVo4+HpSXDbkIfpx5wYsHfDPfcMux/MklMmSjZ3BmxR6u8xwJAKf3MpqyMDLIP5h2nLYPZMYYWKr9WRA5V5JAzYslwnrgUe3XAPWHMkZ+mY0jAgoaE0VVVOR7fH7UfBmS3u+xj6s4BHI4xEaFiG4+4G74CrQLvhEp9kPw8JsgIZKFpoODS9Wc2XHQfGUqp2S3bpFZFSWFWB/x37dhG5lytP87cq3uH5v3L5XRVvbYuU/9OrUqJ6lsMmUI+PAUU21LMBMLa157ZpQkMvYUq9xvcLnk0SJg6ViYF2DBgvJe0ZMBWcAzLsx7XLT/u6xICEhi4RXedsPLJhsUEIWIP92wzyW/Nun+/Bh1y8t3lny+f5+b5kyz597S4DRQsNh3Bz8ofEBbIrdhg/+HB26RWVkt0FL4gmsqEZsHnh6Q0wEgF06KT06xgZ8KFKobO54RgT0GlRMNBPbFg3CpfkRNcYYNjEhxEYnmwRG+5jKMXyXXNCGZWMAV+Jwn6YXv+fZ/tF4C8mZeVlQEJDeeuudp731q7E5Stw0UJD4200IdhZmZ3Ljc6H6ZTcws17EmXR/9GwNWk/giefK2AoYXPYVHTAWBnwyVztcfAfgPAjKy8DxksGexR4ZIPEhvJW5Jg8p0MwP0LDn5vFBoZUvI4IscuYEtRJNWHAVuN6cQfwF0d/AxKnylvzEhrKW3e18XxWSnoK8IgGWdwMFC00HJjemBbMjiYfbhDernpkTonNdk65+ViJC9El171TEzo4J3QpQ2UzgAFbizrw4T6/GfCTdpSMgX5ig08SLasRA0clyQxEMRyNwDC1WXBgKMXP/jtt2fIasaGijpwBo89l3wD0K4PwfxLb2UZ+vo6MhAEJDZFURN3ceA8F9nkbHgSvAJ9sb1nQah9nxyMAVTM5CsjiZqBooWF/moor4N6+MjJrzPvgQ3NKbLZMyg03btnwDNg18OUrjmhSwuHJyukIYyWh8P+8a04ZKNmuMRAmiOReHoYlrd+1bJVRNAxMSsZvhNDwXIvYcI9WpYimiiJ1JKxkc0l6L3iC7RaROiq32jMgoaE9L9qbIwOzk/Y/gYsHDt5ahe3zbBtvonv4zDitvmP8+BWALG4GihYa9qPJuNCwcHY02Z5pmu2EsOyyyT0lH+cYHpx9pRfZsAzYp9J6323YQ3VATgz4Umd2K3gNLJVTJkq2awxIbOga1ZFmxOQ3C7MCxQ3NYkM6lGKnSF2WW9EwYJtyH2C+rNDHu0L3hGgqZjhHJDQMx5B+z5yBI0nRBYbLwErgXeBk4PvOAD7T7A3pd+5LCeHwyXggi5+BooWGfdOb0CLZUWVbpWlWYBUC43/OnsqOmyqnZDPA1b/B3VUuZfxls6WpAxca/g50H4i/wobx0Oc9Cf9XPuHqBsMcrJ8ryMB19OeIYjhh4FCK3u9VsLgqUqYM2MxcN3iGsDfBJHAMUNRhphxnnpiEhswpVYLDMfAnDngdzNV0IG9bQ5TDA2x/D1x08Mapt1iQUCIrWmjYh6ZD2/E3Z1mZMaY4pMkb7rJb3zCQDPkpOydD+W97p3WvUO+haMr9NyOqJPwPMpRCVn4G+okNG5e/PCrBWBiYlPRugtjwfHN0A0+OS4wlLZ1TNwZsce4J/uLE+3uPgs/UjYESlVdCQ4kqqyqueqNr95bwZ+x3gcHxLTAjkJWLgaKFhsaD4YTsaLMP0iT9ZvaF7NIsKiVbJy3LtkV5UK58bU74Yg4Zc3FUVigD5mN0p4INC3VDmWfEQBh3zRxNNhlUQMTNiJaaJUNYyyIMpfibiw1sH+aJceaaUaDidsSAbc71w+d+8T7aVWDZjpLTyXkwIKEhD1aV5pAMsGRN8rc2RxACFUSGH7X5TbvKwUDRQsPX0hvO+7KjyyeWDDexA7JLs6iUjBVc6M8ldlZRHpQvX2PFm/CAu1z5fK+Sx2HJy8eoi4kgw+Vrq8RR2cpiC1GX9wBfSlbiZ9mqLzt/x7Hc5Yf5x9aKAtlxWqOUwnCKI7iGoFuF/s2v2M5fIwJiL6qEhthrqIL+DSY0HElZUSWTVStY5roUqWihYS+akCvbhNVlZdabpnlSVikWm47dQnnuLNaHMuUeJrDzB6GflMnravpqH6MevC78zZUPt5OVngF/ILDbAZNC2+6lL44KIAbEQEEMhGV0f8d1xPuAFxbkhLIdyICEhoGcjGjPDCM6SgeNhYGXxnKSzhEDMEBodbAM/z/HEdqbMBldMu+0pEv/16OJludG3DxHSukLlV8Bxj1B2r5Sxw5w1lgVJ7/slPIQDIy7mR8PBz58wicJlpWegXHPUYT1wE3gh/yP7V/6IqkAYkAMFMDAuMeTZNynyZjhFAnz+sjEQLkZyPBBptxE5OC9RzXIxMBYGGi0nazfdvrSq74aShXMhQa/fq1ehcJ0qQwezUJkS8Jko7KCGfD5e4hoSBhq5xEOsvIzMI55UJKNwJWA/zU7tvxlUgnEgBgohoFxf0Bw8GvKGMyHl8rEQBwMjI/DjdJ6sTCet76RWiMtjYdPvtCmZP6AdGOb/dolBhoM5BDREJKuktDQ+B/y/7fLG8RpOxQD4xjuZazbnezJ9vixd2KGykO/jYyBcR4W+3mO9SF4v+bzitTHyyM7V0fFy8A4H1/tk0L+ChzKZ4+4Ys4dr2+ZGEiSV4kqnCXp+RI3+Qd6kykeZSYTAx0yEMTqJUnEoxW3Asz/40NvxzGUSyYGxEBZGfAOonceRosjy1rgGvld9BwNe9CsaFdZzzxsPJDbA9WpRy+L/aU65elGSWzttG0d0o3clMdwDNgG1IeP62c1Cll1GDBWm7Jz0v+1n7HV6lPVqdyOSjIl6Tl/+lKYPb/hlfW7OkpQJ9eAgXA9OZHryCIDC2vzsP9e8Ci4B3jfEXFTljEDmqNhjIQqomGMxHGah76OJQydSexkYmBIBvKKaPAImypNUurRQdtyU+1BuZ8yJKP6MWVgHMKM+Q3z62y/A29vipoiGRh3NfXg95KD2X6V+jizSG+Ud1YM+JvEsJQwL7A9oiGZg+9cq3SdyorhaqQz7jOzJr2rvpm8s+MsydvXVaNMKkUODCxGmlsCriV+3/bnj3F+bcHGvcSf5cLHxM5lS38o+f607/orBsSAGBADMTJQdETDrtxMXJX+QLbk2Gmk6R3giszNYrukPK2WLU9VT81Du0P7+nLVS1qO8hmCv90AJoOPlMNneTlyBuzo9P/tSrYaOz1y4ip5JE+H8xHRcOX0qIZelqDpeWdy0nMir6P9IVEmBtow4C9UbG/ACyP76cADfPhdWML6s/1/C8uBE8Fo1wPvA67Q/3d9GyEDimgYIVE6TAyIgeEZKFpo+CI3AxcaMr4h2GFpumOJxBmeta4fYUun5dmv61mXOkNfUjGEWD7KVlFtUdSlvYe6eBZ4nbwrCpfkRIYM2L7Uq1/TmVvGQ51lNWdgHELDNxAYJjcLDgyruBPB4YM150bFH5IBm5tryEIDDzGPjvMozyYLy1o/zn4fUsGcQHY6YLSOrdR0kD6OjAEJDSPjSUeJATEwAgaKFhoab+oz7nBYY+6H94+Ag5IcYhO5af6hJM5G5KbtCG/+4LNzRE7V3JUwX8Pb1Im/+a5I1FHNq7Rf8c0j1bx+7wY+kbSs5gxMSWZaEXHh3maxYZr40HPwhUmieT1q3j5GXnzblGuK38+bojvDvA63s+8OgDjhFl4yIEbYT6Z9199RMCChYRRk6VAxIAaGZqBooWEnbgR+01hxaDdH+6sRUhfSXWu0Z8Z7vNEfM8Yo6sFsdHUUOiEPwduDQB3a0ZGX49F2IPXh//vH5ZiJki6MgXANZl4UewJkPDSusEIp4w4YeDJJZpmS9J6BwDC1WXBAgLhpUtK7VAdJ69RaMBDu5f/ienJB/+KGYRZvsb+lH2knss9Xn5KNjgEJDaPjS0eLATEwBANFCw0e4uYPGx8ewscx/GQIDCHdLcZwcqSn2FfSMrXcTCN1Nyq3+ua42D4qt2rvjK9AEcbafrr2VFSSAFuT+n0RuEBaIdG3kpXVtUK9mYxfH8HhiWaxAfHhdaIeuMfJxMBgDNhuXEcmg8WnHxHm/fkP+749fV/jk6/UZT9sfOPzZuBqcC7YZvp+fWphQEJDCyH6KgbEwNgZKFpo2IELvgsNK4+9CO3ONIZMhHT3bPdrOffZ8mmZvl5O/4v0OnRGHoU/XxpLofpFVkW/vG0O6uM+4GNpl+n3k75UhAFbjrr1sdOTwFYVKZSK0SEDqE9zEclwbn+xodcQG5i3SSYGWhmw2bh+PA1aBAX7HPtcfGgZouVzM4Q+4Npsuefb+cAjrA4EPrTrVcDEkbI2DEhoaEOKdokBMTA2BooWGnjDHG4Gq4zN/cHOMiaBDOkeNtgR5dsfxhw+T7l+Uz7fY/DYdk/bxNYxeCMfGgzYstSLd/pcBJq9sVfbKjEQJgC9k/qdCvauUslUls4YYELITyM2PDNdcOg5orMUdXY1GbBZuHYcDN7Vv3x2Nvva9Ins9+z/57Rj7VA+TwH/N/3cvlUtxk/fp08pAxIa1BTEgBjIjIGihYZtufh7RMOqmZUoJBQUbF/e8vRs0y06Nb+h2nNgXNGelC//sGSWjxe/W/zFVnv2GerEH0Ivis0z+ZMVAzYn9XsN8Ov9KUDXsKyoLXk6dELmJ5LhdIZTnPVCktBOZGJgpAzYH7mWnNj/aNsyvc74xJFLAI+mOqrlmJXTY4iqk7UwIKGhhRB9FQNiYOwMcI9PHAWZj5MLHc+PZu9AWELvV9mnW2SKxrCJwBfDKGSjZ6BvnovPjv5cnZEvA3Z82rYPyDcfpV4cA0Hs+2Vaz79m21OcL8pZDIiB8jMQJnz0ORpSwcDn+wovY5iHwc3OBP8GLdea0Bd4cdox4bglOeYXwCMhDgcM1aitSWiobdWr4GIgewaKFhoIYw8Pzh/LvmhhPeU/Z59ukSn6rMqBry8X6UV587Ze+PNOyT/KW4aqeh6ikK6ibt4G61e1lCpXGAJ2EnXskQ3XgrnEiRgQA2JgbAzYAlxDGCLh9/SwMhd9WnMR0+/1RMeYf2/pL4V7ja9ChQjhZh5Z+xZwEdQ/3wRuATNN+712fyU01K7KVWAxkB8DRQsNTA4WOpyrZ1/EMOPwHdmnW2SK4Qb5Epy1LO9UpE9lyzuMzfSHnM3K5nn1/bV5qZfHgA8PWrT65a1zCW0v6tiHt90FFq4zEyq7GBADnTBgs3IN2RgcBJqG4domfPe5GZjfodnMJyH3oXpLA6JDw2SSJ08/Igy38IkjN5q+r1afJDTUqrpVWDGQLwNFCw1bcDH3h741si+mXUy6T2WfbtEphjGJE4v2orz5h0ml4M/+Xt4yVNnzMFu4d/JuBTNXuaQqm/n13+va507RcDA1iGEZmJT0LuUrU9Bx4U22TAxQ+9ikAABAAElEQVQMxYB9nusKwyaaLUwU/iz7z5m2127kMy+kWqMXwnCLuq6AIqGhucnosxgQAx0xULTQ4JPAudCwVkelaHuyfY90J7f9qdQ7bd+Us/eXuhiFOm/7pRx+slA3lPkgDNhOaf0wZlZWbQZsTeqasdJh5ZFNq11Wla4TBlCHZ3sr6Xlx2goVPS8iOHCdkImBwRjwZdND/3LjaUf4qkbGcFp7EswL1gbe/2wZumsLss8jHuo6hE9Cw2BNSvvFgBgYNQNFCw2bpxf6dUbt+bAn2FFp2nMPe2ipDuhbH/rLpXI7KmdDqCX9VrsdaPb7qOqm4Yyvlx46gRVaorZRNm37MxBCmH3MtM/P8fX+v+mbGJjGAGvgzofQ8Pb0pTB7GVjfewUhMYuKIzHQngE7jWuKRzD4RI+PgHtBGj0Vol6vG3iencAxRMO2RjkMPLKieyQ0VLRiVSwxUAQDRQsNn+Ji7oryutkX3icACmkzDq9KFuZp8DeAF1epVN0vS98KHkTVyOJjILTzS2nn/maJuVxk1WbA5qGerwV+P/gBGF/t8qp0Y2FgctKzPWLDK/3Fhp7/Ed2w11FJMsNY0tQ5VWfAVuF68iWwE5hxemntNb4z+WOzhcklX2b/Ic17a/ZZQkPNKlzFFQN5MlC00EDoeuhY5hCi5g8nIe0c5n/Is0pGkrb9lrL5hHl6Gz8Sutoe4+P/w1sLn4xOPLblqOidvmRZmCzwDbYfKdob5Z83A/4G0X4EXGy4GiA+yMRAfwa4GCyC2HBpf7Ghl1n/ev4fQkT6trr/OfomBvozEOZq8uvMyi37L2Dff8Bc/ffX6puEhlpVtworBvJloGih4RNc0P1iv0H2xewbf/fZ7NMuOsUwY7vz9qGiPSl3/uEth/PY8laj3KWqlve++oRNBP8Fi1SrbCpNewbsG9S1r0hxP1iq/THaW3cGpkU39D7XX3DomcL+Yx9KEpY3lImBwRgIy+w+zPXlp9OPCNcdj6BrWZEqvJT4PvvrEv0ooWF6o9AnMSAGOmSgaKFhEy7e/qD38Q7L0eZ0e3+a9p5tfiz5LvtAWrZ9S16Qgt0Pb1CZldq8w8HbVFmcDNhq1I+vTvBPMFucPsqrbBkwF6EZlm8vgHWyTVupVYUBGsh8RDL8or/YEKIbHngrGb92VcqpcuTBgC9fGa4xv2F7ffp594E5GfN8hZVxvK96G6DfWmmT0FDp6lXhxEB3GShaaNiYi7ZfvHNYrzjMKuxpH9FdSruVWwj7v7JbuVU3H9subYOaXDPqSvaok3Ct+B1bjcWOuq6ycs5WoK4fA1PArlmlqnSqx8CkZPxGU5LeR/sLDj1T2fcDngwlIlevyjMqkUfJ2TbgADD/4ImGqIa9OeYZ4P3KG8F6gx9f6l8kNJS6+uS8GIiLgaKFBiIZwkU7B4U4hMa9RfpnxEV5Vt7Y2ZTN3/LOklWK9UwntJM74HEi0NvyqBtB30oyJ0XtppzLkAF7N/+XNwPv3J8CJDJlyG6VkmJs1axMCnlq68oU7NulSuVUWYpkwPsIdhDwSCu/Jl0LVi/SoxzyltCQA6lKUgzUlYGihQbmZggXa8Jk8zB7mvQvyCPl4tO0z6Xc5RANUnzpuuuBh0KGdnhYd/NVbqNnwH6d1tUXRn+uzignA+Ft4i/Tev8j2znLWQ553Q0GEBZWZjjFPxvRDZOS3k27ka/yqBMDfg2yI8Er6XXpcrasblEJk9BQiWpUIcRAHAwULTSw2kR4wPtkPnTYnaR/XT5pF51qGBriE6adWrQn1cg/jNH0TsO81ShPVUsRHjrpCIVw+rWrWkqVqx0Ddjj17pO13QOWbHeE9okBZ4Cb/ngmhdySIRU5TDQtjsVAgwF7F9eiE4AvlWnAVwT7YOPXkm4lNJS04uS2GIiRgaKFhnXTi/On8iHH/kz69+aTdgyp2t8pH51uWecMhAkHvaNweudpKYV8GQjh9I9TVy+CZfPNS6nHxYDP/G7ct+wlkMOQu7hKK2/EgBgoAwO2ANejbwMfzupi6CVgxTJ43sZHCQ1tSNEuMSAGxsZA0UIDbySDCvzpsbk/3Fn2C9JnLF1Vzb6Z8rdYVUvY3XLZhfDpE88t1d18ldvoGbDlqaeXwSOAcfyy+jAQ6p4VDMMSmIeyHVefsqukYkAMxMuAvYfrES8r+gSH3/F5pXj9beuZhIa2tGinGBADY2GgaKFhLS7C/haZt1R5mA8rCOpyRWedto+m/O2ZB3v1S9MWh8/J4Df1K3sZS2w+9MqFoVvB7GUsgXweKwNhybnLqHe/f3i48hxjTUnniQHCY+YWC2IgOwZsIa5J3wFvAL9G/QGUZQ4HCQ3ZNQSlJAZqz0DRQsMa6UV4i3xqIixZ5Bf5hfNJv+hUfQZ2ew5wE5Nlw0CY2d7bzJrZpKdU8mXAdqSuPFT1SlBRQTFfBsuberj+HZPW/31slylvWeR5UQywBOaPfOJIJpC8mYkky/b2uSjalO+IGAjD/PyF1+vA+xWXglVHdGpxB0loKI575SwGKsdA0UIDywKFi+9W+TBrO6XpV7jzYOdRRp+IqDcfDuuWanhT+jx8+ltyhWSXovptf+rKO3E+VEp1Voo6y9JJ25x6fyUFn2ViYOQMsBzmpMYKFXx+B+HhDCZ/mWvkKehIMTAcA2EOB5Zl7ps08nI+f2y4swr6XUJDQcQrWzFQRQaKFhq40IYHhK3zIdc2TtOv8KRhtm1axo/nw2EdU7WvpZxuX8fSl7PMYZiUiw28PZLVjwGPZjCPavDolqMB0V4yMTA8A6xOcfx0oaHX0s8TiW7YmbMlXA5PoY4YMQM2H9emEwF979D3vZrtOiM+vTsHSmjoDs/KRQzUgoGihYbGHAPb5MO2fTi9mO+cT/oxpNq3zOVpMXhTDR88BN8eBI+DmatRpqqXwiMZ7JfAxYb9ql5ala8dAz5PQ5ivwduAz98wT7ujtE8MtDLwZjJ+XYZO3NcqOLDvJg2naGVL3ztnIPTbjuUa5RMa+/XqBrBR5+lmkoKEhkxoVCJiQAw4A0ULDR9JL7Lb5VMdYQZgv4gfmE/6saRqN1HGB2Lxphp+2KfTtnlwNcpTh1IEgehK6s3fau9QhxKrjK0MBMHJV6J4BzwCKjxsrrXs+t4JA/ckSQ9DJw4Cr/UXHMJwiu8xNuddnaSvc8XAQAaMITp2GPDhmt5X9SXLfShYkZE0EhoGVpT2iAExMEYGihYaVkkvrjk9FNh40veHDtY3rrKFG5XfpJaocim7Xzb7C5y+ClgjW1YOBnz1iTC/hq9GsXE5fJaX2TNgG1L/PlHum2DX7NNXilVlgGUC3ovYcGF/scGHVPQ8T3TD7kcliYblVLXyCytXuG/5XEMTgffl7gQMKS5kCJiEhsLagTIWA9VjoGihgbdN4aLKzPF5WehsElJdZfO3doHHvapcyu6XzZejCkLV97uft3IcOwM2P/XmQ19eA0RNyerJgL2X+qfTHK6NZ7OduZ48qNRjYYDhFOszdOLeVsGBfbdOSnq1wslYSNU5wzBgs3Cdoh9nT6bXrfvYMg9XV01CQ1fpVmZioNoMFC00rJheTHfKj2b7F3kw4U6VLYQLP005r6hyKYspW1jV4224/UAx+SvXsTFg76PO/H/C32q/f2xp6KzyM2A91P9ZwN8S3gGWLn+ZVIJuMXBbksyE0LAv0QyvtggOFe9TdIth5dOegXDd2p3rlQ//8v7HnO2Py2WvhIZcaFWiYqCeDBQtNHyQC6h3AHfJj34XGeye/NKPJWX7CeWcBAgdl2XHgC0Cp68D2pGsXAzYh6i3l8BTYEK5fJe32TJgzAMUZnr3oVDdfkOYbVGUWtcZ4AawEJEMv2iIDf65604owxoyEIb/Ltblgkto6DLhyk4MVJmBooWG5en0udDwxfxINjoE9kJ+6ceSsn0q5XKLWDyqjh92eMrtZ6tTprqUxFan7lwoehgsWJdSq5ztGAhLYPrYZ7/n/ABoKEU7mrRvUAaYo2Fl8Pn/Jsmsgx6kH8RAuRmQ0FDu+pP3YiAqBooWGpZLO3275ceKnZLmQQhtlc3o+BjzWNm5VS5lMWXzB5IQwvg4W3Uwi6mEDnK1j1Nvk8Fd4F0dJKRTS89AGAP9Y9qBiw0aSlH6+lQBxIAYyJgBCQ0ZE6rkxECdGShaaGDsdOjwMRYtL7N90zwWzSuHeNK1P1DW58GM8fhUFU/sk2k7OqEqJapXOYxolDDW9Ta2LCkmqzcDtj3tgPtfWFWGzzIxIAbEgBiAAQkNagZiQAxkxkDRQgMzNwehYc/MSjQgodCh9LdXqw74qXI7fBm3wOealStaFAWy38GvL5u4bBTuyIlRMhDG6b9D/d0M5hjlyTq8cgyEoRQe1eD3h5+B2StXRBWoMAZYmeJTDLPY49kkUbsqrBaU8RgYkNAwBtJ0ihgQA+0ZKFpoWCrt5H25vXtZ7LX10jyYw6Dq5mPQw3KMJ1e9pMWUzxaDXx/vf00x+SvXzhnwiWfD/8hf2WoYTOeEljwF66UdnAFcbHgAfLjkBZL7ETAwKRm/YWPiSLZPIzh84agkmSEC1+SCGBiOAQkNwzGk38WAGBgxA0ULDUukHbyvjtjjUR9oy6V57DHqU0t5Qlg3ng6zLB8G7JC0PWnm+nwI7kKqRgRVeLBkJRFNCNgFwkuQRZhMl0mDw1wee5fAYbkYMQOTk57PNAkN5p9ZqeIOBIgNInZbrokBZ0BCg9qBGBADmTFQtNCweNrh3yuzEg1IyCd/Cw8VRw74qZI77NC0vAxLkWXPQFjf+n44/i/o5trW2Rel1ikaD5PhuvAntj21pkKFTxkIS9n+JW0Xl7KdT9SIgTEyMO6tpOcw8Ear4MD3PyFEfGCM6eo0MZA3AxIa8mZY6YuBGjFQtNAwIe3UfT1fzsNbKpYzq4PZCimn+9WhtMWU0dZPOT6tmPyVazYM2EFpPf6W7fhs0lQq5WbAJ9I1ROkwceh/2K5b7vLI+yIZYBmoRYhkOA/BYWp/waHn7SlJ7w9fSxKGO8rEQFQMSGiIqjrkjBgoNwNFCw2sBBHeKu6TL43myxL+Lt88YkrdHqW8N8TkUfV8sV/D8dtgxeqVrU4lsqOoQx+ffyGQ2FCnqh+yrMaEuvYE8MlDjwczDXm4fhQDQzDAHA0rIyxc319s6GVIRc9rRDcc81ySaHLaIfjTT11lQEJDV+lWZmKg2gwULTQsQgfOO/n75ktzmLfglnzziCl1+w6cegf53TF5VS1fbCH4fRn4com8BZWVl4HwICmxobwVmJPnYdjdb/j/9rZxK9BwtJyYrkuy01ai6Ll/oODQ+yxihA8hHVcXLlTOaBmQ0BBt1cgxMVA+BooWGt6TduL2z5c6887ik/nmEVPq4W2cd453j8mr6vliTDAaHkK+Ub2y1a1EfWLDRdSpIhvqVv1Dlte+SJsgyt2IhDcmLjY9DA7Jl34cioHrkmQ8osKXERsmthEcdC8Zijz91g0GJDR0g2XlIQZqwkDRQgPjE8OD2oH58m1nks9boCbLS3k57RlwRb681j11f+CwvwJf8nJC3dkof/ntOOrRBbqLgULly1+hGZbAlqBN3Ji2j6vYLpxh4kqqhgw8mySzM3TiSPC/huDAMIoTa0iFihwXAxIa4qoPeSMGSs1A0ULDAmnH7eB8WexbkrBGQwnsh3A7BcydL7d1T92WheNJQKJOJZqCHUNdutjwe9BbiSKpEBkxECaK5F4VJhd+ie22GSWsZGrMAJ2wBRAYjifK4TsvJslcNaZCRY+DAQkNcdSDvBADlWCgaKFhPjpr3qlnScY8zXZJ86nRxH22UVrmHfJkVmk7A3aEuK5SS+irT8Qjm6VKJVNZsmDAJ4C1u9P/+QvYsoSyTAyIATFQCQYkNFSiGlUIMRAHA0ULDfOmnbXD8qWj76F703zziSl1D/02f+vG/BSyfBmwHnj+F2DycEM8k5WfAWPemCCCMqTaZi9/eVSCbBnwaBc7Bfiku/8BG2ebvlITA2JADBTCgISGQmhXpmKgmgwULTTMQwfNIxp4I5yn2QppPrvmmUt8adt5lNsnMJs1Pt+q5pF9DJ79oePnVStZfctjX6E+p4KbgEKa69sQhii5rU3beBT4fex7QNfaIdjST50zwBCLPZnX4fDXkmTBzlNTCmJgAAMSGgZQoh1iQAyMlYGihQY676GDdtRYCzCy86wROZGzoDEyb7p3lH0m5XeL7uVZ55zCpKP+wPHxOrNQrbKHFQdcQLoNcB2RiYFWBmwO2sbZwP/3HwSIjjIxkD0DzOWwZWPiSMSG1/n+rVeSREN3sqe6zilKaKhz7avsYiBjBooWGryD5p2zozMuV5vkwgRe32/zQ4V3+fjysCzbrytcyIiKFh44noTzR4HC7SOqmc5cse2oz7fBXYAJbGVioB0Dtjntg8UEQls5ka0mE21Hk/aNmQGEhc2nCw29LKXl6HkFHMG4PfpTMjHQMQMSGjqmUAmIATHQYKBooWE2OmMuNBzbcCi/rT1GPswkXzczRIYgNiiktytVb5umbZowall1GLDPUq++isv9YJHqlEslyZYBm5/2cTHw+5rP2/KRbNNXanVngKETeyAwPN1GcHgBweEQlC6J3HVvJJ2VX0JDZ/zpbDEgBpoYKFpo8Dfu3iE7vsmnnD6GcdaEP9fNwls251hLsXWt6vvCqDfqWpbKqAsM2Cf4P3oTPAaW7kKGyqK0DPj11l4AHglzMtDqJaWty/gc/2+SzIqocCBAXGhENvRtn/PfJiYJL3JkYmDUDEhoGDVlOkEMiIHBGChaaCC0NAgNJw7mYHb77SLyejq79MqSUlgRwTu8l5XF4/L7aXPC92PAZ6PX+NnyV2hTCcLkfwyLtmfAh5t+0Ecx0MKAvZs24vcdF3p97oa1Wg7QVzHQEQPc2OdkOMU3ERYYPtEnNKRDKnqfZd9+Lkp0lIlOrhsDEhrqVuMqrxjIkYGihQZfgtE7YSflWMY0aTuNfHxSt/H55xVbDsbcFPQ9Eg/rlXWHgfBA6u3tgu7kp1y6x4CtRL0SoWwvAz08do/4kuYUht0gcocVTM5iq7H0Ja3JWN3mQjQPgsOxCA6vthEcJr6ZjF83Vt/lV3QMSGiIrkrkkBgoLwNFCw0z0ulyoeGU/Cm0/dO8aji+2v4vLfte+fOsHKYzEEKmvX1vP32fPlWDAVuGen0c+FAK5m+QiYGhGLB5aCfnAr8eeLvZaKij9ZsYGAsDrybJvAgOJyA4vNYiOFw7lvR0Ti0ZkNBQy2pXocVAPgwULTSMSzte386neM2p2ufSvHjorpuZ8/wIuKVuJS+2vD7rfFip4CW27y3WF+WePQO2MPV6N/DIlS9nn75SrB4DtjFt5QnggsPPgIZWVa+SCy8RHbv5ERxOQnB4fZrg0HNo4U7JgbIwIKGhLDUlP8VACRgoWGhwhkKHi2ENeZt9NM2rpm+X7Zi0/EvlzbTSb2bAPgjvk8E1AMFHVi0GbG7q9S/AHxy7sHpOtdirZ2nCMrisShOGUvgQHJZPlYmB7Bl4MUnmQnBYNvuUlWKFGZDQUOHKVdHEQLcZiEFo8LeB382/4GFiLn8YOCz/vGLMwehshIehb8boXbV9sgNS7veudjnrWroQudJY0vCn1HUN54Gpa913Uu4wpO3e9NpwOdsJnaSmc8WAGBADGTAgoSEDEpWEGBAD0xiIQWh4iw7Wmd2pEHudvH7SnbxizMVY3tMejtGzavtkM8D7X4GP51+u2mWta+lCHXMdC2LepWxnrSsTKvdoGAirAh1Je/Gop9fANwBzF8nEQHcZSIdbbM6SOhrO013qY8tNQkNsNSJ/xECJGYhBaPAOFmGk3TDzt0eEsNfVjDfq4UFotboyUFy5/W2lvQr+AVhtRVZNBuyQ9H+MzprNV80yqlTZM2Dvp73ckLYdF4RXzD4PpSgG2jNwYZLMOCXpfTSdz+F/DLf4Fp3DBdofrb0VZ0BCQ8UrWMUTA91kIAahwd/y/qA7hbbLyOvf3ckrxlzC8JG34YA3r7LuM2C7wL0P3zmu+3krx+4xEOrZI7UeABO6l69yKjcDYdLePWgzLwNvPycBRcaUu1JL4f0LSTInE0e+3X+lip43EB/OoIO2WCkKISezYkBCQ1ZMKh0xIAaSGIQGH87wo+7UhT9ghw5cjUNTzccCPw96usO5cunPgP0W7n1ekrX679e3ajFgm1LHfm2bCD5SrbKpNPkyYAvRZi4BLko+BjbLNz+lLgaSZEoy0y4IDc/1Fxt66TD1vDUl6TmXKIflxFMtGJDQUItqViHFQHcYiEFowAdjArVuWBj/6p23Cd3ILc48+pb53DJO/6rulc1L+3sKPAk0FrbS1W2rUMcuNLjg8JlKF1WFy4EBFxiC0OD3rN+DCTlkoiTFQB8DXKxmQ2jYDzzdRnCYiujwWwSJVftO0IcqMiChoYq1qjKJgYIYiEFo8DDRc7pTfvs0eXmnbf3u5BdjLmGGfFa9sj/F6F09fLJ14d+jGohukFWbAVuMer4HTAUHVrusKl32DPjQCTsB+FxGb4DDQW/2+ShFMTCdgUeTZGYEhT0ZOvHvgYKDRzn0XjcpGb/x9DP0qUIMSGioUGWqKGKgaAZiEBr8offc7hBhHyQvFxp2605+seZiZ8CBz9WwcKweVt8vOzZti3tXv6x1L6HNRV1fkdY31zoNW6p7ixh9+W0Z2s2f0zb0ENtNRp+GzhADo2PgQiaJZMjEdgyduKud4IAQcYOLEqNLVUdHzoCEhsgrSO6JgTIxEIPQ4PMFnNcd0mz2tKPGG6I6m62U8nBwnVkotuy+hJ1dD6aAjxbri3LPn4FQ399N/+/+xnb+/PNUDtVjwLai7fiwKxfMfb6XxapXRpUoQgbGTUp6P4ngcGOr4PBmMp4IPVmFGJDQUKHKVFHEQNEMxCA0PEtn6ZfdI8KeIb8LupdfrDnZnfDwYKze1cOvMOnbROrhcaD5GmpR6bYnde0rCvAi0JavRZFVyIwZsNloO98CLlL6/B+HAE3umzHLSq49A28l49dgroY/gKkID/f6ihXtj9TekjIgoaGkFSe3xUCMDMQgNPiD1q+7R47dTH63di+/WHOyr8ODvxVbI1YP6+GXrUcd+HwNl4Jx9Shz3UsZ6tyHjL0KPlF3NlT+sTJg76f9XAP8Oo5obB8fa0o6TwyMloHnkmSOo5JkhtGep+OjZ0BCQ/RVJAfFQHkYiEFo+C8dpC5GGHj0hNHJr7vZfPDgE4ydXXcmii9/mODNHxYOKt4XedAdBmwp6vsB4CLTN7qTp3KpJgNhJaH/0I78GvIbMKGa5VSpxIAY6AIDEhq6QLKyEAN1YSAGocGX+ruoe4TbMWmHbO7u5RlrTnYxXNAGfO4KWXEMGG+F7ErgE3SuWZwfyrm7DNg81PfVwB8QfwJm6m7+yq06DIT5h06iDflwijfB0YAVK2RioFgGmFBkFiaN/DFDLX7n8zzgjSL3iq2S4XKX0DAcQ/pdDIiBETMQg9DwBB0i3sJ0y2wX8vOO/UrdyjHefGzTlAs4kRXLgE8OaC66+ZvJBYr1Rbl3jwEbT31/D/g16Xowb/fyVk7VYyCsTnF52p4eZ8vkkTIxUBwDLJO5a/MEkj6vA/t2dwGiOK+U8xAMSGgYghz9JAbEwOgYiEFoeJTO0O9G53YnR9taaSdsy05Sqca5YSZ8f7BlFnxZ8Qz4fBlhokAfdz1j8f7Ig+4xYHtR5x7R8gj4UPfyVU7VZMA2ox09DFzA+guQsF7Nio6+VOnkke80iw3TPvc8z9KZxzKb6ULRF6JeDkpoqFd9q7RiIFcGYhAa/k0n6A+5lrJf4vaetPN1cL/dtf1ix6Z8fKC2FERVcNsnrY9To3JLznSBAduQumcSd3sDfL4LGSqLSjNgvbQj5n0Jk476XCDnAD3UVbrO4ywcEQwfYejEJaCd4DCFKIfzOGaVOL2vnVcSGmpX5SqwGMiPgRiEhofo/FyWXxFbU/aZ/cOSYD9t/aWe330d9jAh3en1LH+MpbZfUSf+JnKbGL2TT3kyEP4fb0/r/yy2WrYwT7prkba9m3b0Y+BiA/d8OwwobL0WdR9XISclyZLM1/BdBIf/DYxw6GWCkZ4biXLY+rokYUiZrCAGJDQURLyyFQNVZCAGoeF+Oj2MKe2m2Z3k+ddu5hh3XvYn+HhJnc9YaskncbM7AFGl9sFYvJIf3WLAZqbeEUKD2OTL8S7crZyVT5UZ8CE5dm3arnxupB0BwrtMDHSXATobcyM07Ifo8Hg7wYF9TyI4bNtdr5RbyoCEBjUFMVACBmbDx4+A9cEiEfsbg9BwL52dK7vLkV1CniyrKZvGgG0OH/4GfScxEgsD9j7qw8Po/w1YnUBWPwZsd+qel4D2DFinfuVXifNhIFzvH6BN+TX/H2C9fPJRqmJgaAY8cgFBYSsEh78NFBx6ptBJ08opQ1OYx68SGvJgVWmKgVEyMB/H/wrs0HKeh3sRlpjQOUy4iffhGj7H+GYyBqHhX9D0Z/jpotm3yNM7WS4IycLEg2G1A96eyuJhwD5OG30HXAFmiMcvedI9BgzB2vzt89tgv+7lq5yqzUBY7eQrtKlngd8L/wiWq3aZVbqYGWCOhpUZOvFzhlVMdtEB8eGRo5JE973uV5qEhu5zrhzFwAAGFmWPCwnfafnl2+n+l9leBFi2LCFUMZkKeDuZ8JYyKotBaLgLKp2jLprtSp7euSKUVDaNATsi5UQTMkXVJMJkbt5WT4nKLTnTRQYMYdvF2HDN4r5ic3Qxc2VVaQZsTtrTCeBN4GLWDwBzOsjEQDEMvJYk7ybKYQs6pwsU40Htc5XQUPsmIAJiYGBRnGgVGpZhH28fk3+BBUGz/R9f/KH+huadEXyOQWj4J1Re310uPAw5dNq36G6+MecWJgybDC8/j9nLevrWNznkzvUsv0rN/yXLnYYHQkRruw8sK1bEQHYM2HtpU+cBb1/0C8KEkQpbz45gpSQGysKAhIay1JT8rDQD7YSGnSixiw+bDFLyA9jPG4OoxpzFIDTcDm1/HYSznHb75GpBaDgopwxKmqyLDOZig95oRVWDYXLAv6d146KlrLYM2KdpB6+AV8HWtaVBBc+JAVuJdnUt8Ciqp8AuQOHrObGtZDtjgIvgvAy1uBhcRhTEltdptYrOCJ12toSGLFhUGmKgQwbaCQ17kaYLDYsMkvYG6e/cyKOxGISGW6Htb91lJCxx+Qb5/qS7+caeWxgP7h3MI2L3tH7+2ULUi3f8fWLAxepXfpV4OgO2NG3gbuD/q2eCnum/6ZMYyIIB+yTt6t60jXlb+0QWqSoNMZAlAwgMB7VMIvkk+w714RdZ5lOztCQ01KzCVdw4GWgnNKyOqy40DBbRcGr6Ow8M0VgMQsMt0FbAJISho/6XaGoiGke8LnxFDj28RFMlfY7YytSLC2S+PKvG6ffxUscPNgtt4BzgYsNtYMk6sqAy58lAGK6zG23L7wfezrhfmiKq8qRcaY+KgUnJ+I0RFt5pERuMfVOYWPKCN5Px644qQR3sDEhoUDsQAxEw0BAa/EH9OnAy2AY8CHwYwMygYR526CGuzwP/PSaLQWi4ic4LYkO3zS4m36e7nWv8+Xk4duhU7hy/r3X00LaifqaCK8H4OjKgMjczYDvTDniBF4ZS7ND8iz6LgWwYMOZqsEMBk1yHe8OlbFfMJm2lIgY6YyBdreIXiAthtYpW0QHB4QFWsNiH8Wbv6iyn2pwtoaE2Va2CxswAMzWHFSV4SE7o5IVIBhT/vu0X+NwwP8Z/84kiN23sjGQbg9DAsAlj+ES3zY4jX39L43Up62MgvMV6FF7u7tulD5ExYPunbVdDfyKrmWLcMSYitjvSNnEu29mL8UO5VpsBm4e2dSJ4HbjYeT5YutplVunKwoAPl/BhE4gKT7SKDdO+97w5bfnM8R59LBucAQkNg3OjX8RAIQx4xAIdveRz4ARwBVgTNIzJDhN/aNugsSOibQxCg4dj/qP7nNhO5OtCA+vUy/ozYF9Ludm4/359i4cB+15aR7xplIkB66U9fDdtE0TO+aR+MjGQBwNhvpizaGNTwFvgx4BVK2RioHgGLkySGSclvZshOlwG2gyr6PWhFb/H03HFexulBxIaoqwWOSUGBmcg5rHUMQgN19FJ4W1ct81WI18XGhRuPIB6mw1eXgRXD/hJOyJhIESeeAizv1ncLhKn5EbhDNhmtAeG6YXVY/Zhq8504XVSVQfsfbSvcwHRmjYJnA4WrGppVa7yMfBmkizGahTHEdEwsTXKgbCcmOZLi4lcCQ0x1YZ8EQNNDMzE5/eApcDiYF7A2udRWwxCAw+zRYTphzBQFxqOibqGCnPOjocb5+fDhbmgjIdhIAhCREyFh8q1hzlYP9eGgbB873Xp/+9lbOevTdFV0AIYsOVoYz7nkYuePlntKUBtroCaUJbtGbgtSWZCcNgKseGaaVEOPZdwpETY9nRJaGjPi/aKgUIY4M1vsgfwOQYII+ybo4EHtPAZ0TRMDrkl2xgtBqHhKqi6pxhy7FnyJtJONpABfzMVHmB/OfA37YmHgRDG/Dh15REoy8bjlzwplgFjSJ8dDt4GE8Gmxfqj3KvPgE8QaX8ALlAzZD6I1ZqAr/oVX6oSosBqEuWha0xCw9D86Fcx0DUGfEwiHfwgKBChlfwTXAMuAr8B14O7AGMYwzG+L7awwhiEhsuh5364KcDsBvK+s4CMS5Kl/RR+fAyut3VZtAzYB6ijl8Ej4N3RuinHCmDAVk/bhT/8nQlYFlMmBvJkwOc9Mr+ve5t7BXwTzJVnjkpbDOTBACtafJRIiO1fSpK580g/0jQlNERaMXKrfgz4W3jCBJPdwJxDFH9+fjsIvANYki4qi0FouIxOyIPFsGI/IW8P9VQIXdsKCCGxHg57atuftTMiBmx96omoKvOhFHNE5JhcKZwBbw/2M+APfvcCDYcqvE7q4EAQuXj5EtqdR1wdCmavQ8lVxvIzgMjwoemTSfa8ka5YsVb5SzZsCSQ0DEuRDhAD+TPg8zDQaQsiw0hzOzY9J6aohhiEhj9C5cMjJTHb4/qWCVw023SrlJq5EPQq0Bup6KvVJ4UM46R93pOe6N2Vg11mwBjCZy+AyeAA4CsmycRAzgzY2rS1vwIXup4DB4LZcs5UyYuBjhiYlIz/eOsEkv4dweEBBIgDGBsUU1++o7K2nCyhoYUQfRUDRTCwBZm60LDkKDL35S39nJVHcU7eh8YgNPwOWh7Nu6Dt07fNyds7Pxu2/1174WbdlCMeTGTxM2B7p/XF3CN6kIy/vrrtYZgo0oUov+5dDxbrtgfKr64M+H3Wbk7bngsORHoq+qquraEM5UZQOAxx4Zl2ggO/vQX+wNCKz/hkk2Uozwh9lNAwQqJaD1NodCsj+t4JA6tz8o1gR/CLESbkqxscAnyslz/gZ2UrktBqY0zsO5xHaHxS4NsFu4T8P8IEwAVEFdjS5P0A+Dr5f5etrC0Ddiu7FwHvg6dJbQ/RzogYsBNwhk58cjbYlTrjoVImBhoMhKFi+/CNlWXCJMbfoI38pPGrtmIgXwZsI9I/Cni/5SXg997TaYP+WSYGomLARYQVkp7NZ0jGcS91sWxcu0iw5y2Z+gtLxp3Tm0y5K6oCjN4ZFxrcxvpcMe1s/RUDYqAjBmbk7PvAq8BXnXDxYDCbjx/8bbDP0cAESZnbH0nRHyTGCp+sskCzi3D9qWIcMOoxhBH/oJj8y5KrfRqeaF/2lbJ4LD/trLTO6MDLxEA7BsIkovSjw//2n9i+p91R2icG8mHANqDNXZ+2P/pSdiJYIJ+8lKoY6JyBN5NkMSIYjpqS9D7WPsqhl9mze14BB/I2cf7OcywkBUU0FEK7MhUDAxlYil2MOwwP+JPYuop5HeDBOfG39NeCO0Fj1Yl7+eyiQ9Y2GwkuO0YwzCzT6IoxlM3Oh8Knx3BiRqfY3eT/l4wSq2gy/gY08PQ42yqFCFa0vrxYoc5+xtYFouMqXFAVrSMGbDzt40gwBfBG2XbuKDmdLAZGzYCtQbu7Avi16g1wGlh41MnoBDHQPQbGvZmMX5/5Gn6BqPBGe9Ghb2jFFvckSds5k95Kxq/JuVeDyxEwjnk0SWbuXhEGzUlCw6DU6Acx0H0GeABLvgRuAUyw1S+qwCMYJoLrwGdBu3ArdhdqPoTDUaDZL6Ht2eIcsAvI/7ni8i9LzrYtPHlH8Atl8Vh+hogdhM9QbweLDzEwOAO+EoXdkbYVIu+0pO3gXOmXfBiwVWh3vwUM5wyRhj9ku0Q+eSlVMZANAyynMhcrVOyB6PBwe8EhTCB5z3+TZNbmHP0cRIrJ4OBJSe8niJJ4gvOvbD6moM8SGgoiXtmKgeEY8De9Hva3JPDx7Lwpit5iEBrOozPxfHFM2VHk7w/QeUSbFFeszHMOD60PwdODwIcOyUrBgEeghJVDvI3vVQqX5WRBDIS2cgTtZDLwUHaGBWrp34Iqo8bZ2gq0u1+Dt1P45xVrTIiKXhIGpkUozHQqgsGTraIDwsJKjWIQAr0EAsMkcEBjX7qc5lQiGz7Y2FfQVkJDQcQrWzFQRQZiEBp+RieiwEmgbGvy94ewtapYwdmWyaMZAlfbZpuuUsuXAZuZemMoV3hTuEu+eSn18jNgy9NWbk3/169h+77yl0klKB8DHs1gHtXAc1m47/yJ7ZrlK4c8rhsDRxHBzPKYGxDl8HMEB1at6LnI9zV4IHrh++x/yieabOzzLce948tqNu8r4LOEhgJIV5ZiIAsGfL4GR0wWg9BwNp2HV4ojJbw9caFhz+J8KEvO4Y2nz9NwN/BhQ7LSMGCzU2c3A4Z0ubgmEwNDMRAimA6krTD/mTGXj0fD6H9+KMb0W14M2EK0vZOBR9n4vfpv4JNA96C8KFe6uTHg4gKCwv+aoxk8M59o0qMgiGxYLbfMR5awhIaR8aSjxEB0DHCDDHM4xORYDELDj6HFJ6UsyKyX/D1E8/SCHChZtvZluPLOnh5WS1Zz1Nnc4J/AJ/7brHTuy+ECGLBlaCs3Av+fvwEsXYATylIMwIDNAw4DzKkU2uNdbLcDGsqn9lEaBtIJIF3BndDsNALDlxAfXr2u+GHXEhqaK0afxUCJGPBxV0WPvWqlKwahwUMj32h1rLvf7QF8+HN38yxrbsbsyeZRDfeCvlDAspamfn4bS27ZfWAy2LR+5VeJR8+A/5/b3uB14BEO+wM93I2eSJ2RCQPGpHr2NeD3IRfAHgFfAbNkkrwSEQM5MoCYcBhDKug/9TeGU/yF337Xf28h3yQ0FEK7MhUD1WQgBqHhe3QQeOgp0oyLu/2nSA/KlbftCl/ewduxXH7L22kMhFDk+6k/H/u8iVgRAyNjIIyZvz793ycC2FeqkImBohgIQ/l2oh2yemC4H3mkw5FAEzsXVSXKd1gGmOzxWIZIXNV8INEMq/iwCVafiEH8l9DQXDn6LAYiYsAndXkPWAosDuYFsb/1iUFoOIOOAUMXijQ7Pu2oEFouG54BY0UV8xUo/E0SEQ6y8jEQxAaP5PHIhs3L5788LoYBHxdvu4OXgQ85+w6YoxhflKsYcAZCm/Q5G3xojwvgREiGSSSXFT9iIDYGEBW+iqjwJBOO+DNCch1DJYhmuJ4oh7/zletr4SahofAqkANiYDoDs/FxD3ArYNxzmIOBG13fllDT5K9gSxCjxSA0nAZdzlmBZttO88HWKNCJkmXdt1rHPiVzXO72MWAL0u79beBb4HN9u/VBDAzLQBCqzk+vm0SDWaz3uGFLogOqxIAxkZ5dCFwEmwouBetVqYQqS7kZIOxmDhcaEBf+huiwM5+v5PNjRDosF0nJJDREUhFyQwy8FwoYIxhEBcatJkyyllwDLgK/AdcDJitixZppx/g+OvZRWQxCw6nQ428hGAdclIXl3NyHLxXlQTnztVvg7HkwVzn9l9fUHWHG9g/gq1HsLEbEwOgYsA1pNx7d5NfPy4FH88nEQMEM2ATaokfbNFaqoH/mQ/0UgVdwxSh7GPBoBoSFbxHFcB4iw/cID2Oi02hMQkM0VSFH6s4AbwITQvSS3cCcQ5AxP78dBOjIJ1cOcVwRP8UgNJzMzd87qYTjF2VhrCcRKcZ8EbKRM2Brp3V3wsjP0ZHxMRBWo6BzEd4ASmyLr4Ii9yis3HMU7WcS8MkiDwMaUhV5rdXDPRfBbV/QmDjyaT4fDrxfJhMDYmAgAxIaBnKiPWKg6wz4PAw8HAeRYaSZH5ueE1NUQwxCw4lQ6UJD70iJzOc4uxsffJiLbFQMhNBUHxO7yKhO08GRMeDj7O0vwP8X6ZjLxMBoGTDui756T2hD97Ndd7Qp6HgxkA8DYV4hhofZzWn7dFHsbPChfPJTqmKgtAxIaCht1cnxKjGwBYVxoWHJURRqg/SclUdxTt6H/o8MHAWaHZfe+Gct0Amytl+Bl4r1oYy52wfgzcPu6bTJys1AWDaO2bDDgyJvpWViYCwMhDlv/M2xi1Z+XfVJkmViIBIGbNW0XXoUo7fRa8HmoMDhm5FQIzfEQJJIaFArEAMRMLA6PrjQsMMofDmGY334REwzdMcgNBwNlX6zL5gXOzj1Q2/mR9Gopx1qP4U7FxuWH/Wp3T/h82R5PDhgiKxnSY/x47o15rxbfs0+RLn5KYTB/yH9X/DyfwPsOPQ5+lUMtDIQQta/Szt6G3CfMf7ffIiaTAzEwoAtTJv0Fx0+z5D3Qf4Nvg6GGgobi/PyQwzkxYCEhryYVbpiYBQM+LKV9wHmdAmrTgy1LCKTrYWHGh7EEibLispiEBqOSm/ycxXLjH0y9WOTYv0oY+6hw+bDJy4rgfd/xEcXCR1LDOKvz6DfOGadQY7JenfefvlD3gVgKIElLVOYs8RnboeDBV9k5+/TH7QRA6NkwD5IO2JIWniQ8+EUG44yAR0uBnJmwBCWbTfwr7Sd0q8zF8mWzjljJS8GYmRAQkOMtSKfasnAUpSaDlR4IGG8X1hh4jq2vurEJYBwvORO0Fh14l4+u+gQk8UgNBye3tzfVSwxNiH1Y/9i/Shr7sbb7/AwsU7kJfAHehcICZsNk7S2c/didr4OXGxYB3TD8vbLxVAvz4EjK4whptq5SbIM53z0CT4XOFnryDzWUTEzYNvThv4LaE/G/dEWi9lb+VZXBowhrsa1OEToTWXLyyHbFIyrKyMqd+0YkNBQuypXgWNmwG8+XwK3gMnAO/INeATDRHAd+CyYAcRmMQgNh0AZnBU9C7R3JMJSWD+PrZLK4U8IlfYQ1L+DmDtl/kD/H+DRF/9ow60P4XkTnA/8f3kd0Gp+zCopmiNxvNzzguZ9fA3mQtpQkU+d+uV5TwBrgsWBR101zD/7Pi/PN4H72Gq+78Nghuk/eD16RMOn/P8T/9ZbjN8+Bmaffow+iYGRMhAmHPVVhnxsvEdAMQ9I0ZMAj9R3HVcvBnyZVjsVvAT8+vcQ2Bu0u7bXixqVtuoMSGioeg2rfKVlYCY8XwD4BJE+zr8MbwD/h5+OAs14wxpu5AsW6ESatd2EL3cU70dZPQgdMe+UbRdxCXhgDkKDz4mArwMmdN2BfS+DndLf12HbMI9I8oglP68B3nolPwKNB3TegIUoJn9ob9iOfPDjh4qW6cSvLUn72TSPhl+P83114Oa+NPY3tg1/ffiICy5eDv/NxdFvg4YR7r7Uw0mybeM8374NjmscoK0YGB0D9n6a2tWAtmSPAG+/MjEQIQM2G+1zT/Av4O31NcD13pqv7xH6LZfEwJgZkNAwZup0ohgQA60MxCA07JfewJmYqWiz7+ELkSmatGxsNRHG9j8Af0+CWceWRu5nNR7o5yQnH/J0cEuOl/L9bLAN8IfqdUDDLuCDRzt8GniEwqqgITx8is9uLpg9B/4JXGx8H/ChGlcAIgQGtbH6tRwpup8XguXBfMDFEv/fdgHBzUXQFYAfdwJYDLjNDxARktvBimAesC/w4zwKy+1+wPfF7yZwi2FgNz3GfLbXTtvXjxt2ycTAaBiwLWhaLjTQvuwG4P9PMjEQKQO2Hm2UYXXGdTC0WV8qcycwS6QOyy0xMBYGJDSMhTWdIwbEQFsG/GHEUaDZPulNe9ECnUiztt1TXz5UvC9l9cA+kXJ4dKQl+CN++dAJt9+BxsO4f3fxYArYELQKDT784EzwVdBsvJ0ND+ZE5vSZCxH+sH4ouBGw1F+IdmIzqI3Vr41I8dfARYNma4gijX1z88F9avZzb777EK9lQbP9lC/MuRHMhYYXAcKRbQK4XvztBb57WnsBmRjogIGwyglt0l4BU8GvwGIdJKhTxUDODPhyrXYkeApwHTSuj3Ya8HuBTAyUnQEJDWWvQfkvBiJiwEUGR4FmX0tv1v7mt2AL62t7x2Gngh0pefZhAi3e/Ef5wND8QL8tRFPffcMnduPzM8BFhVahgV19tgifPg78Qft84GkcBZrtx3zx/f4gv0HzD4N87tSvWUjXw3m9TC4S/Bt4/g1rJzT8jB8faxwwyNaFBhdLUrOVGGXhwgkPhatc0tirrRjojAGfo8fOAv62eBL4FtB4+M5I1dm5MhAmzP007fRywHU+iA5/Ybsd6M01ayUuBvJjQELDGLmdYYzn6TQxIAbyZcAfxNz84a5o+xcO8AAVwsiL9qXM+ROlEurzlMgL4Q/3CCLJ1qmfLi74EIRGm0x3920InU3uBU+Cy8Ce4DXQzlyAcPNogNvDp5H/GY1fPST7Q/Ay+Ac4AyAGhOEbbIa0pfnVh3kMZ03HjCOPcatNO2Ft72R/ebiT9bsYGJ6BcbSxcV/huBXAVeAAwLAeY59WPIELWXQMjOM+Me53YBNcWwKcAJYBvwQe7XAyWIrPMjEgBsSAGBADtWTgf5TaUaCFiZZ482p+g47AjAdJuy4CR0rugn0bHr1e142sIP4Q/58mny7is8+n8G7gAsPqwM1FB48IWAe4LQBcVHDRYC3gEQRuHonjx33Tv6Q2O1uf++Bu4P9fF4DhbKx+nUTCLo7tDyaAhp3LB/drXLpj7vT7gel33zDeOHm86XvjowsQqwA/937we9Bq5PmFJ8jC69jfPjfyaT1O38XAGBiwdWhT/K+F9kUbtE+NIRGdIga6zECYp2hL2uvVgGtkwDVstwYuCsvEQOwMKKIh9hqSf2KgRAz4Q5CjQLPduAH7w8pyBTrRlHUYI/xS0w59HBMDYbnLp6lXf0iIqYPV+kBPpzA8kPP2KXkMNB6YW4UGf9DxB/cdQLPtxBfff1zTzp/yeTLwt7P+xt9/3xEMZWP1iwiDIGo0p93LFxcIPN+Z0h/mTL/7vBEN888uUizf2JFub2V7Q/rZ0xlEaJjp22RB5Eb4//01W89XJgYyYiAsOcz/TZhclrZs14OVM0pcyYiBnBmwJWmvHtXwHPD261ui/GzZnDNW8mKgEwYkNHTCns4VA2KgHwMuMjgKNPtCehP2h7IIzAjZDZ2CxSJwpuQuGPMFBC4Pi6ggrQ/0Hpng/wNvA97M91mr0OBCmD+U/wV8ECwEaLvJ6+AdcDpw+wzwB/zD/QvmwsX14BUwAQxmY/XLoyU8P/fFJ7NcDVwKvDy+fx7g5kOTGP8e5lvwso0Hc4CJ4G6wJvAOsHPg560F3IYQGhLvRFM+I6oi1PONbD3yQyYGMmTAZ/U3RDF7Ffhb4vOBh6rLxEAJGHCh3T4Hrgbefrm+hlVWPs+Wti0TA1ExIKEhquqQM2Kg3Az4A5ajQAvLQ/mNd8UCnWjK2jZKOwKMP5d1zoAx3tqYByGaB4PWB3ov4q+AP1w3t8FWocGP2x34MAs/1nEX8AdyFx98v4sPzwOPMvAH+Yb5Q5ELEn8FMzR2tmzH6tcipEM0QRAy3Ce4Tk4FmwH/vjlo2Al8mAx8/zLpTt/elO7z/W+Aw0DDhhEaGoeFyKQpJP0oaI2QaBykrRjogAEXsexM4O3M4Z8lbHXAqE7tNgO2OG2W6Df7L+B6ay8Db8fN955uO6X8xEAzAxIamtnQZzEgBjpiIAahYYf0hrtyRyXJ7GSbL/Xn2MySrHVCIXx0EpxeUSEa3ktZPHogJuvBmaWARy4MZX5cI8qh+bi5+OIRDb3NO0f3Oawz/yJ17W+eNx3duTpaDIyUARctQ1SDvx3mHmZHgdlHeraOEwPFM+ATnBoisF0K3gYuOtwKdgdzFu+fPKgxAxIaalz5KroYyJqBGISGRnj9qlkXbuzp2cPc7K8e+/k6sz8DdgR8ekeKupZVmwFbmnp+EHjneb9ql1WlK5YBn68hhKP7teUZsDeYuViflLsYGC0DtgjtlqF29hjwtkz0m/0crA986J1MDHSTAQkN3WRbeYmBijMQg9CwNTdTv7l+LB6u7Tz88Tdlw70djsflqD3xSQLDah4+Gdb8Ubsq5zJgwIiYsD8D/7/+FZg1g0SVhBgYhAH7OG3strS9Pcl2D9CYBHWQc7RbDMTGgDGsLrTl89n6cEO/fj4OiK7UMpmx1VaF/ZHQUOHKVdHEQLcZiEFo+Gx6Q/XJ6CKxviU3V4rEoQq4YR+lnt8BF1agMCrCsAy4SBdmWPfOMvNX2IRhT9EBYqAjBuwztLN/AW9z/wa7gOa5UjpKXSeLge4xYHPTdl0wuxl4e3b8DewGNLSiexVRx5wkNNSx1lVmMZATAzEIDT5O0W+i6+RUxjEka6yAEXzaawwn65RBGQhLfXldbzHoIfqhYgzYdtT3G4BJMn0OB5kYyJOB8FZ4e9qaD9/xa40Pg9sJKDotT9qVdo4M2DK03+OBR+t4m/br6S/BRkDtOkfma5q0hIaaVryKLQbyYCAGoeGT3Cz95rl+HgUcW5qhs+qzQRPCKMuOgbBM3QPwOhHMm126SiluBnxG9TD+2Odt2CduX+VdNRgIETWfp709BPz+4sLDjkAPZtWo4BqWIvRLNqQNu8jgYoO366fBKeBDNSRERc6HAQkN+fCqVMVALRmIQWjYJL1hMs42JrPL8Ys3CLJsGbA14NWHUNBZktWHgbCayzXp//qv2c5en7KrpMUxEASHnWlv/07bngudHvHAeHiZGCgrAz58wr4ArgO++oqLDncBJuC195S1VPI7CgYkNERRDXJCDFSDgRiEBlfo/SYZ2XJ4dljq16LVqOqYSmGnpdx+Kiav5EveDIQl3U5O6/4+tsvlnaPSFwPTGAhtzx/MHmlqf6yCI8FBLaTsDBh9FDsE+DXV+1Iu5F8FdgCzlb108r/rDEhoGCPlWiJmjMTptEoz4EKD2xzTNkX8DeO2edOZ8NA57o9FeNA+T1uX/deC7fCLN7Cy7BgIqxDcTXqzAB42xzFMJRqjw5Y8AX4xiEe7sn8yOK/N78uw79PgNXA14O3pkLYnvz4FLm06ai0+L9b0vfUja60n97fubPrugh2rPgyw29nTfJ6HkG8FmI8keRW4v35Mw5bnw6FgR/B2Y2c2W5+0LzkHjAe7U/+/yiZdpSIGhmMgrEaxE0cdBvz/7F5wLLiAdjiVrUwMlJgB+wjO+zV7G+ArPPm96LfAIwi5xo9DhJCJgSEZcKHBbbVpG/0VA2JADIydgRgiGtZGdTfgD2gRmT8M21vgzIicqpArtg7c0rG3cyIq1MfwhSU42z6ou5sM80loq8nv/UuL+cPyFHATYEmyIEZswXYwQ8AKaR3XcsBF6X7Ppx32ajm++asL6q8Mct4+TQf6sAUXFTx9F3yeB/6Q5cJCs13LlwObd2T32ZYk+zsAPthZoCe7tJWSGBiOARccbHfwGPA2iCgYJo108UsmBkrOQGjfm9GmLwRvAm/jE8FpwMUImRgYjAFFNAzGjPaLATEwagZiEBrWSG+CQz2Ujbpg2ZxgvD32pflk+TBg30vr3h/gizZ/SGeM64CH7YZf/naIjlpboWHLdP8G6cEeLeDCAx28ZN50X2PjaXRcBwAAQABJREFUDzIHgUnAH/RbhQY//r0tmMB3wmIDhlrabAmO8TRdjGhNo3lOBM/Tj2v8z/XymTe6QWzwSIaGrc4HL4OnlYOFyUHPxhXvBP8dLJZDJkpSDAzBQHgg+yJt7+G0HT7CdjeAECETA1VgIMznsAttmoiGMKzCr7cPgiMBgq9MDPRjQEJDPzr0RQyIgU4Y+B8nOwo0IzwrPGh8rkAnBsk6qP9v498cgxyg3R0x4BMChjeKT7Kdq6OkOj/5kyTxDnjPIEl5FMNfAR20AREND7HvRtBsi/OFDl2yf9PO2fjskQQePcCbpbZCA7sHmAsTPlxjuQG/9N/hwoHnOdxxV3HMU/1PTdZOz+Whq5+5wHFqvz2ZfwmTmiFo2AvA60EmBrrMQJg0ckfa3/2A/yFj+JTtBWbtsiPKTgzkyIBPFGnfAP8A3s4dPFja18Fg974c/VHSETIgoSHCSpFLYqCsDMQgNBDGF25228dHovHgFnyL4Y17fPSMzqMPcfjrwB/mW+Bjo8P46Jb9/Y7zN+vrgrzsShJ2IaGd8YYzzGMwga0/eDcPnViA73TWksNBq7kAcXHTzgX57HmsDTyywc87FgxlH+DHSeDooQ5KfzuGrY/JnQF4p/H/wByg1b7FDhc7mt9mHch394f/x37m+focDjP325v5l7AEJnyF/zcP79VQisw5VoLDMxCWENyG9kd0U2iLz7Hlf9vmGf5cHSEGysSAvZ92zf2nb4JU7r/2F/Bl8O4ylUS+ZsqAhIZM6VRiYqDeDMQgNHyYmxoPOPb5+KoiLMnHA5n5g5msMwbm4+n3VZ4ep345mYGezMjBWIGpjGvwN/rv68yFIc9+mV8Pa3PEUuzzh/dd0t9ahYaV2O8P6HumvzdvbubL35p3NH0ez+eRCA1+Pg87I3rQ/2N67A1sXUjw9N8Ee4NmW4QvNwEiScLQjZ+zdRHIoyygup+twTdPh//TvM0jh+wXgPzCG7el885R6YuB9gwY/wceXWP8/4X2yL3STgXvbX+89oqBMjNgH6VtfxtwTwjt3SM5rwF7gPnKXDL5PmoGJDSMmjKdIAbEwGAMxCA0rJDe2L4wmJPF7g9vtm4p1ofK5E7IZmLnJuOZZbN3RLg8mckfch0n5sjCwmkeO7bk4WKA1/1vm/a3Cg3r8Jv7t1XTMY2Pl/Hh4caXlq2n7ecd17K/+esH02NOaN45xOen+M2FhS+BxcH2wDsNns/nQMNcTNgLuBjxBvDf/VwXTVrNRQn/vZWb1uMy/B4m5UPcMcQP7+jKxECRDBhim10KXHR+C5wHPEJLJgYqxkAQ2P6P9n06+C/g2h/aPBF/9kXwrooVWMUZyICEhoGcaI8YEANjZCAGoYHx5OFm5uHpEZp9N73R8rZV1iEDM/GE+9BCLJf4StIzrNDwJscswzALIiGeId88+d+I9P1heh3QbEfzZSKYv2lnq9CwCr/5uds1HdP4+Gc+PNT40rIdidBwBue8AxZrOXewr0vyg6PZfAiFp/HPpp2/5rMLEruC2YFHDpwDpoBPgWabkS+83cpV6GnOL/1sRJIYIk+4NvyRrQ9RkYmBAhkwv1edDYiuCu3SH742KNAhZS0GcmQgDCNamzZ+FuAeHNo89wj7E9gZaDhRjuwXmLSEhgLJV9ZioGoMxCA08JATbmD+FjZCs8+m/m0SoXNldOkTOG2HJjMOKzR8h8gHPxbsDPK0PUnc81m+KROfG8EfsC8C/nsDE/l8Z/p9Wbb+IO/nfhW02u3suK11Z/p9PFs/b7CIhln47SXwe9CpXUsCPs+DRzK4YMODUvIL0Gyz8eUV0By90fj9OT5c0PjSva3BkX0TUA+ho7tZ9/JWTmJgMAbChHon0iYZbhXuXYh4th3w/2mZGKggA2Gy1PVp4z8E3A/6RIfL+eyRDvNWsNB1LdL/o+AOmRgQA2KgYwZiEBqWSG9a7R7UOi5g5wlonobOORyQwuW9vGV/eIiohmf4bS4e9Hky9gd1f0DO03xYgT/0f7Ipk0aUg+8fDF/gtxkB4dTJKaDZCMRIngU/b97Z9Hk4oWFLjm31qen0AR/97RKT2CUrDPhlWjSD++JGWGxIt91QCB/q8bQf1GSz89n9+EHTvi5/DCvTEBkSOrfe0XVRRCYGCmYgzCnyDdrjE2nbfJwt86H4ajoyMVBVBoLosAHtnHtCX6QD90C7ChCZqjkdSl7zEhpKXoFyXwzExEAMQsNi3Jh4kPEOWqymeRoyrpllSe+tLZgQcrC5Gr7EbxzjWD3jvNslt0aa137tfmzZ1zp0wn++GDwKmgWR9fju/m8F2tlwQsMxnOTnj3TIgI+d9SESN4Jmc649HffRbT7g33/kX5rMhQofTnF90z7/uDLw4w/3L8VZWAr1x7iCL2EN+FWL80U5i4FmBmwm2uQO4M60fXqkw8lg0eaj9FkMVI+BIDqsS1v/HpgI/PrsEWhXgz3Au6tX5sqXSEJD5atYBRQD3WMgBqFhEW5GfnPat3vFHm1OYZ4Gv3l62LksGwZOIxmmtZ5pgNjwT/YRDsDEa5szdMB8CEHeNjMZvAZ+MoKM2gkNq3GeP4yfC5YEa4PHwOVgMBtOaHBhwIcsDGU+zMHzbJj7736cAt4PPgq80+D/5/69YWfywY/7JlgK+JCEawCcJ+uDZtueL37sms07i/tsm+MO0Rk0m8SOAB5RIhMDkTBgH6dNXgH4nwlt9Hy2/n8oEwMVZ6BvTgfuL30TSSJ+hyUzv872vRUnoCrFk9BQlZpUOcRABAzEIDQsyA3IO2UHRsDHIC70zdOw6SAHaPfoGZgbMeGFDybj3pncMoRi3WQcy1nOwDwCT3q7aH3zPvqcRnaGP7TfOoJD7+OY37c57nPsexH4Q7mLFheCoTpWwwkN93L+dWAoe4Yf3Z+GuShzOuAhPPjhwsEdYHnQbL18OR5MAu6vg/Dv5DOg1U5ix/Mgogd6f0tmDPMI142b2bq4IxMDETEQJo70CJw303ZKpJFtCSL6P4qILrlSMQaC6LAG7f074HHAPSas2nIL2wOArtnx1riEhnjrRp6JgdIxEIPQMH96EzokXvZ8oqNwk/SHLll2DOxJUvb9puUuL54+AeTh/OTj8b2Dsm12WQ6a0v+5L+Ajgx4x/A/jOMQ7UP7AX6S5kPB+MPcwTrjYsQx4zyDHeTmeBYcO8nvBu+1LVNnrAGHHvgKcf5kYiIgB8/ubR95MBH4texT4vA7D/W9GVAa5IgY6ZcC4r9oJ4EHg/wcOH2rk/xu+jLMsHgYkNMRTF/JEDJSegRiEhnnSmw43nJgt3BRvi9nDEvo2I0+Gd6HivP08UQ2vgQl8JtLB367zkGszA++M+INk61v5PIp7PolekkfCJU2Th/fkMUA9xGrmq9bcDLzjegNYNlZP5VedGbBe2ubOwK9n3lb9mvYD0G7y1joTpbJXngFv83YUuBs0RIdH+cxwSlsPuAAuK46B/0fWDpkYEANioGMGYhAa5khvNkd3XJpcEwiTe03F1/lyzaZ+ia9LkW0flrs8DvhnsPV0GmwJdjEkwR4Ceb8FXJR8eQBIeHitvXlnj85fc13EykkI092H9uHRDQwHYfXUxCfok4mBGBkwrnmGoBkmzeN6FwSybdj2xOitfBID+THgQyhsv/R/4G22/v/wEvgloB9gc+aXt1IehAEJDYMQo91iQAyMnoEYhIZZuZn4zcXHjEdstmHqZzfC+CPmIRfXLmHg8juEMDBxVPLXgTnYJnDPb/YnMMPA3zPdQzh+smOmKZYzsQ/j9lnlct3eR/u4Cvj1xN8cE64rEwOxMmCL0EaPAROBt1nfHgveG6vH8ksM5MdAWEp8Z9r/b4BH/Pj/BHM12ZXAh8bp/yI/8ptTltDQzIY+iwEx0BEDMQgNvMUJN5RvdVSS3E8OYfxv4Os5uWdVvwzexxAKOhRBaPAH3DYW3lJ7x+O4Nj9qlxhoYiCEqL9AW/E3ZKcCxEyZGIiVgbA8pkc0ILKGe6G3WyanDeI2l0aZGKgbA6G/9Qn+B34EngZ+73f8A3wTrAL0v5FPs5DQkA+vSlUM1JKBGIQG3lCHGwgPBLFbUNafit3Lkvq3M35/fXDfvVMRwo29s7HD4MfpFzHgDISVKS5Iry2PsN1AvIiB+BkI49e/T3vl3hzuiw+w3RvME7/v8lAM5MFAuPd/lP8BXjL0m9fhv3z/MfgUGKOY7MOVJFi01JqEhhZC9FUMiIGxMxCB0ODOhxUdTh97Mbp1pu2Lr/6g+4Fu5ah8mhnwzoTdDnwc/v81/6LPYqA9A6ETijgY/m/PYTtv++O0VwzExICPTQ/h4vekbdeXyfw50HUvpmqSLwUw4HNG21eBD6kgEjJc2/3/4zKwJ1hkZE7Zahz7MDhkZMfX5igJDbWpahVUDOTPQCxCwxQu9iUYDx7eNrnQsE/+VaMc2jNg74F/f3B8Dize/hjtFQPNDISHNn9L7JO5Pg++ABR220yRPkfMgK1Ne/0VcIHV7z93g71A3pPjRsyJXBMDzoDNDj4LXER+Fvj/h+NFsEV7juxgfrstPc6P/djA42wl9q8NJgz8rfJ7JDRUvopVQDHQPQZiERp87oMfdq/YneRkHq53eScp6NxOGTDmcQiTRd3Ldq5OU9P5dWHAO5R9ywv6UpjL16XkKmcVGAiT5fkM/Q8Af0BK5wzyN7MyMVB3BsLqQ36N9yFz/wGHDWTEFmX/n8ERwOfvcUGCuagb5ktr2k/BK+CvwMW9ukU8SGhoNAdtxYAY6JiBWISGV7mYc3Evg9nP8NU7eKyLLiuOAducOngHXAXoHORihGEmnxwk5RnYT6hmsuIgv+/K/h0H+S2L3fuTyJezSKheaYSO5L60Ga59NgUwCe1Yx/fWizmVNhYGwpj1dWm354PJwEWHOwHXAwmvsdSS/IidAfsj/y8X9vfSzmSfR0suPG1/iH57i+8t9/lKRzxIaOjfKPRNDIiBDhiIRWhwVfncDsrRxVNtO3z1jt0GXcxUWbVlIKy/7XVBWHzmRj0npJ0MtsrFR/mNtx3JrKDVNmGHn/v71h8y/P4Iaf09w/RqlpQvlRaWUfP28xj4VM0IUHErwYDNT9s9APhYc2/Lr4OfAc3lUIn6VSHyYSBMAulLaH5xevphuVkXFXaevs8/haFKX5u2rxYRDxIa+jcAfRMDYqADBmIRGp7lYv7LDsrRxVNtAXydCk7uYqbKalAGwqzT3sE+aNBDRveDR0d4Wi4iuFgwmNBwJL9dDVqNjn8yEfi5Ehpa2YnuuxGxYo8C6sv+BJaOzkU5JAaGZSBEOSB+94tyeJDvh4BFhj1dB4iBWjFg6/F/4df8pv+NMNm3R7rN1p+KMAH1N6ftG2nEQ/8USvZNQkPJKkzuioGYGYhFaPB5DxhXVxazW/D3vrJ4W20/wxsGf0D0TsMuHZbVOxisapEgJCWnAdIcVGi4md8YLz3AXFxgbGdCJ39YocHzmxf0gGabhS/t9jcf0xrR4Gn4OW4+rOODYDnQNP6Ub9PNj/Fw0Pmm7xrwyee/WA28H7Sm05yf/8bkWclCoNmIGkiWat7R8tnLuQpol37LoXl+DauZHEN1vwk8FP0kMGeeOSptMZAfA2Euh71pw3cAvy76ELMrwbZg5vzyVcpioCwMhCFz/+rvrV3B/8f5Lft48RDuC9uzRZSwYSIeGmeXemiFhIZGNWorBsRAxwzEIjQ8wQX8ko5L07UE7HD89Q7cEl3LUhkNwUB4UOTmGDoBg82pMMT5fT8tyCcXCdYGHtngQsOxoNXmYcfboHUywd3Yx3wjyQTgQtRwEQ0e3uzpXAwa5g8C3gF6EryrsbPNtlVo2IZj3N+NwPPpZ//ux7mg0Gz+duYl4L97/l5mHwrSMJ9/5LuATlU4xo97BnwWNKyR32bsaKTlx/n/sQsU1wD/7vAhHh8BDXNhgv+hZApoHDORz5uCAs3ehzu/BfhklNd2B+6rTAyUlIEwce7ptOPngLdr/lftB2C1khZIbouBDBgIItyp/RMKq1kh0DWbrZz+36zAdl9An3nIiAcXJn4KXgHcV0s5maSEhuYmoM9iQAx0xEAsQgMPQ/aHjkrS1ZND5807bS03pa46ocz6MWDzUh883IeJOlfv99PYvgwlNGxNkk+1JOtv7l8DjaiKkQgNnoQLGf6wvaV/wc4A/vC/pn8ZwgYTGlwQ2B+4CLYVeAPcABr2DT68A3w7N1gF3A7uADMAt5OBiwxfBy6qLA54AA+RHmuwdWsIDS/zmTelIXLh+2y9LO7D2WBF8HFApysID2yCEc4djvOOnvO2CPgF8HJ7JEbBZuvj3p3A/8fvBl4GmRgoMQNhTDpCod9ngyDrbZuoK5+Z3yaUuGByXQyMkgFbiDbv7b/puu7iQdi3Xv/E7ET2/xdwbxwu4sHPrMTQCgkN/RuBvokBMTAGBvbkHH/zm5fQ8DnSPr4FR/HdH1ya32zy1S0s10X4e0c2+xjO3odzPj+G8zglqN9XN53rD3dfbvruD3GtHPh3f7A8AGwGxoPBbDl+8OO3H+wA7W9lICxd9RR18wJw/joxrxt/aD6uTSL+EM1biz7zY28B/jDesJEKDX7u38HTwB/YPc8jwHD2CAf4eQ1rPPh7O2y28/gyuWnHS3z+ZdN3/+jCjLflFcCCwIWIs0CzzcyXJ0FDtGjk56JBw97DB/ffxYeZGjvZXggmpt/nYOuCzE3p98ZmFj749eg3jR3Fbv8/e2cBJ0lxf/HZI9wd7s5xh7tDggQ4HIIlWNCQACFYBAkQ5I8El+DuToCgwZ3gwd053DU4d9T/+2q793pnZ2Znd2d2e2be7/N5U9XV1dVVb1qqXknHiuXWFIf/JVZAr8Ode2Dz5LObgVowEKbhWubdFx5Lru0fce8CWwFPGaoFxU4jxwyELbjOEeCLpxHFT2ZrRGBiUZx7g3hHtQd0O+JhRuIi0Iffpikkx0ms/lPnsFxvWWjI9d/jzJmB/DOwCVlMG1D1EhouSs6Rpi9XjR2dV7gNDAaJhWcIvjnd6qGrBs0/gRrvPbVnOeDanh7UHj8OP/2efKcVs+KG33DiqayfgJcyeCsJ176bQHo83k52BlujgXqDeyOidEqsdTbCfNBOYzrQKA4j+lBuCQD6j0oJDW8TvmEm7QPwqyE9VSasWqFBh8wBvgI63x2A3pNurfh6Sxv+yxcdeTDbSlf3yYjE/1vccrYiOxR/tRIRJK7oXpal51ujfbPjVyKChIWsnchGetzi+JX+ZWDbIjzDtsqVIwvce+FAoIqpKpGURXPgbWagGRgIiIta2DjwTIuCmq7zi8EvwDjNUEKXwQx0ZiB2SDC6p9gC795wD0jqWxIY4hQIxPeqRjzsQjzec5WmVhSfM5fbFhpy+bc4U2Yg/wyo4bQH+Baooq8GlCr/aQMAb80sFRrailJUL/9pQOdX4yyxOEz59nSrh67SVHq79/A4RV8UzNuL4zgkrlbPecMGyfHFDb/higQOTfZnnVnZoMES9++f3ZH4J8D9AhwNfgTbAFtpBqYiuEis0afdYu8E/0n6PezSB1cILSc0UDGPAtBkybG6fiQIXQayDef32H4iCZsLtzv7DxF0vezdXcRkf/H1ljb8Fyo6XveZ0h0CVkn8a+CWs9+xQ/EXLhHhEMJ0PWr0QXq+RYriSWjQtZ21E9hInzOq4Cn9t0BWgEv9EmhyaLFyeiFZp/yBERvx06qDc5hRZ8kM9IKBOIKH50O4AHwFuEcDz7DAO0jz1G1moNkZCHNyrY8CLwM6oeKUicx7sNsRDzdyzCWdWYqLVX9D+Kadw3O99QC5E2xmwAyYgaoZUMP1EaBGwjFAFf3uhAaJBFOASUCxTU6AGvjl7CJ26BzFQoPiDwVUZAr3a6Pd4ueD1NCaCCyWoPi8qtQrP7KZwXCgHpdZgM61P0j3463KVAadMzVtj59sKK2lgMpawgKNrdjLeX6ys7jhp/wpX4eWOFhBOu/nIMODgqP9ll8dOxe4DzwGbF0ZWICu/48BFYPCDJ13x3n2esG/AKbpvK+qrXJCw185Ovufrcq2/qtK2LKbM26XHK90NfJnoW7ia3fx9ZY2/DMVo5jKAfwqb0MAoz2i/3e4WdN9uiIQTysBxadHs4udS4i4lpU7X3dCw884VunvrkQaz8JPyf69gDKEV8AGjVcG59gMVGIgjuLZgmubkYfxixW61p8H+4BZKx3pfWagsRkI43KNLw+WBlN1LkulEQ+K2d3Uimxq8asUW3HMLNnQnPglMgg2M2AGzEDVDExLzLvBciBtQB2I/38JcEraDYT+ALKNl83ZpuIRF5zDKWmVhAYJBh+B58Ye+c7DdLYqTOmmkCii0Q+DgCw975b40zj7ZvxpWBpfx3RnqNadpk5QmYqjCOi57DiH0qVXp5SFy4mGWBDUiCtu+A0nTMeWExrUK8ww1ZIP9P8QnvLzR/xKZwlgG8tAFBlQo0ZzQY3hTy8lNmj4Lw33uKBfT0Wo9D6RIJc1Kt+FfbMBZfz6/64usy8bPCcbEt4uBhIEXwNPg6GgkhVfbxsRWddJ9l7V8Qck4bpGdc19AS4FWdMIBx2big0SO87ORsA/IfgAXJaElztfd0KDRp+ovLr3sqb0HwYnZAPz6w8bQhn/VRQcHsIdmd+8OmdmoLcMaERYHL2D2B2vdZ4T4X6wI5i6t6n6ODPQeAxUGvFQzdSKbInjFDzdS4LeI2eAjcE02VgD5LfQMEDE+7RmoFkYSBtQ1QgN01LoD4F61HXczEANlRuBekHLWSWhYTMO4uFaOGfswatxjsFj2P4l0AiCnwI1aBRvbSBLhQY1dnYGe4FhYH6geIeA4aAnpsbOtZkDnsevRtb1YBUwF7gKKH01woosNjbYF9ZhR3HDT3nRccVCg0YyqMf6umS/hISszcGGjtsjCVQDWXk6J9m2w5cJEBY+nhSR4eHCuOE6UEFs+BV0IpbFUTMcUrWl90lWaJiAo/VfLFFFKtUIDeOSjhrX74MpkzRXwNX/f2yyXc4pvt42So6rJDQord3Aj2AfoPsZfqKwcTtuarqXdD/uB2YBi4JbwedgcSArd77uhAYduydQGa8AKq/4vBJI1FwQNIjFxcJ2oigfAcrDpVjQnHebGWhGBrTAbuB52CGw6bnKu1LDwovnpTdj+V0mM1BxxAPvvrDqWI6KF5Mcu4d4VGHCzwDvwjhy6BtcvUOEp8AxYC1QNC00m0bd/BYa6katEzYDrcFAtgH1P4osVLJfslONgr3AveBdMDWoZKnQcDyRjkug0QlqrKgB8ykYDmTjsNj+W3xg4c32zY7fufHpvLsnIZsn22qkZE2Nx2y87L7u/M8S4dpMpOfxS1hRz29qs+JR+n9LA8a6YXx26eVyMWHFDT+VT8eVAy+W2DPOC6eTpY28GTKhl+NXfIkwrW6dRIYfCkNQEYZ0JzZszN/AdRceBNWKDdn7JOV8TTwfg+L/LN2fdZ9j4+psQAm/RAxdH+sV7TuFbYkBErvKWfH1Vq7hfwAJ6BxDkoSUd4kNEgTSa5Me+Siq4URT2fcGaRwaFHHkzZJxb/tPufPpmBMz8eQ9AWSfM8oDDfT4dQrlQenfCTYBDWhhEqg8GHwNdJ2dD2ZrwII4y2agCgYCnQxxaPlJuKnI9hX+S8EGgPeizQy0GgPdTa2oxIe+gBGne+o9onrKaMC7Mbp34qbv70qJ1GqfhYZaMel0zECLMpBtQKnyn20AlKPkdHaoQSCRYKVykTLhF+FX/PcyUMPoDnA0mBpkLNxC9KcJmBGsAtTLfwlQGvsBWSo0rNq+2fFba6Hhro6U2z2DcZQPNQpLWLiQ3TQw2l5jpxpsqQ3Ho+MkrvwZaBTG8YAKWVwkcHrcYkN0KbwDeNEU1FBJsSN+paU0WtlKigxVig2/gUI1Ah8FU/SSxOk4bp5eHpu3w9TY1/U1bYWMKY6EtokqxOnLLo2K0n1SrfjTl3P1w7FxmDmCahxBg3gSeG5qEUmbGWhWBmIPLwJsfA9+gct7Ki4mKdFhfWDRoVn/eperiIFKUyuKona7qQ4RjZQNxwHdS6qH9pdZaOgvpn0eM9CkDPyEcqnRqoZztULDiskx6u2fDHRnFxFB51BDogq7jAfbrN8SUcdQQS88A85ItvfDlaVCw/ztmx2/tRYarupIud2jxpbypZEGJUxD5VS5mugDdj6UiTAcv447NBMm7zJAZX0YZEdOsFmgwhaP0XGl8ALhVXJKzOayiiJDlWIDvfCxEfgkbpHY1VxkuTQDyYAWzAvnAQlb3wFGdASJVDYz0MQMxF7ZX3Kt8/4PqejwJf5/AosOTfzPu2gpA5WmVqRxcu9aaMj9X+QMmoF8M9BToWFCivMyeApImKDS0K31RGigwTcEcWE+Nb6XBWnje2b8amzvD2Sp0DBf+2bHb62FhuLh7t0JDYxC0GeQpmNUQ1VCgzK+M1DZGA3RyTRnnYpZYUmwWBHoKY3HSPRpNatKZKhSbFgXGr8HTG0IpUaVtBq3Lm/dGAhzc42pN+pHwNSncCzwNVc3vp1wfhjoJDpQb4gjHVLRQdMrtN6NzQyYgfwxYKEhf/+Jc2QGGoqBngoNZ1I6euXioovb46qBrEZ/JeuJ0LA2CZHmGR8VJbhFe3jHlIVyQoMWy1Ge9io6vprNZ4l0bSbi8/h7KDTo6HBEoTCCxsSQxzNpDdcOUDyiQVE0KuEuoP2/AjL1sNMALlygjRI2J2GKf3mJfc0exAiEQriVRR9TMaE795TCT8SVIPGmyMKa7ELYCi+BYUU7vWkGasxAWJDrjOswCg667k4AmiZmMwMtwEAUHXjPaS2jkIoOCPPxntgUd5IWIMFFNAONwoCFhkb5p5xPM5BTBnoiNKgRrMbaPklZ1EC+E3wORoBy1hOhYR4SoZG+pMSMBYCGGG8JtJbBGHAskJUTGhhREKdb3Iu7EVD5ZFeCc6Ov/E+thAbyPTM8zfhm5lTD8Yu7UkKDokk4oNFReBuoopWOcmAqRlm7hz2M/ii0Wq8oldHCmIULbWM+LAzuVmx4iTi04kZzsVKZjaNkcIotrMLfo8rua2DW4r3eNgO1ZyDwnAiXAY1wkOBwErDQVXuinWJuGYiiwzpc95pa9CngHRmnF12Hy3u/1+vn5LbEzpgZaDAGLDQ02B/m7JqBvDFQrdCgBr9GGTwK0sa7yqJGmUSAu8EgUMp6IjRw/N73M2BCDWg1zAX1YC8L7gKPAVk5oUH7DgESKnSsGvCy94FW/q9kNRIadIoZaDgswPDoDhuOT/kpJzQo4t5JnFNwnwbvAgkn5YyKWEzz/8pFaOLwzShbt2JDkcgwsjIfYXnoVA/be2DhynG91wzUioEwH9cbU9A61nDg/g96XtjMQAsxEOezr8a1fzpg/acoOlAPYPBaIWwHVAexmQEz0L8MWGjoX759NjPQ1AzQyKrqqxN1JiGcTaXiM06i3r3Je3mywRw3WS+PrcFhgREJsaL0sxok5iRKM1BRbOiZyJCeILAWRvgAMEonjExD7ZqB+jMQGM0VF84bg8u0qXAamLn+5/UZzEDeGAgI7FH41bSidwAifRz5cx/ubmD2vOXY+TEDTcqAhYYm/WNdLDMwEAzkRWhQBVujJBrYwrSUYTQ4sYEL0QhZLyk29E5kSIsb5uB/ew0wKiWsm4baNQP9w0CYi+vuAqDnh3p0zwUsJGkzA63IQGjj+l8KsPZReBlIdBAY9Rf+DhZtRVZcZjPQTwxYaOgnon0aM9AKDORFaKBxrh69RrdwPeVgukm/fvO40UnrTf47iQ19ExnS0+trAOEpoN7lbdLQPrp7crzyWsrUc30w4LwVbVv2snhlSRuPUD4nF7/MsjbukBKxNEpo0xLYoETcNEhrhmxeAb9OIjIFoHAxyE6tSnbZ6TkDUfA6m+uPZ2Hszb0c142qnhPpI5qKgcAnrQPTBQPTODtEh9fxs35TGAkqTTdsKiZcGDPQDwxYaOgHkn0KM9AqDORFaFCFgQXSGt3CxpRDPS+/avSSNED+o9iwEAtEzjh24ceRfct34FOp4T/Jf7hf39KKnyhl3nHJ6TyqGGvxUq6VwsKgnG3CDsU5qESERQj7ErwF7gFao+QOMBHIGlN6YhpKJ4vPs5GK/PMWxc0eJ//Hmfi34989s21vnxkIM/FXaQg5a77E58lNuCv1OVknYAYanoEwgnvhL+AuMBrwPIri/nm4GwKJpDYzYAZ6z4CFht5z5yPNgBkoYiAvQsORSYWhwXsmAj3McSXtG4p49mZ9GIhiA+Nsvyb5kbU5RfwPr0qux/NxS40S6O5UZCkuaLpXmYj7EJ423ksJDRohsAdgKkdJoUH7Hwe3gjR/i+OX8MBw405GGQqvAY1syGKGTrE6byj9bNzUzwJtMT/ZER9LEUaDOMbHsdWOgTANdB8KPgNqULEwbuCa1yJ6NjPQ6gyEqbgXWCA5XAN4B8R7RFOPED/DLmDOVmfI5TcDvWDAQkMvSPMhZsAMlGYgL0IDQ8hjJUFDwRvcwjGUZQyYucEL0ijZX5GMLlrbzIZB/H//SK7J/+BO0cP0NdWBa6DktIifEs7Q+MKZQGJDsdAwAWGPgB8B11JJoUFTIXTsyiBr57Kh0QZDM4FP4b80s91b72Qc+A6QcFFszxFwVHGgt2vFQJiIv5uRKeENIMHhTbArcO9trSh2Og3OQPxs5i+4J04CrwPdJ8JL4GjAiCALdA3+Jzv7/cOAhYb+4dlnMQMtwUBehIb9qQSoUjBx47MeF3ZTWeiJtDU2A2Fb/kf1kL0MetI7xlD3+PnX4uJLRHgRHA9+DbhOuggNLCoaj10OVyMLFOdAkDUq0wXy1eXzsqkAobUTZBrtoHi7g3HAkmA20AZ6ahdwgEQMehG72AGEfAGyAkeXSA7oKwOB6yEwnaZjrjrP78C15BX5+8qsj282BsIC3Bd/A0xRi8K/3sk8o8IVYGswQ7OV2OUxAzViwEJDjYh0MmbADLR/2lJiwwBb2JsXvyoCUwxwRmp0+nAHZdEnEwfXKEEnM2AMhFX5H1nPIHwCVqwyGwx1L3BNd7FTCVHvv0bulBMasgfRsCwpNDBUuPBeNmLiJ68x/krJ9qLJ9t24XI9xn4QLKt+FWUC1pvR03C5lDvh5sr94dEaZ6A7uOwPxU4BX87do9BSjX8K1oNrrs++ndwpmoGEYCFNyb2wOLgF6jquuITwODgHLAD1rbWbADBQKFhp8FZgBM1AzBv5HSkJfbU8S2KwoEc0ZZ7hvXMjuV7ilelGHEs5xa95Gpy0v/sXV+CplCxKoRo7mrctfzqqNV+54hc8MmMpRcti79stmAseDQdrImHqSL4bSjai4qCKzcWafvQ3LQGBxxPj5y9G4uqYrmXrK+O/jVxuy8dZmQ1MmFksCeyI0HJRNCP+d4JmiMG3qntO503tx62T7Ztyfg/mBpjh8BZ4A44JqjF7AuA7GZGUiz0h4qTKXie7g2jEQZoX6Y4F6a/XMeRJsBfRstZkBM9CJAX2hIvAsDDxT45onumeET8E/wRZgmk6HeMMMtBYDFhpa6/92ac1AXRmohdCg4dgfgrQRIkHhUMDLu/AyeDXxX4KbNTXoRwHiTfkuI7IVX7gGZHsXJC6MBm+DR4Di3AioMHSyauN1OqhoQ2neC3SOhYv2pZvj43kMKE6phtrtZF9DNt8Dd6UH2W10BjTaJtwK+N/DhaDceiKrUlJdGyMzJVbFVSMK9smE9UVo+DfpvJhJK/UujUfn3jQJmBhXwkbxyJpTCFO8dUB3Ni0RfgBnVIio+0b3qO5724AwoGlnYSfA8zZeozyTw9/BdAOSHZ/UDDQEA/Gzxltxn1wOPgd6vv8IqGtEMcKjHRrif3Qma8jAA6Ql2MyAGTADfWagr0KDRAV60Ap7ZXKyFn41YjTKQaZe/78AhS0CUmMUQ1ytnl7YsCNtGfbPchhhirdzEmkpXF76havAkCRsCdyPwTHJtpxq42UOKelVQ1DnF0oJDfRsFx7KxCklNCgv3xQKDx9HMqQT5mHb1hQMxN6wo5L/FbEpDC9RrG0J0/WTrpOgKBcD3Wtc5wXtF9RwVzyNntkMlDIJbopzUNHO09j+qChMm+m9t2aJfdmgZdlQuntmA8v4JeApbqn7IXuIxEZ6BG0Dy0BcyPSX/GV3Av638D2QMKbnks0MmIGyDGixyDASUA+JI4N0/wgSIBjVFbYBM5U93DvMQHMwIJFBsJkBM2AG+sxAX4UGNWjGgOkzObkX/51AIkRq6vE8FayfBKinVT2gl7Rvhz/wAueF/tz8bKsBdX17eOFEXMKjkJAERed0fiVATJ0EVhsviV7S+SmhVMrLfg1gC/Z9B14G1wDli4pJSXuuUJiGPMb50wgOtuZiIC7I9zX/r3qNVygqWzpSIdvYv584ul7K4Y2iNNLNckLDvkTQfaf7KGt/ZEPnmCUJXBq3lIihkQyKt30Sr5LzNDsfrhSBfRMCpaeRErbcMBAW4m85GyB8xgbTo7jqvS03Gic3OXdGzMDAMxBHO/yO+wUBtdPaDs+y/Q+wKhg68Pl0DsxATRmw0FBTOp2YGWhtBvoqNNwEfXdnKJwEvxocCAfRBvE7aeLPOgofBqZqD4zfwua469UwolLc8Tm+m/G/C7KiBZtRsNB56LmLVm28JHoXZwJCXgTHg7ShWNyDSy9HXP1/osTV+bNTPNjssAPwfVEofHkDdHwGxu/YY0+TMBC4PsIogGCmIesd9nN8ujZ27Qgp7Sl3nWVjlxMaJMjpHFSCO5nuxWcyIcfgV7yVM2HySuCTUKd0KpmmXFC+wkmVIrFvUaDz7NNNPO8eEAbiYni78xeNAvxPsdF0JO4sA5Idn9QMNBwDcTTbktwz+wEaYh1fsvgGP/WgsAvo7nnacKV2hluSAQsNLfm3u9BmoD4M9FVooBHdaXV9TRNQg2M1cBrQfm0/B0aCMhbodVUFeNGjiKD4v0kino+rntsZk+3USeMxlDFatfHS44vdUwlQHtXT92ugPBQLDQR12IH4FKec0JA0No/6U3u51Itoaz4GYgPutuQ/vgBXgtVQ8CU4o5vyVnOdlRMalPSN4GOwFhgBDge6V5YCqc2FR3mR+LAB0LDfncH3IJs/jXC4EywPsjYfG7rOd8gGlvBvmsRbpsQ+B+WGgTitYm3+0psBQlNsLF2HuyYYJzfZdEbMQO4ZiGv2bMR9czZ4C/CcjHgH9xywGZgu98VwBs1AVwYsNHTlxCFmwAz0koG+CA0zcE41QjbPnHuVJOxFXA253g7sCXgRxwXl5sctYYFG1x2k1fYdO2/JRJDgoHNo+sGQJHw47htA4Wo0yaqN1x678y8V7x5/DeBAjtH5y02dmLF9/zjkK/YiPs62rSkZiD1dGkbL9RA0pHZeinkl+G83xe2r0DA56dNIjCMTdC0+BbYFxbYcAeQrXq+Kp3v+IDAIpKb7VPuUp6xtyIbClUYlk8jxEXBjtRJLudoX5uCvPRYgBsdr903c/cGIXGXTmTEDDcFA/DIRI9sCAnD4GvDc7HgnHI//l2CyhiiKM9nqDFhoaPUrwOU3AzVkoC9CA/MTYyNkZCY/9IzFsE9wU2FAu9WzqgbLZdroatsewmAC9k/8AvuyL+M2tq8HOvYDQE9cHMp9chK2Da6s2njtscf+ToNX6e4zNqhHIxrKCQ1qcI0Gh5J1xJBY4Vghcw57m46BsB7/sxptX7FOKddzvGYX74diTsw5RlRxnmmJQ+Oyk8BQxWHdRtEoIN1De3Ub0xFyyICmdcWpa/fjqnH0I0DsDeqtzT7Dc5h3Z8kM5JEB3TdheUCHRNB99QPQvcWIs/AwOAKsDibMY+6dp5ZnwEJDy18CJsAM1I6BvggN25INCQDzZbKzcBImIaDYniTgpeJAttWbygt4JdJ6QqMLik09rzrXpeA0oKHiiwKde32QWrXx0vhyLwbiYEegcwgaUq60DwYMfyxp3Y1o0EEfgn+SFA3B+I179T7bmpqBMDP/9UOA62cFeojHu7api9teuB1wRoGh7Zv+bVwGYq/sP7h+eXbFhhFTc8JxYMHGLZNzbgYGmoFYB1iT+0j3FqMbo5jHOyIKEPfhSpCgI8ILSw70P+XzRwYe4FewmQEzYAb6zEBfhAYNs1aDnBdoh02FT2FHdoSM9TyMV8ja7mwQfzaGG36vF+8vsjsTv0YrFNtWBOg8NOw6rNp4HQfgobchpqO0SuGNbOSMvzuhYcIkvVPaj4k9GCqfBBJbUzMQP5HG1ITXEc/GA2ds2cTF/Qllew1oioWtaRgIg3lWIeKGG4B6YfXs4tkdEIWDFvy1mQEz0GsG4voO63EvnQieBbq/hG/BHWBfsBwY0utT+EAz0HsGLDT0njsfaQbMQBEDfREafk5avBy7rK5/N2HPgOxLcgTbinsMSO0PeBR2CI5eqvg1j7GTafQCww4LC2VCJSjcnyANrjZeGr+SmwooGp1RzroTGiQoqGz7tCcQpmbzK3BNuQQd3mwMBO6Pf3xUKJynoejqFW7GHn/dI1SWbc3LQBjGtctzLLwK9Iz+GlwAVgKDmrfcLpkZ6C8GwrTcS5uA08HLQPeZ8A24A+wLlgPZOlV/Zc7naT0GLDS03n/uEpuBujHQF6FBDSetaH9GUe5WZJvGVeEKoIbIGuBBQKOrMCeQ0fCOX6RgaK7mdi98MuvT8WJd6SK2tXikRizIVJHVug0SL5YC84PzwBdgVpBatfHKra6fpiO3FkLDpqRDeQrLKMF2C0cSRJhHNaSMNL8bh8xyvcb/HfEtZAWz5i++S9hEDAQE3sCzPfCMjg0gPcveBAeDuZqooC6KGRhgBqK4tzn31ZngFaB7TZDwcDv4P7AssPAwwP9Uk57eQkOT/rEulhkYCAb6IjQov+VW11+dfVRCY2NbosPjYAmQ2pZ4eHGWxWNpRNwVQJqWRjfwoi0o/WKrJh5Df+M5JSaUs1oIDYeTuISVccaepGNUw7Vjw+xrDQbChlx2nwC+qhL+Cga1RrldyuZkQNMnwjbgXpA2gqicxqkVkzVnmV0qMzBQDJQVHjTV4i5wANAIowkGKoc+b1MxYKGhqf5OF8YMDCwDfRUalib7EgwWL1OMGQnX6IVuLCxAMqqwblkmohrss4LuKrHVxitzmpoEj0cqHwBGahRbx1oNixXv8XazMxC4F8JtyXV+B+6wZi+xy9cKDITZuJb/DkYl17YaP5eBNcFPWoEBl9EM9C8DHcIDo0nDiyAV+77Hz7TScChgvSuNqLOZgR4zYKGhx5T5ADNgBsox0FehQeleAv5V7gTVhWvobXxZ/qG6+LmOtQO5GwWGds1lmIpyMt0k/LvrPoc0PwNx+Pku/P9qjH0KNm7+MruErcFAvLZHck2fDXivxOf5+7jHAgurrXERuJQDwkCYjnuMkZjhJPA0YBRpvP/G4D4CjgbrAuofvbEwN8eeCq4HJwLfz72hsXGOsdDQOP+Vc2oGcs9ALYSGmSjll2CO3pc2zMLLS6r8H3ufRi6OVA/ea4Ch8uUsMK0ilrXcKJByBzq8aRiII3ieSq6Dy3GnaZqiuSBmIA7hDptzXd8C1NjRs/15sDfQyDSbGTADdWMgTMl99isggeFRkN6Dug+fA6cB3Z/Uu7oz3a/hM/AfsC+gYymMBpt0d6T3NywDFhoa9q9zxs1A/hiohdCgUmntA15cvbU4rFwvwV17m0JOjluYfKD4V7KOUQ1Uwm2ty4AW8gqHJZU2rd/we1DqE62tS5FL3gQMhOm5rnmuB9bpiYKDnvP/TcIkUtvMgBmoKwNxUeLVuOe0cKsEA42o030ovAsYkRqnW6zYNRthR/a9BliXJbVwDNvvgEHtIZoiFXYBGlFxJBiRxrTbkAxYaGjIv82ZNgP5ZKBWQkMfSxcb33rp7dnHhBrk8NgzoPKu0SAZdjbrxoCGocZeJ10PWlxvvrqdygmbgQFlIMzL9X0geBHoetcQ7/vAn8EMA5o1n9wMtAwD+tRyWAbsAfjkdvgQ6H7U17xKWCoopLu0llaMPy6uIKHiabADuBt8Doanse02HAMWGhruL3OGzUB+GciL0IBaHl9c++eXqlrmLIxPed8CGsao6Ra2lmYgjMN1sBPgfgzfg0PAeC1NiQvf5AyERbjGNaLnNZCKDhLa/gKGNXnhXTwzkDMGwlLcd4t2n6k4Eu9Z4iIQyuJIBr23kns27pfYwJoOWYuLwx5F+JmgxMiJbFz7B5gBCw0D/Af49GagmRjIi9AghV2VzUObidzKZQlbJGWmF8BmBsRAnEJ0ZXJdvIq7unkxA83PQPgZ1/oRICs6UNmNjZgRzV9+l9AMNAIDHV9O+ph7U9NEsfAKOLbdn/7GEZv/TbfYr7qORjnsC44GAWwzdr98FiI68zGgWxYaBpR+n9wMNBcDeREamJseXz7/aC56K5UmrtL+COX+EDCiw2YGUgbC2lwTbyT3xKW4rCpuMwOtwECcSoTgHF5Orn81Smi0hN3ALK3AgMtoBvLFQByBuR/335fgQTBXe/7C7Ph1f67cOb9xetTDSZyZ2a/FIzPCQlyzhXWJUqtGiEjj2u0HBiw09APJPoUZaBUGciI0iO44ZLybhRSb7W8Jy1FuvajpzbOZgSwDYUKuCw01VSVNvUE7A+bD2sxAqzCgXtNwEHgB6DkpPAb2AV7LpFUuA5dzABmIX694nftNwvdvAJ1CqYUN2Nb7iYWNsxb+SdgV7SFxGuDbRcfNw7bu5RGgCiEim7b9/cCAhYZ+INmnMAOtwkCehAap5We0CvFjyxk0VP474N66saTY18FAWIhr416gipnW9FitY5c9ZqBlGIifhD2A61+LzqWiw0v49bngJUCmAdQypLigZqCODMSRCT9wb+0NhnY9URS/meJXbEH35cHtofFrM6d1jhEWZL/uYYkMWo+oghDR+Uhv9QsDFhr6hWafxAy0BgN5Eho07++81qA9W8owG+XWAoCXZUPtNwNjGYjTbDZLKmSqoGmlcK4bmxloRQbikO3duQeoEMcvV+ieeAfo83org3FbkRWX2QzUjoGwFveR7qs3gcS89UDRl2Hi5zIRwbPWMZ1Co5G0yLFGPGycjcG20hoDuE+7EyI6H+mtfmHAQkO/0OyTmIHWYCBPQoMqigy5a0XrWCDp561Yepe5WgbidAr1AOk76BoFcyhgioXNDLQqA2F67oHtwS1Ava9qHH0KzgfrA98frXppuNx9YCB+cnxd7h+JDHcBjTjVvaWvZe3QnnCQ+K0pFZnRRFF8YOSdLC4eqWMWa99Of2McjXqoQohIj7HbjwxYaOhHsn0qM9DsDORJaBjFi4dpBK1oYTLKrhEdD4HMS7sVuXCZu2cgzMp1cjVQJU4C3ebA1033xDlGUzMQn6OaR67paF8B3R8S5a4HfwCIEjYzYAZ6zkAUBTTlQffRcu3Ha4RD+AJcDCYA64BvQDKCIa6xonuw6L6LoxiYWtGdEFGcy7AGx2wJFgCIFLY6MWChoU7EOlkz0IoM5EloeIGXBxXCVrXwZ8qvl/IWrcqAy91TBsIqXC/PJteNRKqRPU3B8c1AczIQxuN+0NdbzgTvAz1bfwSPgL8DreswqDnL7lKZgf5iIKzIfaR1UzRF4m2AEJBa/FqFwpdMQ/AvAnQvsphrd0LE2KPafeGe5Fgd/zWgQRxOBr8Hi4IhxUd4u1cMWGjoFW0+yAyYgVIMDITQMDUZKdH7Gp7gRXF7qUy2Rlicr6gX9gdgytYos0vZdwbCT7heJFJ9BFQBuxEs3vd0nYIZaBYGJCiEpcCh4Cmg+0T4EFwANgVTNUtpXQ4z0P8MlBPt4vvoFe6vOcEc4HHwr/b8dSdEFJciDOZY3m1xqtQZuI+C70F6P8v/GJC4uCNYGnjqVDGN3W9baOieI8cwA2agSgb6W2jYgHz9AM4DRb1JcdrAfVXmu0mjSfmPPQPJp6GatJguVh0YCJNw7RwIuKdjxesq3PnrcCInaQYanIEwjHtDQ8C1qOqXQA0VjXbQqKADgEQJD81u8H/Z2c8DA3GtB72LxoBPwEWARSBTqyREpHEquVF80CiJrcGJ4H6g0Q6p+KD7WqNlLwF7gFWBOrts5Rmw0FCeG+8xA2aghwz0p9AgkWE0ixHw4C/wEiicDzJiQ7ibYIa1trrFhZL0kvxtqzPh8veGgVixO4rrR3NlVcm6GNCTZDMDZqArAxpuHVYCRwKNKEsbKGoUsThx+B2YvutxDjEDZqB6BsoJd90JEdWfYWzMuJ7EvNy3Gqmk+/o28DFI7225mubxbyBxfj0wGygx0nZsqi3ks9DQQn+2i2oG6s1AfwkNUWSYt9A25t3C4PCnwjg86IvFhnAzQc/Uu8D5Tz9OodCQwM/BiPzn1znMJwNxNf6TuIY0nFTzZM/y9ZTPf8q5yhMDcbSD5nz/C+gZnDZONLXvcCBRYmiecuy8mIHGZ6CcEFHLkoXh3LvrgP3A1eANkN7fcrWw5T3gBKBnwOKAtV5aziw0tNxf7gKbgfox0B9CQ4fI8B4iww+FIRFdxYZwLQ/1V+pX1EZKOUiNV4/0XSAz6qORyuC85oMBiVXhbCCxQaKDxAf30Objz3Eucs1AXP9kWe6Xg4HE3x+BGiQamq21UHYB8+e6CM6cGTADFRgIU3APrwB2BueBJ4Hek6kAofcmHWBxysduuKuB6Sok2Ay7LDQ0w7/oMpiBnDBQb6Fhfco5WiMZsiJDabFh9GU8wN/KCS85yEbYCT70svtrDjLjLDQ8A3ExLk2jUGNJItbRwIJDw/+vLkD/MRCHeW/CfSPhTkOv08bIO/jPBRqqPU3/5cdnMgNmoPYMxHUfFuZe1rSpY8Gd4FOQ3u9ytWj3LUDTFH8DFgSDa5+XAUnRQsOA0O6TmoHmZKCeQkNFkaGr2LDSq4XCmI+ak+belErzBePcwu9wF+hNCj7GDHRlQD2w4UogweFboBEOw7vGc4gZMAOVGYgjzyQI3wC+AmqA6L7SyvpqgKwOJqichveaATPQGAzEqRdrc0/vA9Qx9iLQIpepAKGREBoRwfpj6iCKC082ophvoaExLkjn0gw0BAP1EhqqEhm6ig0b86DOLhDZEBzWMZNxvvBnvLCeAM2ilteRLyddPQMSr+Jid6ooqYJ0Fpi9+uMd0wyYgbEMxEUlNQT7UPAISBsgurfuBvuCpcG4Y4+xzwyYgcZmQEJiWAJsA04EWuPhc5CKD3LpQNOn28MxYCuwOBg/x+W20JDjP8dZMwONxkA9hAYNHf1hMr4uoYUfUzGhO3e9wiAeyHGByG0bjcT65jdsBi16WbEQmc0M1JqBMBfXluam/gA0H/VCME+tz+L0zEBrMRAm5z5aD5wMXgRpw4N3brgOaCSExD5GrtnMgBloHgbiaNQR3Nsa/bA30NdrngN6v6bPAQmRei5cDvYF6wKE/lysyWWhoXkuRpfEDAw4A/UQGlRxorGimtQ4VQkNtxTGZRnvth8HFYbxEJ50+ICzkrsMhEuhUy+m5XOXNWeoSRgIs3B9nQY0VUfDv68ASzVJ4VwMMzDADISZuJ9+ByTkvQvSBseH+NXY+BNYCAwa4Iz69GbADNSFAX2tJiwKfgv+AW4F74P0WSBXC80+DM4Fu4M1wQx1yU75RC00lOfGe8yAGeghA/UQGpSFcUBVYkO7yFAYM6gwKVMERulB6zmtXf7E2Dv2OtzopdTfL50uuXFAMzMQp+scx3X2FdD9eD/YCIzbzKV22cxA/zIQ13eQuHAV+BikjQ0tOvdvoBXuNSTb913//jE+mxnoZwbC1NznK4K/gDPBg+ALkD4Tvsc/UT9mykJDP5LtU5mBZmegXkKDeOtWbBgrMhRoRN93RPJgnbLZSe9d+eK8Pi3edx9w5bN3JPqoqhmI4tZeXGvvJPfl27gaBjpV1Uk4ohkwA1UwEIYM498AAEAASURBVIdaz8+9tQPQMOv0nlNDQ4Kfej73BWqMWIivglFHMQONzUB8JmgU1GqAaRX9ahYa+pVun8wMNDcD9RQaxFxZsaGzyFAYzsN0Z6CK1bDmprwvpYsLDomjE/qSio81A9UzIFErbALUy6JrT2LX2WDh6tNwTDNgBnrGQJyvvRX3mYZQvwp07wlaS+W/4GhAA8Sf0+wZr45tBsxANwxYaOiGIO82A2agegbqLTQoJ13Ehq4ig6KF7YAqUnNoy1aOgfh1APG0WbkYDjcD9WEg/Izr7kKgoZy6BrWaPl+YCbrHbWbADNSNAU2ZC78Gx4PHgNbs0T0ovAjOAluCOeuWBSdsBsxAKzBgoaEV/mWX0Qz0EwP9ITSoKGqIXATCBnxdYmhBazJouoRGMqQWfstuVZoWTEPslmIgLiikz6dp0SD3KpeiyGF1ZiBMx7V3AHgf6J7V+iFauMrTnurMvJM3A+0MhIm531YFB4I7gd4HuheFj8A1YA+wLBjPrJkBM2AGqmTAQkOVRDmaGTAD3TPQX0KDctIxsqGryKDdccE5VZJYAMtWmYEwAp4+BJrLO1PluN5rBurFQBjC9bcFkPCle1dfrEBQVOPGZgbMQP8xEKc48e4Mu4B/gXeB7klBI5A09UnTLTQCafr+y5fPZAbMQIMxYKGhwf4wZ9cM5JmB/hQaxIPEhu1BiXUYwjpUgFQpWl4Rbd0xEFckVy/W02CS7mJ7vxmoLwNhSa5DzSf/Bug+fhZoVf1J63tep24GzEBpBuIna5liF04Cj4PsdItRbF8C/gw0JWpw6TQcagbMQIsxYKGhxf5wF9cM1JOB/hYaKpQlrEJlRw2U1StE8q5ODIRfwZcqj7cBf4miEzfeGBgGwmRci/pM13NA97PEsLMBjRmbGTADA8eAPpEXVgb7gpvAp0D3qKBFXu8DR4ENwIwDl0+f2QyYgQFkwELDAJLvU5uBZmMgT0LDMlRuVOHp70/5NPh/GnukxNt5DV4QZ7/pGAgjuS7Va6opFbpGHwPbAho8NjNgBgaWgfgJvXm4H7cEp4OngIRr3avCW+AysCvQ+3n8gc2vz24GzEA/MGChoR9I9inMQKswkCehYTEqMqrcbNoq5NeunOGYhDsWBrOZgbwxEKbm+twdvJJcp1/ing2YJhVYssVmBsxAPhiIi0yuxH25N7gOaGHJVHjQpzUlFp4CtgYLAY+ky8cf51yYgVoxYKGhVkw6HTNgBgp5EhrUs6IKze/9v/SUATXW4gJg4u9vPT3a8c1A/zAQe1A1RepSkI5yeBP/oWC+/smDz2IGzEDPGAizcX/SARCOA1pUMr139b75Jgk7EXcrYPGhZ+Q6thnIGwMWGvL2jzg/ZqCBGciT0DCCSooqLszvtvWcAS3mFf6dcMhQV5sZyDMDcS2HP3C93gN+TK7bx3G5dr0qfp7/Oeet1RmI75pFuU91/2rKxaNAX7bQ+1vQeg//BacCxVkMDGl11lx+M9AgDFhoaJA/ytk0A43AQJ6EhqmojKiSsmcjEJfPPMbPDd6Y8GjBJp9/knPVhYEwM9eshmo/n1y7mid+C/gdmLRLdAeYATOQMwbiu0eCwjZAAoOEBgkOqfggIULTLs4EO4ClwAQ5K4SzYwbMQKFgocFXgRkwAzVjIE9CA5WOWCk5qGala8mEwlB4vDXhkgqdzQw0EgNhca7dY8H7yTWsYdpXg03AhI1UEufVDLQ2A1q/IU6l0JQKTa24H+grNKn4IEFR4uLFYDewMqDDwWYGzMAAMmChYQDJ96nNQLMxkCehoY1KhiogRzcbyf1fHq0OHu4AGpLO0FWbGWg0BsI4XLtqeKgH9BOgZ4MaKZeB9cB4jVYi59cMmIF4X8/L/bsZ0Kc0bwfp/Z0KEFq35RpwAOArVGFWQP3AZgbMQD8wYKGhH0j2KcxAqzCQI6FBlMeGxGmtQn59y6lhqeE/wGJDfYl26nVnIM4JX5Nr+XzwBVCDhGdXuACsBVifxGYGzEDjMhBGcB//CkhcuBa8BVLhQa7ue73PTgBbA418stjYuH+4c55fBiw05Pe/cc7MQMMxkDeh4UMqDzQebLVhIEwEn6qcqaK2V23SdCpmYCAZiFOD1MupL1d8lVzbn+FKhPglcONjIP8en9sM1IyBMCX384pgF6D7+0nwA0gFiNH4nwUXgT3AL8AMNTu9EzIDrcmAhYbW/N9dajNQFwbyJjS8TkXhyrqUtGUTVcMr9hCpcqa57x6C2rLXQrMVPI7a2Zhr+gqQzv3+Er+mV2wEENpsZsAMNA8DcdHJRbi3fwf0PrsTfApS8UHux+AOoP1bAi1SaQGyeS4Cl6S+DFhoqC+/Tt0MtBQDeRMa1DtxU0v9A/1S2PATeD0HqBKm3h8W6bKZgWZiIK5Lsl5yfX+eXOta9f4asAWYrJlK67KYATOQZSAM4x7X9Ko9wSXgOaARD3rnCfIrTCOh9gEa/TQrsPCepdF+M+CvTvgaMANmoIYM5E1oeJgX/z01LJ+T6mBAFapwBFCl60bgFfw7uLGnuRiIvZ5qdJwF1Lupa15Drm8G24OZmqu8Lo0ZMANdGYjTrBblfteohmPA7UDTM1PxQa5GQNGDG04HfwIrgGm7puUQM9AyDHhEQ8v81S6oGag/A3kTGu7iJc+3tm31YyDsCsc/As13nbl+53HKZiAPDMTRPCtxrZ8M3gVpI+Np/IeD5YFH+OThr3IezEC/MCAhIX7RZhfcc8Aj4BuQPhvkajrGfeAMoHha/4H3pUdA9Mtf5JMMJAMWGnrJflsvj/NhZqCZGZDQIMvJXObwb/IyZ6HQNnvMlX/qxEDYgITPBgwtL/wavm+r04mcrBnIEQOxkfBTMrQGWB0sClQ30HPwFnA9YLRP29u4NjNgBlqGAX12szAbmBfMA+ZOXOojhez6DqwFU3gBMA2jA8/jf4nnxve4NjPQ6AxIaJAt0e74t1oGLDRUy5TjtRIDeRMaLoH8kbywp22lP2FgyhoW5LxXgRkBoxzaWDjLZgZaiYEwDaVdDUh0WAWk6zg8gR/BocBUi8K93Bvf4drMgBloOQaiODmCYqfCg9wUk2boGIP/VSDRIYXECPxtjI6wmYGGYcBCQy//KgsNvSTOhzU1A3kTGpgnWdiEF/METc16bgqnz4cVLgMjwTlgWzeqYMHWggzEHs0lKbhEB4ZJFyTEqd6gHsy7gEQH0PYsrs0MmIGWZyBMBwVzZSABQtsS77Ntjg/YlvigkRAptP0a0SRQ2MxAnhiw0NDLfyN70/cyCR9mBpqOgbwJDf+A4Z0AwxjbWEfAVn8GNIe9cDTYEfwXbAz3r+DazEALMxCmpvAa5SCsDKYFsrdAIjoUbuVeYbFJmxkwA2YgZUCf3dUU0Cg6pOKDtmcHQ0Fq3+PRuzYVH+RKgHjRzxVYsA0UAxYaesm8hYZeEufDmpqBvAkNB8D2PmASXrRfNDXzuSucvkteOAGoh2V7+L8gd1l0hszAgDEQFuDUqfCwDH41GCSGPgZuS3AP941GQNjMgBkwA0UMhEEEDAcSHVLMlfinx83aJ2wgOEQRQm4KrQXxTTai/WagxgxYaOgloRYaekmcD2tqBvImNPwVtg8HDD2sy4JsM5G2zvFnUOsRE/pc5J6gO/uQCBpB0J+GcFPYvZsTfseK2v8kziVgQXA+2IH/Ib1G2LSZATPAfTIeLCwLJDysCCRCqI7xPbgf3A4kPjxE8A+4NjNgBsxABQaCFuSeA0iAyLoaBZFdrJsvYsRRVRr9gOgQIRFCfk3F8PMGImx9YsBCQ5/o88FmwAxkGVAjMkcNybAtlXhepEEv21rb+CT4GNCLetxaJ056U4GUz9TVuUYXhT/Mdn/bME6ovKghlOat2H27PVNhCFFZGDL+D1RewmLt4f41A2agNAOBez9sCE4FLwM9wwTusXAd2BksBNSjaTMDZsAM9IABrQURlgO/B0eCawBTLALv845njZ43iAzx+XM9rt7hTIcMq4JZgL6qYTMD1TAgoSEVG6qJ7zhmwAyYgbIMpI3NshH6d0fYlBeiXpi1btzOSznoXYyNbdKvi9BQiioNo7661I5+DkuFBo0WqdLCGtDF6ItYmWEUiL8fXiVxjtbyDITh3C9bAqYfhXeBnmkCw6EDX3oJfwEWHlr+OjEBZqAvDEg8CLOC1cAfwXHgRvAKoIOj47mjZ49ECUZBROHzGFyJEDpOx/+kL7nwsU3HgIWGpvtLXSAzMHAM5E1oWIeXnl6Ky9eQki1I6ztAT2OBnoCqRjRMQLwpwGCQNQ2ZLhWejZP1lxMaJiPShNmI+NO0i3s9dT7ty5qmQiwB5gbV9FT0QmjQ6c6fjRHgD7JGJP/JhwwHD9MqNGOT4lfexNOiQCNF5FeeZSqLhpXPAyrlU/lbCkwOiq3UOdI4jL4oLAx+BiZOA+2agXwxELj+ww7gMvAB0DNOkPCAEBl2AlzHHvGQr//NuTEDjcpA4D2skaFhLaDny0ngZvAqKBYhNBLiRaCREMeDP4M1wFxA71hbazFgoaG1/m+X1gzUlYH/kbqQEwsr8GJTBXztGmboMNI6EGieo1zSL3Sn4C9NHF7GhctBakPxPA3eBKUaxGm8rFtOaLiXSA9nI+I/CyhvK2XC1UBX2LpJmF76xwEqBjFc+94H6X68Ja2nQsOUpEKjqOMcOs+PhcJvWYTq+99kzkDPSVxv4skk7mu4GyX+VXE/Svw6ngpOgV7cTjYfW48D7U9xAX4JKamVOofmw1OBKnwI0uPkngsmADYzkFMGNDIozAt2AJeBrPDwOdtqDOwPVgOT5bQQzpYZMAMNy0CHCLEGz5i/gBPATYB3bZx+wbu0QwzlvR+YVhnuAeeDA8DvwEgwAnRXl2pYllo44xYaWvjPd9HNQK0ZyJvQsCgvLr3kNq91QZP0DsRVg7Sal2Mad/3k2ONxJT4sk2xX45QTGrQwIy/wuK5Dmo4EDOXt4DQAdzdA476j8XwEfokM9DgU1AiZBVwJlNbPQTkbxg6lTUWh8NMyYB5oh/0Tn877SzA54JhpbsUljatBrJTMwfYrQPFuAFsBKiAdQoMEEKZcFGYFGwBx8R+Q2qR4aGQVngG/BhqRIPFA4sSlILVS55Dg8iW4GswFlMedgMoo12YGGoSBDuFhey7fC8FrQPeYoEr+s+As8HuAMOdRDw3yxzqbZqABGZBwEGYDEjolhh4FrgCPgc9A+mxKXepE8Zl1B+7Z4P/Ab8CygHqH14ZowIvAQkMD/mnOshnIKwN5ExpolMYX2R/rRFgqHoxbRfq8cOO6Du/ibgx4sRZ4ifbI1LhWY7jY5iZA6SldWbqthrge8qndheeaZEPTFsaAE5Pt1BmK502QbcSn+1KXF348n85ZDnskkcfBpYejsGOynTpJHjdVzwfiguZ8TvEpO4Xx00i46YgGiQxZO5+N7zIB9NrGvKySCZN37yR8gSRcQkPxOWZO4uyWxEmdLfEskW7YNQONyYCmKIVfgcPB3YDnSEcF/3P8twD1LKpHcqrGLKNzbQbMQOMxoFFWcZrXurg7AzpgwrXgKUB9suM5RT0j+ukYidM1bsc9G+wLtgAjwQigelY/WlzXgrpFmKT8SePivnOW39/0eyw09PIv7ueLuZe59GFmoLUZQDGPpt7ugTaU+sJm4DFwEbgTSKiohT1HIi8BTS+4GKix/QY4B5wK1LuvBv/SYBsgmxcMArzUO9m3bDHcurBhp9DSG/8kWOmXsleTQIkZqcgwI/55gF66ygt24f2FwgXb4qGnYxIaQyMQHB7Wuc/l01pULjrs4Q5fu+dNnMFAIg+Vj8JigAZUYTYwC0htosSzEO6Tif95XMVN7TU8Sv8wsBbQiArhLGAzAw3OQNt7FODKBDhB98yCYEkgIU3uSiAx9ShGUfQh3AfBo9yLiIE2M2AGzEAtGWj7lNSEx0qnGqYkfARQZ4BcYXjiLo47Icga9Q1NzSi8XgKqExFeq2eZRI44yhJBpEAdUyJJ2274EwvT4EEQKcil/hVG4a5OHD2PMxaGsqE60bPsU13GZgYiAxYafCGYgfwzQG9dtElyktUXyQeV9jgt4TbcH2uYL41U2CRJb2Vcpa+XnASGZYEa3G0gFRZmwi97v93p9PsBWxOA8UClBoZe3HeA7mwFImhUw9xAgot4QGBIrU0NG3o0xqOCMIcqDmeD7Xkx/7E9yzGeKiNZUzqyQe1OYVji3ynZzjovs5GtkLyT3Zn4lcf9wAbgoATP4W4MngA2M9AkDMTKrIQ1gcqxLFaKf4pH+BlYFfwayLjXwtO4qfDwX/yqFFOpt5kBM2AG6sVA20ekLOhZVcLCFASOAMNLuGsSVqKTSV+/ikKE6g4SJP4D1HGRAiGgrZu6maZyFPYBPCfbqGvFL5vdi0tnRtsFuGojXgFUh1Anh871L3Au0LM1sXA6no3AFwDBIWzF8VclOxPHQkRnPrxlBsxAKzPwPwov5MjClzy8z6hThg4k3QDGrTL97ZL4amR/B/QC6ol9TeSryxygF5/ysgjQf7ApkL0C/gEuBHeD1FbCo/i/SAMyrl6GapyXs2Hs0LGHl4uQCZ8aP/9B4RGgPEq8kM0MlMb+2kiMvLbxMo6f1voEl5f9EXexT/EWTiMl7gFJ+JBk+wZciRHqHahk4uPyShHYNw/YDXwFVFGwmYEWYyCu9TAHt95m4HjwIOCZ1TGUmWdRUMX6WLA5QET0eg8tdpG4uGYg5wyEiXkuzQ/WANuDw8AlgDpYnEJGHaPjmUY9o2N6xij8CBDhIvCnroWMX+BIhdhkd3xGntS+ERgpFsYA1XMSC8uxLdF28vaAsCt+RA19vlgWtgHUOcJ07dsxDCEiTiF5G/dj8Mux+xrG9wA5FWxmwAyYgT4zkEeh4S0ezt01LHtb8J4IDXNyEjVcLwYaLfAaUC/hUFCtUbkvKzSMw76PAA312DCfFld2KtB5aLgXdgapTYNHYsfZaUDiToj7AbisKDy7OYyNAKoRGtZO4tJg6WRbJOEHZUIzIoCGTIZTmGXCy1rnuuQYnMGZuMVCw9+T9DbMxJF3W/A8mE8bWOYc7QH8Lg4krKzWEdLuOR/nWzBuUbg3zUALMhBXl+deiRX2s3CfBKo4c39G8PwPCINxwTdGVwUJFW0tSJSLbAbMQEMwoGlkEgPiYpN6Zu0OTgB06IRHAaMfguoP3VhYmnjUVcKW7RFjOkWdFBIQ4nNS9Q0svAl2bfenv+F9wrZu36pGiEiPy7UrkUGwmQEzYAb6zEAehQYa2eHWPpesdALVCg1qqD4MeIkUaEBHW4FfNdbpEazaKgkNSuRcoDQpc4dtgE9hwiwdoe2eQ3DUkN8PaN+iQFx9DpKXIb6uNowgpXc32LMCZmSfRgfQa1CgAVJYAEit3xJ8BXTubPlfYftykLEVePHrXI+C+G1uyhMbLwe0hxeGJJEnxRW/bwMdQyMnfrXiS9xLQWolzhGnlOj/eRasD6h4xPU0xMPVwGYGzEBJBsL43I9LgT+B8wD3UKxw634VvgA8J4KEwt8A9S7+pGRSDjQDZsAMNBQDYS2eZ68DjYzYb+yzLfwb/0WdixJmIUzPxIUAdYzoTztAkqjxs59JB053QkTn1HO8ZaEhx3+Os2YGGo2BPAoN9/BA/2+diKxWaDiI8/OCKaxXlA967GMjfJWi8HKb3QkNSl/nyTbep2BbDXp6H7uYKvx7AzXGddwPQC+FJUElG8ZOxe8OqPzRtuFXiz2l8ZWXZYHEB4WnVkoE2IidHHfyHjijAP7wCFPJL2wP7xAalMbs4E4gYUPnkuhwGpgapFbqHNonYUVCQ5pHjfb4J5gI2MyAGaiagTAhtxH3d9gJMCpI6zt0GvnwDdsPgpPB78FiYGjVyTuiGTADZiAXDMQpFFvw/HoeXAMmac+W6pyappG1sAhhqr8wujV+KUPPwSLRVXWbcDCoQojIpp1rv4WGXP89zpwZaCwG8ig0XMdD+6XGorHfczuIM84K6t2olkAxee9LF4duq+eUkQvxhY1QoSGLXWwCQmbrEtp9QBtRpgVzg3G7j+4YZsAMVMdAGI97dQmwPTgDqEKNmBfvY1W+ETmjICFhYhewIpBIajMDZsAM5JyBwIjKKKZu157R+Hz7v86ZjiO6PiMe9a3wV/Bq5/3aCnSEhH2AxIsKQkTXI3McYqGhl39OkQrVy1R8mBkwA/VmgAd7qZWH633ahkpfIwDU019ve7NvJ2j7nuOP4wV8Ju5fwK7gHrZvwj2MhSTvwJV9BV6Ovp790OApvJegZ0c6thkwAxUYiJ+UK6pwxjVX5uWghcFCCdbG3QwkFj9V9xQbjIooPJPgOe71L5MIdsyAGTAD/ciAFnUs/B3wrGpT/RKTK5EgThVVgOogE8uTscXx30tc6lthHPwaoZqxoA6SEYDREIX5wbvEHY2btUnZQIDImqavxdGhCuSYsnif9BB0bWbADJiBxmUgjyMa1DDlYe1FyRr3siqX8zAZ/6uGGX4OEAnCQ2B9oBEaNjNgBhqOgfi1i1m5h9cDVObjcGR6/uIcaN3jgirqo8D14EjwO/AzUO8RWQ3HpjNsBsxArRmIi9yO4Xmz1diUw0i29Wzasj0sLgb5JNtJp7S+NBHrKemIhz+y/frY4+WL081UV+U5FvYAElczJiEirn+zWiYQb1zQ8izc+8BrQCMh0mdl1tVz8wNwNxjaOY26bhUJzHU9lxM3A2agyRnIo9Dwf8lDlwe9rTkZ0LzI+GJ/J/mvX8TdAbjh0Zx/uEvVcgzESjY9gnFI8eG4/wavAlWes5XpN9i+EfwDbAOWAVO2HF0usBkwA3VkIOzFc0UdHKwBFdeh+QT3VjC4/aRhWvx6Pt0O9gbPgyuBpmdiHQtDZhaDDFcQfkeyvxshoj1W+d/YCTMP6a0INgN/BXomXgwuBP05NdRCQ/k/ynvMgBnoIQN5FBq256GqiigL8Niam4EwhP95a6CXuv7zL8BxwP99c//xLl3LMhC/erEo9/hvwKHgGsAQ5k5fvtCz4EPwH6CGwU5gNTACJBX/liXQBTcDZqBXDITpeX7omXMK2AIUTakP0xCWNvA3xc+IhKyFcwh7E+wHJEK8B4a1x+hOiMimk3v/A+RQsJkBM2AG+sxAHoWGDXh4q6K5dJ9L5wQahIE4/HpV/vNrwRjwI7gZrA08raJB/kVn0wz0ngENDQ4Lgl+D/cA/wZOgeFgx86TD48n+/XE3ARIu+HKGzQyYATNQLwbiopB63pwIdgSzdj5TJSGic8ycb1lo6OUfZBW8l8T5sKZmQEKDLEdD1sPy5Ifha4VfshDO1cqcrZUY0GeiCoxqKWju5ORgFDgZnMn18DGuzQyYgZZhIAqNeibMnUCjneYCcounWLxN2AsZvIhfGMWzAwHTZgbMgBmoFwPxWbURqauT7DlwA8+dV+p1tjqmm45mWKKO52jKpC00NOXf6kL1kYE8Cg3zUyZ6sgoMqW+jcWlrTQb0eb0CvQcFeg7iCvff4l4EEB3aHsa1mQEz0NIMaMG2DtFBwkMqQqinMTun+Qe2VeGX6PBS4iYiRBvrxNjMgBkwA2YgYcBCQy8vBQsNvSTOhzU1A3kUGqaDcVX+/kaD8tAGZH9P8swCZ4ULMnln8cPC6mAB8BG4HChO1oaysT5Qz50+o/QQuAUUm+KtB1g4qMAQ4oI+FfkFKDaGIRdWAqpwo6wXngA9NfUk/h6cACpVyLdl/1vg3yC1+fDsBTYHrMzcF4vTaFhsqbAuUHmeAmcDOG5jLrfNDJgBM5AyEOde69k1O5ijCDOyna0Pfsn2ywkkQsifuG3v4reZATNgBlqJgQeSwnpEQyv96y6rGagTAxIaUrGhTqfoabLx0z9ao+HInh6Zg/hLkgc1fCfL5EUNblVe1SN/P/gBSEhYBKSmSvEoQLnjt+c/SPzX4P4EpDYczzNAaSitz8B3YAWQtT3YGA3eBo8ApXsjGAdUa4p7L9CxC1c4SKMOFOegEnE0BWb3EuG9DArTcSrK1rF45Pf4tfLzmiDLUy/T92FmwAw0NwMaKRUQfONndf+Geza4B7wPeI51Au/GuB7EZbgHg63AcmBGkBUrmpsyl84MmIFWYkBCQyo2tFK5XVYzYAbqwEAOhQaVMtCADufUobz1TFIVzyfBXkUnUUP/LTB1Ej4D7nMgu/7EbWx/AxYHMjWa1UBXA35nkJrEAgkISyUBE+GeAj4GGi4s0z4WUyxcBYYA2RJAcY7RRpW2D/F0fqGU0KA80uiPAorilBIalBeVaxiosQXSDmeALwDnD++Cw4FGhNjMgBkwAz1kQJ/XDYuADQHP8Q4R4j38esZkwXMtIPrGr2YcjbsjWA3MDsbt4Ykd3QyYATOQFwYsNOTln3A+zEATMJBXoeFlKmvZYfiNQDW96oUxYPpMZlfGr0Z48YgDvqYQFzjU55MmBhIPLgFZk0jwEbg+CZwKV2ndnGynzox4JCycmATIVTw18rN2OhuKlwoe2X3F/p8S8D04EyitYqFB+X4EKD2JF4pTSmggOIoqR8lTH4ufy/sNWbgTkJ/YGGC0R/gjqIPAUZ9SOFUzYAbyzEAUIRbimbI+QASOn928Dfd1wHO/kwjB8zyMAtp/BtgH6Bm1HBgBLETk+a923sxAazNgoaG1/3+X3gzUlIG8Cg33Uhl7uKYlrX9iN3GKu4tOQ09Xp7UNtFZD8fSFQYQNAxISsjYZGxoNcGkSKOFADfptk+2s8zQbjycBEiLo3e80D1m7qCDH43+pjQomEeFFcDz4NSglNExLuMpKxTmOvlCcA0EpO4BARh0UhpbaWduw+C3rv5NlVf7JUwTXUazoz1/bczk1M2AGzIAYCIPBnOAX4E/gWHAteAZ8DdJnUepKmHgT3AMuAoeCHcBagLV1gp79NjNgBszAQDBgoWEgWPc5zUCTMpBXoYHGtYbCN5R9Rm73LsqxRAI1yJcFTwF63AtfgZPB+KCS7cRONeDpCYumqRHaPq19s+N3cnw/gHeSkPNxqcgWNNIhaxpVoOO3yQaW8J9K2HOAucxlhYbsYZpCoXTLCQ0/T/YvnD2ovn7Nnw6MyojzqlXZJ38Rr+D+A/B/hHHqmwenbgbMgBkQAwEROSwG1gM818Mx4ErwCGDUWsfzKX1OyeXdHIWKG3B5JgfeLWELsAKYHej5bDMDZsAM1JoBCw21ZtTpmYEWZiCvQgMjAeJwVDViG8G07gKVw/iFhWx+72PjDfAJOBtohMDVQELAJaCcjWTHt+CWTIRB+F8GEjTSdQjUWP4/oHNr1IBMwoS2jwNDgGw4UD4UvjMoZ2uz43tApTia8qtjKokEqdBQbuqEBA+lsTkYIIsV87+SjXsA3MeK/Ye4ZwNGeIQJByhjPq0ZMAMtz0Cc/qURESuDrcD+QM+m28BL4BvAM7QLJFI8Bq4BJwLWzAmbguXArGBoy1NrAsyAGegpAxYaesqY45sBM1CWgbwKDbtSSVLFaljZnOdrx6pkh/wWRhZli2H7MZxGbieTyKD483YKbd+g4Ru/SkFvV6evV2jv8sk+HXsnkHjwGrgRpCMa6M2P6zoozgdAUylGg5OBwsqNaJiGfYq/D0itFkKDxBCd/9A00YF1A2tUhK3BtSCtwH+H/1ag667UfzKwWfbZzYAZaHEG4qiIRXg+rQN2BIeBi8B/wCiAQNxFiOB5HySoSozQ8453QFzkcgvcFcFcYKIWJ9bFNwNmoDMDFho681H1VqP0jFZdIEc0A03MwNtJ2TRS4M0GKOfMSR7pYepkKodGA1zYKbR93QU14hcCz2T2bYf/BHA7WA98AbJ2BxtUNguKNwKcBM4Fmk6hURMyiQlrAgkKKwCNgPgVkBCxLUjj4e1kx7A1HvgUKJ4sHdmwAX41wC9QYA+NEQQxzZSjHh5e6+htElPOaEeYAHcVsDpYDVD5LhwBhW/h3gAk4CBAtBX/DwTbzIAZMAP9xUAbgkH8dPKjpc8YBhE+LZixDPT81rNuMCgyTdOIn0LWc0/vrFJ4n+fgj0UHetMMmAEzYAYSBiw0+FIwA43DgCo6MgkNjWBqnMtGgKflSUwVN9lX7U7Hr6YnyD5vd+Lv7vweCs4GfwA/gFL2AoF/zuxow78AuDMTJrHhlARp8FaJRyMlStkIAicEx5fY+TfCJPhcUGJfd0FKc0qQctRd/H7c36b/5coEOHE0gwQHCQ9bgN+D0YTfhyvh4RbwmCvcsGAzA2YgRwxEEeAdMiQ8VDpjWrsmLjqcihF6v6ZQmPysbRO/hITTyRCMw3uEpOfQO7qEv62ckN0pMW+YATNgBsyAGTADzc+AejKE/rTFOdnQyieM80tpLGsF74awn5NLNe53LcqtRhQofPOi8HPYVu/Q5Em4hAXFOyTZLuc8yI6rinaqUaxj1UCWrQV+ABotkZoqmPcnSMOqcTXqQmn3ZY2GRZM0slMyqjn3AMfRaIe4CjyjRsKrAB4iqEiHK8AOYO4BzqRPbwbMgBmoMQNasyZ+RYMRXvGznHvgHgf+BXiPxK/6lJuq8S37XwP3gsuBjtPxW4CVwXwgfe/VON9OzgyYgRow8ABpCLYeMuARDT0kzNHNQB0YUEP8CHAnWAN8DUqZekpk6mFpBHuYTKp3fK6izGoKxN3g5CRcQsHvwIbgRKDen6nBYYn/S9w9QdbeZ+PMJECjDc4DNHIL14HFACuSx/RvxJUpnIZxXAySCl4Ukv6KOy8oFgzUm/8FoBJYN0s5ubNuZ6hLwnG0w7UkLWBhDn40tUIYCTQdBYtfR9H/fBvAbXtdoTYzYAbMQGMy0Kb3kEbOCWWsY3SE3tHTZzBdxr8k/mnAOKDItKZE4T3wbgVoP1Pd2hhVZjMDZsAMmAEzYAYajYH/kWGhP0wig7qAf5QL7gTjgzIWPibahWV25jFYjfb/lsjYRISpHKpYqdyfAnrJC4OAbEug8HJgqH6HqcImoSHlUFMzJFiMB7K2AhtvAqWp0Q1qCK8Oik0ixnPFgZntWoxoOJz0PgIlKpuZMzWUN34+E9Em7AKuB9xDHSMeXsZ/OmAUSxjeUMVyZs2AGTADNWVAa0cExIewCFgT/B7sCxDfA6PzwoPgDcD7seMZynurw8+7LiA2hCfATeBccBjYCWwERgLE7DBZTbPtxMxA6zLwAEUXbD1kQEOHbWbADHRmIBUZ1Biup0lkOGLlQhtjzsdtO5N28l/ihwgqjWwId3LMTPRmzFLPjNUw7aVJ6x7wU1BKcBhC+AhAQzR+3hKn1zYpR6qn6EUgMaGUqWE/AmjUhMSNgbDxOOnr4Fhw0EBkoH/OGcblPPrfJfCsCJYA+r9lVKLjqJb/4FKp1uKf7qGDB5sZMANmIGEgjpCYgg1EiYhpM6782e1J2C5l3xH4fgKNhpA/dRErouD9YeIifrdJhLeZATPQmYFUZFA9xtYDBiw09IAsR20ZBvpDaOgkMgwttN+KJ9LWriw2BA33V284PSFt2V79PP85l5A5NTrXy3Mm+zFvO3Au/Y9zgW/78bwDfKogkUHCw7IJlsKdEMi+AbqeJUYJj4IXuMbpubOZATNgBsxAZQbCUPYXiw8S3hVW7ErsLmeaNpgRHqIQodF3CmNEZcd26ke0bxtDuM0MNDMDFhp6+e9aaOglcT6sqRmot9BQUmRIGa0sNoQZiKfe4BN4uf85PSbnLiMwCs8CxJE42iDn2a1r9rQuzktgd3BpXc+U+8SDuFgQLA4kQMidG6TTSb7Cz9DgKDpIeBC4jtzjBg82M2AGzEAvGQgTc6DEh6kSTIkraDv1Z91JCC9nGj34KUiFB4kS8qco3k7C2zRt0mYGGoUBCw29/KcsNPSSOB/W1AzUU2jYFeaOWInpElcyXSIdyVDMZjdiw43EXwxMT6OrUV7W25HfL8H5oJVtYQq/NdihlUkoX3Z91SIuzilRKoXEB4kSMg0DfgpIdHgMPA6e5j7QtWUzA2bADJiBmjMQp8FNSbLCFIlbya99E4FKpnqWRIdPEjcVJrLbxf7PeNZ7lFslVr2vXgxYaOglsxYaekmcD2tqBuolNOwKa92KDCmzGbHhLsJ+Ab5u3xd+jXsJWJ+X7r/aw/xrBpqVgTgkeAFKJ+FBQo3c+UG63oN61F4FGv3wZMZ9jfuj3FodRLOZATNgBsxAfRiI4oREiWKkAkUaPnkmjvypqFwqWxIZEBuiOCERIkUqWKTbqauRFvLjttoaQPoca0F1xYspe1J3ZMvWWwYsNPSSOQsNvSTOhzU1A/UQGrSY0zu8RcMrhcFtEyZrMnTH4hZ8HOGi+DGFwp+Ie3x7/Njwehf/vbxA1uwuDe83A83HQKzEaqTDgmCBjKvhwKnpPn4KSHyQ+0w72jSU12YGzIAZMAO5YyBO65iCbAnFIkS6LTeLydhOp9zhLWlfEJoKD1khIg2TW8r/v8YUrIM6odYFCDttEmISC+PjWQbMC+jEansk2ZE43e3vHLuFtiw09PLPttDQS+J8WFMzwIsl2kQ1LqW+MLDnunzB8UJE+590IzZcwMKQWxZGq0f2EX5Wwv18bH7CSfi3ATPyonhvbLh9ZqCVGQgSGiQ8ZMUHCRKDM6x8gF+iw9OJmwoQqmTazIAZMANmoKEYiF/nmIQsZ8UH+SVAZMNKbQ/tpqha6FKjKFIhIuuWCk/D5IKBmOoR9uTce4PxAAtxp6M5gjh6DKhuewfQSNm/s/8wXKy7/TGO2o0LgWXBK+BWjm+FRa0tNPBn98YsNPSGNR/T7AzUS2gQb1WJDUUiw8ocp5dWxsLibDwEduMhf0Rmh71mwAx0YiCOfpidoPmAenFSzIY/O0xXo4QkOjwPnktc/G3v4LeZATNgBsxA0zEQR4imooSEiFSMyLppeOpOmsRLp++VY0UdRV8A1d8kUKRu6td2KWj/57x70room9VaWI2Y54LjwN9IQ1MoEgsX4hkGViX8G4QF1S1vAsPZfpPtbvYTsxBO4WcrcD3QO/VtwDmbfnqGhQb+6N6YhYbesOZjmp2B9OFe6xENKW8H4Sk7sqF7kSFNJqhHdhAP+HnSELtmwAxUy0DQKIe5QCo8yNW9NAvIChCqKEp8KBIgtC5EwyzGSvZtZsAMmAEzUDsGgkYMpOJD1p2UcCENS/1Zt5r6pUZT6P2D6BAFCbnCB0CdTBIkMhZmZUMN4o2B3m0SGvSlMiyMw4/qtr8h7HKFtFt4C/cIcAKosL/tWNJYhziXAkYMtr3A9iT4nwDsazsat5nNQkMv/91sZaqXSfgwM2AGesjAXop/ReHHPTctjO40jaJ6kSGe8Rx+eUGEn/GQfzCG+McMmIEqGYgiwZNEFjLWMQJCFTVh7sRdFzfTO8TcpkIYRdiLRdDnS/kErReihAebGTADZqBJGdCogILQi1FvseGvhnpWfJA/hUYbKF210xQvjcvogxh2OO6nILH4xaYr2TiSdw/TGVQvzE63LczBtoSRR0HWGMlQmA50t1/HrA+uIn1EBlkbogcfUGsPPzoG+ccMFDFgoaGIEG+agX5ioIvYcAmLPmbWZNBL5rNu8nI++w8BvwUWGiDBZgb6zkDbD6TxbIKi5AJrokTxQQKEKmazgznBKkA9Rql9SwXsZTYkQsgVJEAwCkKVx3TOLFs2M2AGzIAZaDEG2jRa4ZMEpcp+VKnACmFnsY93TLreQhQmEAI6TOsW6ZyI4J1MYRIgutuvgxYGF8iTMaW3dmY78QZNKxkGvgIaKYFr8R0eWs4sNLTcX+4C54iBDrFhOb4u8d9C0Hw+LfxYjchA1Lb3aczcgGcT3P3at9mymQEzUCcG2t4iYeHWzieI0zBmJUziQxZLsq2REFmjYhcQG2KF783EVWVN/jT9D10pgw2bGTADZsAMdMNAWI4IGwLWTQin4qqTaiRgBF7YDJdRCHFB5O95r4zGnzWN0pMYMBhU2N8x1fCV7MH4fwQSKoqNkRWFHTOBVG3D12x/WSWUp2zc4u2PKYvCbDlnwEJDzv8gZ6/pGYhiw0OFsCcLpvRAZOjgZX98Gs1wDNi4I7SrZ1KCdgN3gZu67o4h+/D7NpAy3l+2BifSEL//S064Cy4CShfVPNndZ0cvVb28+mpDSUB5Fpfi1NbSDMRpGM9BgVBkcUirRIjZwMxgpgxWwj8lKLbvqJTpXsyKD1m/9jFPdyBWNC/OqrfNgBkwA2ZgABnQ+2BfMEmCWXD1npF4oHrd7UCN8kGg2PROegL8ACrt175xgMSCrKVCRTZMftVJta6R9lfCFEX7q22Xko8wLe9AjZaw5ZiBthznzVkzAwPFQPrgqmaxnlrlcRUSegB80fMEgx7ofwar8dAtJyIMZ/8ocBjYA5QyFOLCI0B56S/TvL5tQaqIa6j506C4F5igPtm4HH0BUPk0t7Gvphf6Z0BcilObGeglA3FBMVUKZwTDErfYP3mJxNUz9R5QJTPFO0V+ttt68UwhFZsZMANmwAw0KAPhRjL+Os//P7QXIPwU90HAOyaOzMMbNO3veTAXUJ2mwv64+OMY4vyK46/BTSycgYeOrLb105C+uXHKRVaYmID0Sm3TYdR2et/O1aOjH0hiL9Gjoxy508rapsMMmIGBY+DmPpxaivV64CReHPPx8P2mD2kN9KG/IQP1GA6nl9WG4NGBLqDPbwY6MxDv1xcIE8pYGJ8dqfgwA/4spmdbo4KmBepxKrI4XPVdAiVCZN2sH8Gi7ZOiA71pBsyAGTADjcmAhIPMGg1tD1E/VEfOdmAv/Opo3hPcD17i+f9j5f3Eap/aN2v0jf1ZHO9lYzf76mtjNF9BUMeXrQkYqHaIShMU1UUwA83KgIaOhT9SuivB/4G/1aikqNSasxeHyk2BOyeQ+l2qQaKXlkZNqEdWvaso6XHhIZwuNgchzNeLi+MV71T6Us1TUx7Uc4t63WE6l3p4GToXV3xOdwzBMw8YDDSEPe3JVeNL8WVqsKksxS8x5VsoVz52xWMRcgrPaMNmBvqPgfiNclUShTIWVzGfhp3TAwkRcoXpMq7uvalAiSGy4XvCNUKiEt5nP/DcWHiwmQEzYAbyysCeZOzDosztwDb1xDASV50vEq8XbRcZ8BUK3e3XqNAtOP5UjqH+FdZiezZwObCZATNgBsxAlQzQcI+r5FYZPS/RwlU8+GksaFRDF5MIoMb9oV32jA1Q4/vmsZux0X002xcCHZtCYVnTkLkPQLpfroSGpUDWlmfjZZDGQ2EvXAKyIzBeZPsKkJrSUZysTcKG0sgKKnrh6aWapi33XKCXqVZKzobLnza0xNXjRfv1MtU5UpNQcR5QPnUsHMdzy787sJmBBmIg0MEQECLCYmBt8AewLzgZXAno4QqvAa53LVBbEgh/4RVwH9Axp4ADABXVwPMgLAMQJgMCX+w5ayB+nFUzYAbMQLMyEKbmmfxr8HMwbtdSVtofpuSYR8AoQF0xfAu26ppGU4Y8QKkEmxkwA2agzww0qtBAj3wg7+Fe0FbEwnC2aTT0WGj4jmOuB6uAuQBiRkxnRVyZRhAo3UuBGu28iAqbAXH4KEhN55cQcBMYAXjZaapHPJYGTYf1RmjQSAYaPoWrgfKo0Qs7AeVLrl6m8yfbh+AqLzKNlpBAohEKvHgLEwMJFh8BlSe1I/B8DTYBE4EVwDtA6e8ObGagSRkI3COBe0o9YGEj8GdwMDgT/Bs8BBADY4WT+6GkKPED4dwvAUEv3AIQLsPRAKEwbA3WAUuB2YHOV/zsalJuXSwzYAbMQKMxoDUUwkpgXZCOFG20QvQmvxIZBFsPGaBnw2YGzEBzMND2Jg/+fSgLlfjCNuDUGpTrC9JYD6RiwC74aRgUfgpuA8PAJeBPQEKC7AKgBju9pR1GT2ccXSARIo2nsJFgZtAXm56DJwAILHEUhtISB5+DZwENnbh6P05cwJGGUTSJEBpGrjzdHEMKhWtxjwF/BwuAV4HK/A9wEZDdDnYGF2vDZgaal4G2zyib8Hz3ZQyTEGeaBFPjZqH7TNuMpIgjjFRBLScojOY5JrFPz4nUTf3aTvHxWH9Dr0tDMWxmwAyYgUZgIK6hcGsj5NR5zAcDFhry8T84F2agVgwcT0Kbg0OprF9FXV5zqrNWrnKvOIMACwJ1MjXUU5FBOxAzok2YuBqhIIwHFgZzgfnBYmAoSG1BPE8BNRhS04iAG8F2aUAv3dc47mFwGJDAcUOCs3ArmfL4NZgNzJKJOFHiXwhXAoY4k6iSteuzG/abATPQJmFPeLF7LuJ6ElMST+KDRAj55abIbs9D+BSgQn0lLngpASIVH+R2B0TUNj2DbGbADJgBM2AGzEAdGKjw4q7D2ZykGTADdWagbQwCg0YzPAhOABskJ/xf4o6fuMWOngUTg7eLdnxatE1vYzSJErLBQOLGbxO/Kvdq9EtQyDbeZ2b7JVBs7xUHVLFdSixZgeP2AyrvQQmew90YPAFKmUZjqBw7ldj5MmESU5RvmRoxWdNIj6wAk91nvxkwAxUZ0HNKi0pGVIzZvjNOp9CICQkQxZAIkQ2bkW0Jmxo1UamOo2elnm96Zn1ShOIwxUvjMMJDK7TbzIAZMANmwAyYgUoMVHoJVzrO+8yAGcgtA22PUIHWUP+/4mqtgR2pGKuhzMI9HesT4O1kqpyr0a0GdtZKNeqz+w9k4/dgd3AZGAVk54KfAR2vXkM1+nWOYlMDoZKpQp8dGaG405U4QELKLgnUA7om2BdcAuYGpUyiisSG+YG4KWULJ4HTFu2UYDNeUZg3zYAZqAsDceSBpnAIxc+oCmeM0zkkRJSChAhB++TOlbiT4lZ67vE8Cxq58QX4X8aVvxy+LLFPYaDte1ybGTADZsAMmIGmY8BCQ9P9pS6QGYgMqOGvyvLOYCUqxogObffgXxlIANCIh6xpFITsqXan6l/SjusYHJE5gsWC4jkUpGeM1kh4GPwCzApeATLlb7XoK/+jynyxQKH8y9LGwOL4tV4CgkqciqHpHoIEBI1wGBekPZDKT2rKk86/NpAgk9q2eP4C1gdPA4kQvwLXgtRWTz12zYAZyCsDHdM5Xq0+h0GC62RA4kOK7Lb8wsQJJsKVYKlt+ScESqNKi58VleggkUJu1q+wrzLh6f6sq/3ZeGx7zQo4sZkBM2AGzMAAM5CtdA9wVnz6JmVAjbypgOa6M1Q1zuFVr5T8troxEHsA/5qMaDiN05zBDIKHCoVFqISOuY7t84DEBlWKVwSaYnAO+DfoiWk6xIZgS3AVmAPsDWYDMqWvYccngs3Bv8B2QFMrdgPzgko9enex/4/gAHAlWAwcDL4DqUkw0DWlURw63yNgaSAB4SYgoUNCw2ggseMVcDk4CkhgORrMDJS+jjsSXA+eBjLl8xiguecSNOYHxwHP74YEmxloLgbitIiPKZPQSwvjc+BEFaDnlPbLLeXXqK3s/sFs98CCnnc86ztECvklTqRhWbdcuOJ8nTkm9X+DkOFnH8TYzIAZMANmoDIDaY9g5VjeawZ6xoBEhc3A1mBBILEha6qwPALUWLs8uyMnfvUOyVTRawILEhR3AvtBO/7N32AmQ9pTpwqpGtS3gr+BbKP/ebZfAOuA1AbhkUh0KFD8GcERQA149eip9/8kcCe4BvwSXA1kc4DTgRrz44B7EvwFdzwgU2Ne+VlXG9iU4FygEQR6Xr0DNgUXgePBIUC2OFC8ubWBqRxXga1B+n8q7s5gMJgLqGyzA+VpWZCmfx3+vcEHILV98EggmQ7o+t0eqJwSQA4DNjNgBsxAnRiI37uXIKF3aypOlHK1P41Tys2GyT8E9NQkMiA2dIgWeh6mokS1fsXLQukVbVvMgBObGTAD+WDggSQbS+QjO42TC1WsbWaglgwMIzE1IGcCanSqsfpJAjUu0wauGoQ0emMvshpt74G8WNowbRKhIaU1zIzvJLAa4H85fX869G/E/xnoq6nxPhy8CiREVDINO1b890tEepmwx8H6RfsmYVvHjSoKz27qeTYNUDyl8wMoNp1XFexPi3YoTCKCjitnSn8W8DoYXS6Sw82AGTADjcFA/PqHnn2VMH5mfyV/uk9u6tfztrcm8SErQFTyp/uyx6T+4n3Z8MQfFyftbT59XK4ZCKqX/AF8B9QBIch/AX0LSd0nTM+2OlQWBg+Cc9nndzxE2DoYsNDQQUXPPKo428xALRl4hsRmBn8G/wRfgFI2FYFbgYPArWBVkBdrUqEhpTdsjO9oMDU4E+zGS7W44U1wv5tGS0iUuhRs0+9n9wnNgBkwA2aghgxEISMVHVIBolp3PDKiuKlbzq/9fRE0VF6J0iUEiBimDpPsvux26q/GzcaRH3jUBjzU2cLvOYHqO48CjeDRtSJ3dfin0yBMgV+jKD8G/wKbg8fYtx5uYmEoHtVRhbfA+ex/EzdjYXE21gLqoGI0ZdtdmZ32Nj4DFhp6+R9aaOglcT6sJAMahv4iUCNRw9GrsQOJtBdQb/J71RzQD3GaXGgQg0G9/ocDiT16wZ4Fzubl+DzuQNgunHQzsBDYFFwEbGbADJgBM2AGumEgChoSHFKkooS2s/50fyk3G08Ny2yc4m01VGtRf1bPeiI6dIga6XZ3bvbYbNzi8Ox26k/cVui1D2fAMfWdrHBASIeF8/BqBO4KxPmRulE6MnN5tu9ujxYY/RC/ZKV67fxgXrAM+0fhYmEdfq4EVwAJENsB6lZtF+LamoMBCw29/B9/0svjfJgZKMXAAkngHaV2lgm7k3AJDTOAvAgNZKXZLY5gQOn///buBEyes64T+ExuQEISIAaSQAggIYFEEEjQQNCVIMoCcngBggpyiQvIZdZdH+UQBEVYEBZEgXU1KgLqQggogXCEQ65AQiAkXIGECFESckGg9/vrqfpT03b3f3qOnu6ez/s836mqt+5PT01Vv11d0z/JPjt7+/Qkdzb0zkq3Gh3+JifJtsElg1tevpM11KcKL0s0Mmw5txUQIEBgUQT6X334VvamMoXSq0aGamyoxoi2EWJctzuu+tcyXB8GtNN2u7XeTSr9h4a2jRTV+DDQENEfHlXXTt8d39bVMusrCtVt68Z1m68zbMkdHsdnG16bjCr3zohTcr2TRoYq/bsc6jqonhOVhobe/dL9+eRWGZe7Gfr/leY9Ga4PR56U4bqD4VXJb2Z8Xb+k9L6UH/kgp3dq6nb3VdL+HH4QWFQBDQ2L+spuz35d3Kz2hHQ/t8ZNOCnT1R/4z65xepNtqsBynTDvlRPirdN9VPLIpFrtX5K6N6RbjQ452W7JBUAWvau8dFefHgIECBAgMLMC/fNh+wZ9ylu5q5GjGh+q0aHbCLG7unb6ttudvq2rbttfb6Jv0qyjrWvH7536TS69+sCh2yBRDRC1nnrwaV0j1nClphnsr9cjz6Ba/lS6TenVfEcnd871zFnpHpW8K0kDQb/R4EbpPyQ5P+mWr2TgB5uKk9N9x8r0VdO/6yEfxCw9K8ly+ndhltHrk7acmp4/Tk5I3tdW6hLYiQIaGnbiq751+/zBLPq8JH/s+w+Xqj/GzcN20re61B/mX01OSd6eTPPT86xOWS2wfEGG/0dOxr+b7r2TX0mqFf+Xk4zrvTbd1+Uk++V0FQIECBAgQGDqAtvZyNHd2f/U4NE2QAw2SHTrd9e/T9YwOE01BByWVCNEjds/qenaVF311/uZs5JOQ0M1MCztkVTDRN21WXfdPjV5Z65p7pjubZIqF690dv28Mn2HNkO3S7eua7vlqxk4OMuoO1tq/KW5Nrr8+xMsZ3n9u0XaxopmVK+ue2v9VdqGksFu7edgXTs8OG5wONMtV51CYGYENDTMzEuxEBtSt4jdP3lN8sokn4r3W6G/ke7Xk/qDX7cD3jipVub6/ft08ohEmQmB/u2Dp2dTkv5zHH4p/dUg9Ozk91L3r+menVSL/0VNt/qT5XqdFQIECBAgQGChBWalwWMdQx/GAAAqsklEQVQccj1joR72uHxZM9V7M5wPTpbeltS16meb+mow6Jb6Lyzth2S3Tf/g14G/lbo9kxsmNX6woSJV/X/XelD1dEo1SlSDxxa+9+rV6qqxodsI0e2vRotR4+savlJ3GbcZNzxu3Frmb6fpdgeXOWpcNe68L9upzLjAFv6yz/ie27ytEjg/Cz4peVzyqKQe7letzW2pPxr/luQP/tL/St6cVJ0ycwL95zi8PJuV9I5Ltx7W+FPJw5L6bupA6V2TijQ49HNJutW4VK91t9vpX64TnkKAAAECBAgQ2AKBXY0M7bLfmZ669rhlclZT+QNNt+0ckp53NwPXpTvYEFHT15v1q5ruwPj+cxzqGqltrEhvlXpj3P8PFu3dGKO6e2fiGrdv0va3004y3E7bdgeXUQ0q9eFfO74aTyr1oWCl2z9uOJNOveR9Q30Y1r2TZOrbYIVrENDQsAYkk0wsUE2qr2hSf8DqD9n+Sb0RrTeg9YdbWS1wswwOaxVfPdW2DS1/IquuVGt8Sv9uh9zOeGJuA7xjXs9XXJTKQ/Nfn9KwdGVO4Psem3NXWvuX60SV8iPJUf2+lR/1q/DanKQ/kRP+Hf59aemBX15aOqjuiKgT8zebbvr/Isv46yPza7Nfvo1zYR7k/P+Wlh79xYy/Irkyy6/ftcOTeyaDpS4m/q6p/MN0z0wyv0KAAAECBAgstkDvKdm/X0x+LNcK1TBQ5a5JveHO9cTS15Jcf/S/YvHxdFN61UCQ65j+Ax6roj4cGdYQcUGWmWufXl231Bv2brlpBura57xu5Up//+GQV6e/siCl/xWSYQ0Rg3WDDRc1vptJxud108iwIL9AdoPAjhOoN7GVaZVqjDknqXfj81Tuno2tOxYObDa6WvWrkaDe/A/k+D9J1X9NfjU3szwvz1/KfHvkFrmjc0vjDXIBsFf6T8tJv5f567uNvcz/O0k1JFw/uUvTf0C6pyc1vj/d5UtLz0uDxOD6ani/a1P9suQFeb7ln+daIcv+zG9l+JHJQ5KfSX4iyX70cmHRy22NvVskuUioh0j1/2VaFq0QIECAAAEC8yXQu1fO43Wt8LSV83n//P7W9H8+yYcXVXp/lHwiOaAZ/r30fy5pGg96f5H+01bGtT97/zd1b26mv2/6cw3T//ClmaD3wAznmqZdRzuf7hwLfCDbXlEmFBi43WfCuU1OYPcC9Sa6Wnfrj3b+8O76tLr6Z7W0jQz5NH0q5clZy4uT9yUnTmWNG19J/e3Iybn+DebSc5vF3TrdnKDr3zz1vxLTVPc79anBt5qKt6X7k8k9k/cn5fzC5KHJ3XJyzkXAMfdZWjo3FwQ3/8elpbe8Kh8w5Pfo9CPyfMpTcsfi9ZaWvpoGhBulW/Oe/F/y2Ij8jr3nnDRe5Pdsj+ungSLj9kr3sJqmuXPr5PTeNnl5suZSn4LUJw91C0YljRe7+kcN150UNV2bUcNVPyy1zm59O1zdpt+/zIqFQoAAAQIExgj072rIhxv9D4/qmQmfSfJBw/KnV2bqVd0bk7skdZ2SuxTq66HLdW2S0js+P96T5EOJ5fdm+K7pf1fy0xl+d4brE/kLkjdk+OkZruuZf04+mOG6Flqg0vul7Mz9kjI6NXl79rH6m9L70fT8XHLLpAz+NON76Tald1h6fjE5Kcn1Wv1njuWvpduUXn1V5EFJGmr614uvy/gzm5Hb3WkbGU7Y7g2Zt/VraJi3V2w+trcaFR6ePDo5LsmbxFXlqgx9JHlpkj/OM1em2dBwk+z9+ckBjUL9If/rpn+WO3Wy+Yfk8OSrzYY+ON16PY9Jzm3qBjs3TcWlyTuSkzsj6wT0peQVyROTFyZPS45OmguC9K387+oXpVsn/w9VRconk5qmTnBDSm+fVOZ38pifz2R/kvPjfTPpt1N3/aQaInaX/TJNN3UyHDVc42p9la0sdfJuGh12Pdiphru5bsxwjWvH766/Hd92q5Gw7e92u/Vtf9ttp6vhNlW3u/52/JBu9wImS1IIECBAgMB/EujdKFV1XfJvyQV585q7JgdLrz6FyHl7ud4AD5Te/0xF5YtJNUz8UaZ7TrpN6Z2YnjcldV2Ra43+9W2ukZavTv+ClN4fZkd+Lanrr9rHJyTPyz7WcErvx/IjDS9Lf5u8L/nvyZszvqZL6X8lpT6cKpNXJtWgcERyt0xTDTwpvTQ89P/jWXX3T56UPCDjT093u4uGhnW+Ahoa1glntpECh2dMWn2XbpHUJ77nJZc12TPd+iNduX2yV1J/nOsP0SXJrJRpNjTUH9zHdnb8ovTfLqnGmFku9Ye/3qDfs7ORz07/U5I6QRyS3Co5O2k907tULd51Enp8UvveLZ/KQL35/OGkPhW4eVKfPnTL8zPwzKQuGqoxo97Yfyv5naROeDlptRcT/a9TZHBX+YH0XZo8t8muEZvf0//OYjU2tA0P1W3Tra/+Ydm7mb66Nb66bbrDbX9163hqp6nuuOEa144f1l+f0sx6+V42sDKkEWJXXTu+fq/a/pq+7W+7w+racaO67Tzd8dU/ONydbnDcsOnbulHdweXVdNXwNGz6mnbYuGF1w5YzbLpu3bD+bl13mW192x03LtMMezOQvVEIECAwdYH+nQ+3zWo/lr9N1aAwUPofaByXyi9kfDVoLFDpHZ6d+VySxoHlt6zsWK8aHU5JjkpdPuDofTr91bDw2834E9Othodbp+4LGf/76f+FpKbP3/5eXXdcmOTDn+U0LPQfOP7RDN81w9VN6b00P6oh4oT+4Pb++ECz+lnYlu2VmHDt9UIrBDZT4G1ZWH1q/evJ3ySXJ8NKTVN/qJ6bVOvofZKdVo7NDj96YKcPy3C9kf7dgfpZGzw+G9S0ZO/atGoguDqpk0u1bldD5jVJnXhyMumXrzXdOzfdtnNQem6XtCfoapwYbGSoho1HJhck5yVV7pDU37GfSX4rqd+rKu9PHpHUiawt1SBR9dUYscWl/2n7tVlJZQ5L/3bQch2WPddQ351msL8drm6bWs+w/rZusNudvhpFBsd3hwfHd4e7/TXPPklbN0m3O223v5ZZw8rEAtUe0S/dBom2v0a2/aO6w6bZXV2N707T7W/XM2ya7nSD/aOmn2S6UdOOW3Y7rtsd3IfuuPX2j1pmvXjrXeaw+SZZ3lqmHTfNqHHD6ofV1favtX7YdIN1Gx0e3J5xy1vruO503f72tWvr2u64bZhgmu26k63/3ys+WBs6vPQbHz48fNzc1z48e/DFXNK95ft7svya9FdSejfJjzQg9O9mqIqU/tdM6oOd+yUvS+6R/H3q6+9FSv9BmnVXbO5YqK9Q9K8ZP5/6ppEhNSsP885dDb2DU1/LUuZQoC7WFAKbJVCtvUcn1cjw6t0stN5QPj+pT5nrFqtDkkuSnVRekp2tNyKD5empqD/gXxocMSPDh2Y7bpTkxLOq3ClD9Xr+VfLI5O5J3fr24uTipBqeciLpNxT8XLpVX63gZfAbSf09qvmHlRr3d0lOOEs/njQnq6VaZ5Vq0HhQ8s3kUcnjkjcld0m+k7TlC+mp+ZWxAv2LgfrUZsgnN2NnNHKowK6ncncbIaq/Tdsg0Q6P6o6bbrmzvHb+mn5Y/bC6mmewfnC4XW5b33YH5x1W39a13e48bd2o7uC0o6br1nf72/l3V9cdP66/O65d9mC3pummHd+dtzt+XP1GxnXX0d2GqlcIzJlA216xa7Oroq1s+7vDNWF3eC394+Zpx3WXM65uK8etZdntNAek5/rJRVWRMmz7q/665PH50/WRGmjKEenmg5/er6T7hKSuw96aPCPT1YdCt0qqtMteGVr5Wu3NmoGapq7huuUrGWg/ZKzxX+6OTH+Nr3LzRENDn2L+ftTFu0JgswTqE/oqZ6x01vTzXZmqGhrqzetOamh4cPb3XsmwUp/c/2HyC8NGzkDdHZptGDwp3Kup/1zTvTDd+l2o6Z6VVENDNRA8JqmW8XOTdydHJt9NTk/a36H07io3TN8bkp9MHp2cmbTlb9Pz8eTspH1TXHc23CB5bPLTyT8kbaltOSK5fnJVohCYgkD/U7j6Ha90G76msG6rIDBOoN8Itpwp1pvBxot2ObXStn+zumtd5lqmGzfNqHHD6ofV1f6utX7YdIN1Gx0e3J5xy1vruO503f72te7Wtf1td3B7usOjphlWP6xucP3tNG23u65R/e20bbc73aR146bf3bg6ttppNtqthoU6/3yjFpQybJ+qftg5qu60rQ91bpHU3akHJ09ODk/ul9wyqXLZSmfXz/9IX+4y7d8hWcsYNr6WVaWWPWx8jcsylHkV0NAwr6/cbG53fWpd5YSkfbPZrxjz46SMqzefnx0zzXpG1a39P7aeGTPP3kn9sd2qsl8W/KLdLPznM/7lyXt2M912jL5Vs9KvD6x82Gv+1Uzz7uRHkzqx1cnujKS+OpFW86Ujkj9NXpe8Khk80dRJqBogjkoemrwx6ZbLM/Cv3Yqmv+6qqIaGY5JuQ0Ntc52868RYd1MoBAgQ2MEC/Uaw+rusECBAYJhA/X2oOxPukcu4C1Ym6FWDxatzSXe7dK9cqes/h+qqpr869aFZrunqDsle3XVa177d0ozvV9UyfrA7Mv31gVCVwevClVo/50JAQ8NcvExzs5EfzJael9Qbx/pE+W+SatEcVuo7Xb+anJK8Panbrzaz/HYW9oANLPDbG5h3d7PWJ+5H7G6ijK+W47sm1RAzS+Xfm405It1PNf0Hpnuf5Jzkk01d26lx1SDQvZj9TIb/WztButUIcWzyrqQt1Vr+z0nN/xPJWclgqcakWyV/OTCi5qky+Pt3RL925d9YNb06BAgQIECAAAECQwQuSl2u7dpGhv4UpzXTHZHuxU3/Qel2GxpunOHPN+PqQ6f2uqypWuqOr2Xcvh3RdGt5VdplrAz5SYDAjha4bfb+zKTeVFYL5tnJGUl9N+vvk3cmn0jq9uGa5tykGh02u+ybBdYb1fXkY5nvQ5u9Qc3ybp7ut5La97Xk0c18s9Q5sdn2p3U2qk4I303e16mr3roTofbzDTXQlGqQenM70HTvm25N91PNcLVkX5DUVx1u09QN61RjTM1374GRp2a4GmjuOFD/TxmuRqS6q0EhQIAAAQIECBAYKdB7Qi6zct3aq7t9m9J7YIZz7dXLnQ69H0hq/C+0Y5v6XBP26kOilF7eA/TqPUCn9N6VuleuVPR+Lv1ppKhltaX3xAzXnROzUD6QjagoBAjMgEB9Ov34pN5QXpvUG8E29Wb0kuSM5EHJLL7h28o/KK/PPrcWa+l+LdPvn8xS2S8bU40lfzawUTVc+/Si5PbJ8UlZXtEMp9MvD8/PagTISaR/Z8dD0q2TyZ8mbXleempZ1UB1ypBUg1aVo5LalrqT4qHJLZKnJtWYMLh9qVo6P/mX6lEIECBAgAABAgTGCfTywU/v0uTPkxsmub7r5UOl3hnfn6v+FWXv7OSQJB/09XKt2zszqfcDKb0Tk7wf6N2nGc71Wr9x4tbN8F4Z/lLykiQNGr3cqdq7MPnNlfHb/nMr3xds+87ZAALzLFAtoAcn9an0YUn+mMx82ao/KPXGu95gr6WBoTvNC2dQ7E3Zpg8PbNf1MpyTxK67VWpfP57cIemWPTNQDS6txUXpf3lS87clJ5yxTg9oJ0z3pOTczvTVsPHcZI+kW3KC7D9R+cndSv0ECBAgQIAAAQKjBHrH5ZIsH+j0GwvyQU7vnUnnqxC9AzKcO0Z71yV1d0MaIXq3Xb203tNSlzud++M/m+7DB8bnGrn3haTubMhzGXr/Oxm8jls9y/SGtup9wfT2YJvW1LQ0bdParZbAbArUH5QqJ6x0Nu3n72ZJd1vH0uqukEcm9QZ6Vko9G+G9Se3PYINDfW3lyOTi5D+SUSUnpv7Df3LC6TcqjJpurfVpSe/f/fG5dKsRY7DkJLf0zKROfuO2a3A+wwQIECBAgACBHS5QdywsXZ3nNXxzOES/8SEfJi1/fcT4fVJ/s4z/4vDxVdu7RX5ckmnqztRZKVv1vmBW9s92EFhYgU9kzyqzVLRcru3VODWT/f3aJt32qerk9pXkcdu+JTaAAAECBAgQIEBgXgS8L5iXV8p2EhgQ6GW4MkvFH5S1vRrV6lzPR/ihtU2+rVP9Wtb+yaS+tqEQIECAAAECBAgQWIuA9wVrURoyzTx8X37IZqtaIIF870uZU4F6jsLTk3r2RH39YZZLfV3iiUk9jFQhQIAAAQIECBAgQIAAAQJTFdByOVVuKyNAgAABAgQIECAwkwLeF6zzZXFHwzrhzLZmgb0z5U2TGyT1aXI9QKYexOeT5SAoBAgQIECAAAECBAgQWDSBWfm3IYvmutP3pxoVHpvUfyO4MqmH8NWt9Rck9STay5Mzk4ckCgECBAgQIECAAAECBAgskIA7GhboxZyRXTk821H/9rAeFHhNkv+7u5T/h9tPPYjvoCZ3T/ceyZuSJySXJAoBAgQIECBAgAABAgQIECBAYJVANSxclTwm2X/VmNUD9XWKZyX1FYrTV4/a9iHfxdr2l8AGECBAgAABAgQIENh2Ae8Ltv0lsAEElpbqyf71ryqrkWGt5TmZsOY5ZK0zTGE6f1CmgGwVBAgQIECAAAECBGZcwPuCdb5AntGwTjizDRU4tqk9Y+jY4ZXvaqoPHT5aLQECBAgQIECAAAECBAjMk4CGhnl6tWZ/Wy9uNvGECTb1pEz7vaQeFqkQIECAAAECBAgQIECAAAECBHYJ1MMeP53Uf5Wo/zpxQDKq3CQjnpHUMxpOGzXRNtW7RWqb4K2WAAECBAgQIECAwAwJeF+wzhfDf51YJ5zZhgpUo8H9k9ckr0xektSdCt9I6t9a1h00ByY3To5O6vevGiYekSgECBAgQIAAAQIECBAgQIAAgaECy6l9fPLB5NqkHvbYphojLknqOQ4PSmbx6ztaLvPCKAQIECBAgAABAgR2uID3Bev8BXBHwzrhzDZWoBoVXtFk73TrLob6V5fXJNXIcF2iECBAgAABAgQIECBAgMACCmhoWMAXdcZ26TvZnkubzNim2RwCBAgQIECAAAECBAgQ2GyBWbxtfbP30fIIECBAgAABAgQIECBAgACBKQloaJgStNUQIECAAAECBAgQIECAAIGdIKChYSe8yvaRAAECBAgQIECAAAECBAhMSUBDw5SgrYYAAQIECBAgQIAAAQIECOwEAQ0NO+FVto8ECBAgQIAAAQIECBAgQGBKAhoapgRtNQQIECBAgAABAgQIECBAYCcIaGjYCa+yfSRAgAABAgQIECBAgAABAlMS2GtK67EaAvMmcEw2+ANT2uhq8DsuuS753pTWaTUECHxfYL/0fjtx/H3fRB+BaQnU8Xdt0pvWCq2HAIG+wHJ+7pt8NrmiX+PHMIF6T3DOsBHqxgtoaBjvY+zOFHjTlHd776xvn6QusrzRmTK+1e14gbrQqnOhhr4d/6sAYBsEqqG9Pf7qGFQIEJieQHv+u15WqaFhtHs1Mkz7vcHorTGGAAECEwgcnmmrkeGpE8xjUgIENkfgqCymjr/Hbc7iLIUAgQkE7pxp6/h7xATzmJQAgc0RuEcWU8ffgzZncZZCYLWAZzSs9jBEgAABAgQIECBAgAABAgQIbEBAQ8MG8MxKgAABAgQIECBAgAABAgQIrBbQ0LDawxABAgQIECBAgAABAgQIECCwAQENDRvAMysBAgQIECBAgAABAgQIECCwWkBDw2oPQwQIECBAgAABAgQIECBAgMAGBDQ0bADPrAQIECBAgAABAgQIECBAgMBqAQ0Nqz0MESBAgAABAgQIECBAgAABAhsQ0NCwATyzEiBAgAABAgQIECBAgAABAqsFNDSs9jBEgAABAgQIECBAgAABAgQIbEBAQ8MG8MxKgAABAgQIECBAgAABAgQIrBbQ0LDawxABAgQIECBAgAABAgQIECCwAQENDRvAMysBAgQIECBAgAABAgQIECCwWkBDw2oPQwQIECBAgAABAgQIECBAgAABAgTmWmDfbP2bk+Pnei9sPIH5FLhBNvufkh+ez8231QTmWuCAbP1bktvP9V7YeALzKXBwNvutyZHzufm2mgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwW4E9dzuFCQgQWItAHUt3T+6WXJdclkxaDssMJyXVvTT5TjKqbMb6Ri1bPYF5E9iM42Gtx98tgnPAiFyZ+u/NG57tJbBBgc04/tpNOCI9P5N8sq0Y0l3rsTpkVlUEFk5gmsef89/C/frYIQIEZl3gttnATye9Ts5J/+HJWsvvZcJqWGiXcV36nzFi5s1Y34hFqyYwdwKbcTys9fg7ODrtMTqs+0Nzp2eDCWxMYDOOv3YL9k/PuckVbcWQ7lqP1SGzqiKwcALTPP6c/xbu18cOESAw6wLL2cAzk8uThye3SR6TXJV8MblBsrty70xQb1remNwpuVvytqTqnpR0y2asr7s8/QTmWWAzjodJjr+Tg1XH5TuSFw/JTVOnENgpAptx/LVWB6anPe+NamiY5Fhtl6tLYFEFpn38Of8t6m+S/SJAYGYFHp8tqzcejx3YwmpsGFY/MNnS9VPx+eSipG5/a8s+6an6Lyfd+o2ur12+LoFFENjo8TDp8ffMoNVxfdIi4NkHAhsU2Ojx167+Z9Pz1aSOrWuTYQ0Nkx6rWYxCYKEFpnn8FaTz30L/Otk5AgRmUeCD2ahrkvrOdrfULaBXJx/uVg7pv2/q6uLq+UPGPbcZV99XbctG19cuR5fAIghs9HiY9Pj766DVMxhuuAh49oHABgU2evzV6ttj8Ovpv3/y0WRYQ0M73VrPlVmMQmChBaZ5/BWk899C/zptzc7tsTWLtVQCO0Jg7+zlDyefTf5jYI/rqxTnJcclNd2oUl+TqPKhlc6qn23dXZrazVjfqhUYIDDHAptxPExy/BVVe7zXHUe/mDwluU9yvUQhsJMENuP4K696HtFzkh9K/jEZVSY9VkctRz2BRRCY9vFXZs5/i/CbM+V92GvK67M6AoskUN8prTcc3xixU5elvk4G9b3tui10WPnBpnLYMmr+KoeudJY2Y33NonQIzL3AZhwPkxx/det2vRn6t+TzSfeuhvMzXM9oaRsH06sQWGiBzTj+Cqied1LZXZnkWN3dsownMO8C0z7+nP/m/Tdmm7bfHQ3bBG+1CyGwf7MXdcvnsNI2FIx7IOS4ZQzOP27aWv/g9MO2SR2BRRHYjONh3DIGj6djA1fnzLrAe3ZydHJM8gfJkck/JQclCoGdIDDu2Kn9Hzx+Nmoybn2bva6Nbqv5CWy1wLjjoda92cfEsVmm899Wv6oLuHx3NCzgi2qXpiZQz2aoMqrBrn2I43dXJhv6c9wyBucfN20tfHD6oStUSWBBBDbjeBi3jMHj6cK41dclvpy8r2N4Svpr2mckT01+J1EILLrAuGOn9n3w+Nmox7j1bfa6Nrqt5iew1QLjjoda92YfE85/W/2KLujyR71BWtDdtVsENlXgkiytHuQ46lPMtv6bY9bafqWinbY7aVvXzr8Z6+suXz+BeRbYjONhkuPv0mCdmnQbGVq/1zc9d2ordAksuMBmHH+TEE1yrE6yXNMSmEeBaR9/zn/z+FsyA9usoWEGXgSbMLcC9RCr+uPbNggM7kjVX5UMPiiyO91aLp6+0sywGevrrls/gXkW2IzjYZLjb5xVPbehSns768qQnwQWV2Azjr9JdDbrWJ1knaYlMKsC0z7+xjk4/43T2eHjNDTs8F8Au79hgU9nCfVd7ZsMLKkeAHn75CPJuK9O1PxVTlrprPrZ1n2oU7vR9XUWpZfA3Ats9HiY5Ph7SrQ+k9TXJwbLUU1FjVcI7BSBjR5/kzhNcqxOslzTEphXgWkef85/8/pbYrsJEJhrgQdl6+vrE/X97G55Vgaq/iHdyhH9Z6f+4qT7aeiNMly3xn0s6T5LZTPWl0UqBBZCYDOOh7Uefw+OWB3Tn0qWO3rV/7akxt2zU6+XwKILbMbxN2j00VRcMVjZDK/1WB0xu2oCCyUwzePP+W+hfnXsDAEC8yJQdwWdm9RdC89OfjJ5TjP8xnS75dgM1JuRT3Qr01+fkFZ93f1QDRMPTepiq26Nu3PSLZOsrzuffgKLKDDJ8bDR468ervXOpI7VM5JHJD+bvD2pulcnCoGdJLAZx9+gV537RjU0THKuHFyuYQKLJjDN48/5b9F+e+wPAQJzI1Bfmzgt+V5SbzgqpyeHJN0y6o1OTfOw5LKknb/6fy0ZVta6vmHzqiOwaAJrPR424/g7MHivSKoRsD1W69/bPj1RCOxEgc04/rpuH83AqIaGmm6Sc2V3ufoJLKLANI8/579F/A2yTwQIzI3ADbOlP5IMNjCsdQfqFuzbJMck+65hpo2ubw2rMAmBuRHY6PEwyfG3X1TumBwxNzo2lMDWCmz0+Jtk6yY5VidZrmkJzKvANI8/5795/S2x3QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSGCuwxtFYlAQIECBAgQIAAAQIECBAgQGACgQdn2rcmVyb/kjwqUQgQIECAAAECBAgQIECAAAECEwvcLHNck/xysn/ywOTa5OGJQoAAAQIECBAgQIAAAQIECBCYSOA3M/XbBuZ4U4b/cqDOIAECBAgQIECAAAECBAgQIEBgYoHlzHFh8oKJ5zQDAQIECBDYIQJ77ZD9tJsECBAgQIDA4gjU1xfOT86ZYJeOzLTHDZm+l7r6asRlydlNfzojy5Mz5sbJy0ZOYQQBAgQIECBAgAABAgQIECAwNwKPyZZW48BvTbjFT2rmq3lH5eqMG/fshadm/BXJyYlCgAABAgQIjBBwR8MIGNUECBAgQIDAzAk8IFv08g1u1Z9l/lMHlnFAhu+c/Ebyf5px3Wcw1Ncl/ih5VHKf5P2JQoAAAQIECBAgQIAAAQIECMypQH1Vod74t19z2MgdDc8cY/CEZh2ndaapRoZqnPhSckynXi8BAgQIECAwQmCPEfWqCRAgQIAAAQKzIvDWbMjDkr9Lfn0LN+r1Wfb3knt01lH/1vIRSX31Ys/k2Ca3SlchQIAAAQIEhgj46sQQFFUECBAgQIDATAl8JFvz35N/Tu6/hVt2dJZdH8Jc3FlHPQtin+TNnbrqrbsefnqgziABAgQIECAQAQ0Nfg0IECBAgACBWReorzRsVam7FA5L7pS0z394ZWdldQeDQoAAAQIECBAgQIAAAQIECCyoQN3RsJFnNIz6jxNVf13y3KQaHxQCBAgQIEBgnQLuaFgnnNkIECBAgACBmRPYP1s0+Pypa1JXacvH03NOUg95PDD5iWTf5I+TFySXJgoBAgQIECBAgAABAgQIECCwQwTG3dFwQQwG71h4fuNSD3OscYP/deLI1H0h+W7yyEQhQIAAAQIENijgjoYNApqdAAECBAgQmBmBd2ZLzh3YmvMGhgcHL0zF/ZJ/TV6VnJ+8P1EIECBAgACBdQpoaFgnnNkIECBAgACBmRN4zDq36FOZr/6rxYuSv02OTi5PFAIECBAgQGAdAoPfY1zHIsxCgAABAgQIEJh7gRdnD85KDk3qWQ0KAQIECBAgsE4BDQ3rhDMbAQIECBAgsFAC38ve1B0R30kem5yYKAQIECBAgMA6BDQ0rAPNLAQIECBAgMBCCtR/o6iHR9Z/pHh1Uv+NQiFAgAABAgQIECBAgAABAgQIbKpANTg8JHlYcsNmySele0TTX51qnBhX1rKMcfMbR4AAAQIE5kbAHQ1z81LZUAIECBAgQGAbBPbPOuu/UNwxeUfyxOTmTf/t0q0Ha78muSL5evJnyY8nxyevS6rsbhkrU/lJgAABAgQIECBAgAABAgQILLzAC7OHl3b2su5cqH+D+e3kBslByb8kdcfDbycfTnpN/irdKrtbxspUfhIgQIAAgQUR8O8tF+SFtBsECBAgQIDAlgjcK0s9q7PkakSoB0a+O7kyuTo5OfluUuUPkkOT65KvJVXulYxbRk2zT3L3pO6M+FhS61EIECBAgMBcCmhomMuXzUYTIECAAAECUxL4ZtZz4MC6bpPhv2zq6r9VDJavDFTsbhk1eX0t473J4cm1Sf0HDIUAAQIECMylgGc0zOXLZqMJECBAgACBKQnU1x9+JNmzWd/B6V4vuX2T9uGQzeihnd0to57hcMvkQ8njk5cnCgECBAgQIECAAAECBAgQILCAAvVMht9P6o6DRyUvTaqx4UXJF5Mav7uylmXcJQt5bfLl5MWJQoAAAQIECBAgQIAAAQIECCywQN3RMHj3wr4T7u+oZdQdEi9olrV3ul9L6kGTCgECBAgQmEsBz2iYy5fNRhMgQIAAAQJTFqiHPdaDGrulnqUwSRm3jHrWw2lJNUacmtSDJhUCBAgQIECAAAECBAgQIECAwLoF6m6GurtBIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2FaB/w8RK+vWphcydQAAAABJRU5ErkJggg==", - "text/plain": [ - "Plot with title “paste(\"NPM, |\", rho, \"| = 1/2\")”" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "pdf(\"figures/401K-contour-npm-lower-rho2.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", - "invisible(dev.off())\n", - "\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)" - ] - }, - { - "cell_type": "markdown", - "id": "14dd7abd", - "metadata": {}, - "source": [ - "The nonparametric contours are slightly more conservative, but the message is similar. Under perfect adversarial alignment, income-strength confounding can overturn the finding; the contextual matching scenario cannot. At $|\\rho| = 1/2$, even the lower confidence endpoint for the main scenario remains comfortably positive.\n", - "\n", - "The treatment-side gains for IRA participation and two-earner status come out near zero here, placing both markers on the vertical axis. This follows from what `gain.D` measures in this model: a covariate that barely moves propensity scores away from the middle of $[0,1]$ leaves average precision almost unchanged, however much it predicts the outcome.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/T-ADML/r-ADML-401k.irnb b/T-ADML/r-ADML-401k.irnb new file mode 100644 index 00000000..a1600b88 --- /dev/null +++ b/T-ADML/r-ADML-401k.irnb @@ -0,0 +1,1146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ec70e3f1", + "metadata": {}, + "source": [ + "# Empirical Example for Sensitivity Analysis: 401(k) Eligibility and Financial Wealth\n", + "\n", + "This is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n", + "*Long Story Short: Omitted Variable Bias in Causal Machine Learning* \n", + "\n", + "The original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "170f2037", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:10.878737Z", + "iopub.status.busy": "2026-09-11T14:54:10.877672Z", + "iopub.status.idle": "2026-09-11T14:54:11.736717Z", + "shell.execute_reply": "2026-09-11T14:54:11.736203Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Install packages if needed\n", + "repos <- c(CRAN = \"https://cloud.r-project.org\")\n", + "\n", + "if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\", repos = repos)\n", + "if (!requireNamespace(\"sensemakr\", quietly = TRUE)) install.packages(\"sensemakr\", repos = repos)\n", + "if (!requireNamespace(\"ggplot2\", quietly = TRUE)) install.packages(\"ggplot2\", repos = repos)\n", + "if (!requireNamespace(\"ranger\", quietly = TRUE)) install.packages(\"ranger\", repos = repos)\n", + "if (!requireNamespace(\"dml.sensemakr\", quietly = TRUE)) {\n", + " remotes::install_github(\"carloscinelli/dml.sensemakr\", upgrade = \"never\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "267a6538", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:11.745657Z", + "iopub.status.busy": "2026-09-11T14:54:11.738176Z", + "iopub.status.idle": "2026-09-11T14:54:11.759105Z", + "shell.execute_reply": "2026-09-11T14:54:11.758692Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dml.sensemakr 0.2.2 \n", + "sensemakr 0.1.6 \n" + ] + } + ], + "source": [ + "suppressMessages({\n", + " library(sensemakr) # attached first: we only need add_bound_to_contour()\n", + " library(dml.sensemakr) # attached last: keeps its methods for class \"dml\" unmasked\n", + " library(ggplot2)\n", + "})\n", + "\n", + "options(scipen = 99) \n", + "\n", + "# figure sizing inside the notebook\n", + "options(repr.plot.width = 7, repr.plot.height = 6, repr.plot.res = 150)\n", + "\n", + "dir.create(\"figures\", showWarnings = FALSE)\n", + "\n", + "cat(\"dml.sensemakr\", as.character(packageVersion(\"dml.sensemakr\")), \"\\n\")\n", + "cat(\"sensemakr \", as.character(packageVersion(\"sensemakr\")), \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "a7079423", + "metadata": {}, + "source": [ + "## Setting\n", + "\n", + "The outcome $Y$ is net financial assets and the treatment $D$ is eligibility to enroll in a 401(k) plan. The observed covariates $X^o$ include income, age, education, family size, marital status, two-earner status, IRA participation, and home ownership. The identifying argument used in earlier work is that, after conditioning on these worker characteristics, eligibility is approximately as good as randomly assigned.\n", + "\n", + "However, the threat is that unobserved firm characteristics $A$ may affect both eligibility and financial assets, for example through employer matching policies. Conditional ignorability then holds given the full $X = (X^o, A)$, while the data identify only functions of the short regressor vector $W_s = (D, X^o)$. What we can estimate is the short parameter $\\theta_s$. The task is to bound its\n", + "distance from the causal target $\\theta$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "641048b9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:11.760854Z", + "iopub.status.busy": "2026-09-11T14:54:11.760463Z", + "iopub.status.idle": "2026-09-11T14:54:11.771931Z", + "shell.execute_reply": "2026-09-11T14:54:11.771465Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "households: 9915 covariates: 8 eligible: 37.1% \n" + ] + } + ], + "source": [ + "data(\"pension\")\n", + "\n", + "y <- pension$net_tfa # Y: net total financial assets\n", + "d <- pension$e401 # D: 401(k) eligibility\n", + "\n", + "# X^o: the observed worker characteristics\n", + "x <- model.matrix(~ -1 + age + inc + educ + fsize + marr + twoearn + pira + hown,\n", + " data = pension)\n", + "\n", + "cat(\"households:\", nrow(x), \" covariates:\", ncol(x),\n", + " \" eligible:\", sprintf(\"%.1f%%\", 100 * mean(d)), \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "5be9f408", + "metadata": {}, + "source": [ + "## Short Estimates Under Conditional Ignorability\n", + "\n", + "### Partially Linear Model\n", + "\n", + "The first specification is the partially linear model,\n", + "\n", + "$$Y = \\theta_s D + f_s(X^o) + \\varepsilon, \\qquad \\mathrm{E}[\\varepsilon \\mid D, X^o] = 0,$$\n", + "\n", + "with the nuisance functions modeled by random forests, cross-fitted over five folds and repeated five times." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f4dad1cf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:11.773658Z", + "iopub.status.busy": "2026-09-11T14:54:11.773277Z", + "iopub.status.idle": "2026-09-11T14:54:38.547333Z", + "shell.execute_reply": "2026-09-11T14:54:38.546902Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Partially Linear \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 26.474%), treatment (ranger, R2 = 11.599%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 9114 1323 6.888 0.00000000000567 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set.seed(1)\n", + "dml.401k.plm <- dml(y, d, x, model = \"plm\", cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", + "\n", + "summary(dml.401k.plm)" + ] + }, + { + "cell_type": "markdown", + "id": "994cf0bc", + "metadata": {}, + "source": [ + "### Nonparametric Model, with Income Groups\n", + "\n", + "The second specification drops the additive structure. The short regression\n", + "$g_s(W_s) = \\mathrm{E}[Y \\mid D, X^o]$ is left unrestricted and the target is the average contrast,\n", + "\n", + "$$\\theta_s = \\mathrm{E}\\big\\{g_s(1, X^o) - g_s(0, X^o)\\big\\},$$\n", + "\n", + "estimated with the same nuisance learners. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c886bdf1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:38.550249Z", + "iopub.status.busy": "2026-09-11T14:54:38.549906Z", + "iopub.status.idle": "2026-09-11T14:54:38.554742Z", + "shell.execute_reply": "2026-09-11T14:54:38.554353Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# income quartiles\n", + "g1 <- cut(x[, \"inc\"],\n", + " quantile(x[, \"inc\"], c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE),\n", + " labels = c(\"q1\", \"q2\", \"q3\", \"q4\"), include.lowest = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e281ccde", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:54:38.556283Z", + "iopub.status.busy": "2026-09-11T14:54:38.555936Z", + "iopub.status.idle": "2026-09-11T14:55:06.503086Z", + "shell.execute_reply": "2026-09-11T14:55:06.502651Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Nonparametric \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 25.572%), treatment (ranger, R2 = 11.48%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 8198 1196 6.852 0.00000000000726 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Group Average Treatment Effect: \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "g.ate.q1 4646.2 794.2 5.850 0.00000000492 ***\n", + "g.ate.q2 2729.4 1296.7 2.105 0.035305 * \n", + "g.ate.q3 6854.3 1838.0 3.729 0.000192 ***\n", + "g.ate.q4 18406.6 4061.6 4.532 0.00000584674 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set.seed(1)\n", + "dml.401k.npm <- dml(y, d, x, groups = g1, model = \"npm\",\n", + " cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", + "\n", + "summary(dml.401k.npm)" + ] + }, + { + "cell_type": "markdown", + "id": "ef3e8146", + "metadata": {}, + "source": [ + "## Group Effects by Income Quartile\n", + "\n", + "Plotted against income quartile, with sampling confidence bounds under conditional ignorability,\n", + "the pattern in the summary above is easier to read.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5c41f860", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:06.505709Z", + "iopub.status.busy": "2026-09-11T14:55:06.505362Z", + "iopub.status.idle": "2026-09-11T14:55:06.510354Z", + "shell.execute_reply": "2026-09-11T14:55:06.509927Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "group.names <- setdiff(names(coef(dml.401k.npm)), \"ate\")\n", + "\n", + "df <- data.frame(groups = 1:4, estimate = coef(dml.401k.npm)[group.names])\n", + "cis <- setNames(as.data.frame(confint(dml.401k.npm)[group.names, ]), c(\"lwr.ci\", \"upr.ci\"))\n", + "df <- cbind(df, cis)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "522bd397", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:06.511809Z", + "iopub.status.busy": "2026-09-11T14:55:06.511512Z", + "iopub.status.idle": "2026-09-11T14:55:06.689836Z", + "shell.execute_reply": "2026-09-11T14:55:06.689403Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p1 <- ggplot(df, aes(x = groups, y = estimate)) +\n", + " geom_line() +\n", + " geom_ribbon(aes(ymin = lwr.ci, ymax = upr.ci), alpha = 0.1, col = \"blue\", fill = \"blue\") +\n", + " theme_bw() +\n", + " xlab(\"Income Groups by Quartiles\") +\n", + " ylab(\"ATE\") +\n", + " ggtitle(\"Under conditional ignorability\")\n", + "\n", + "ggsave(\"figures/401K-groups-noconfounding.pdf\", p1, width = 4.5, height = 4.5)\n", + "p1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "90b555b3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:06.692037Z", + "iopub.status.busy": "2026-09-11T14:55:06.691679Z", + "iopub.status.idle": "2026-09-11T14:55:07.062469Z", + "shell.execute_reply": "2026-09-11T14:55:07.061975Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RV_alpha (alpha = 0.05, threshold theta = 0)\n", + " partially linear : 5.6%\n", + " nonparametric : 4.6%\n" + ] + } + ], + "source": [ + "rv.plm <- robustness_value(dml.401k.plm)\n", + "rv.npm <- robustness_value(dml.401k.npm)\n", + "\n", + "cat(sprintf(\"RV_alpha (alpha = 0.05, threshold theta = 0)\\n partially linear : %.1f%%\\n nonparametric : %.1f%%\\n\",\n", + " 100 * rv.plm[\"ate\"], 100 * rv.npm[\"ate\"]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "99f710a6", + "metadata": {}, + "source": [ + "## The Main Confounding Scenario\n", + "\n", + "The main scenario assumes that omitted firm characteristics explain as much outcome variation as the\n", + "maximum employer match accumulated over three years, which implies approximately $C_Y^2 = 0.04$. On\n", + "the treatment side it allows an additional 2.5 percentage points of eligibility variation beyond the\n", + "observed controls, corresponding to $q_\\alpha \\approx 0.03$. We take $|\\rho| = 1$, the conservative\n", + "case in which the two induced errors are perfectly aligned.\n", + "\n", + "What $q_\\alpha$ measures depends on the target: for the partially linear model it is the\n", + "nonparametric partial $R^2$ of the treatment with the omitted covariates, and for the\n", + "binary-treatment ATE a gain in average inverse-variance precision.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8a03f2af", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.064135Z", + "iopub.status.busy": "2026-09-11T14:55:07.063812Z", + "iopub.status.idle": "2026-09-11T14:55:07.069566Z", + "shell.execute_reply": "2026-09-11T14:55:07.069163Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "cf.y <- 0.04 # C_Y^2\n", + "cf.d <- 0.03 # q_alpha\n", + "rho2 <- 1 # |rho| = 1\n", + "\n", + "bounds.plm <- dml_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "eb62473e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.071060Z", + "iopub.status.busy": "2026-09-11T14:55:07.070750Z", + "iopub.status.idle": "2026-09-11T14:55:07.077621Z", + "shell.execute_reply": "2026-09-11T14:55:07.077164Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "bounds.npm <- dml_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b6c06ec7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.079155Z", + "iopub.status.busy": "2026-09-11T14:55:07.078850Z", + "iopub.status.idle": "2026-09-11T14:55:07.090590Z", + "shell.execute_reply": "2026-09-11T14:55:07.090101Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "cb.plm <- confidence_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)\n", + "cb.npm <- confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6a24c23a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.092294Z", + "iopub.status.busy": "2026-09-11T14:55:07.091969Z", + "iopub.status.idle": "2026-09-11T14:55:07.102416Z", + "shell.execute_reply": "2026-09-11T14:55:07.102009Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 2 × 5
ModelShort ATEBias boundATE bounds95% conf. bounds
<chr><chr><chr><chr><chr>
Partially linear9,1144,201[4,918, 13,311][2,725, 15,573]
Nonparametric 8,1984,529[3,644, 12,752][1,618, 14,805]
\n" + ], + "text/latex": [ + "A data.frame: 2 × 5\n", + "\\begin{tabular}{lllll}\n", + " Model & Short ATE & Bias bound & ATE bounds & 95\\% conf. bounds\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t Partially linear & 9,114 & 4,201 & {[}4,918, 13,311{]} & {[}2,725, 15,573{]}\\\\\n", + "\t Nonparametric & 8,198 & 4,529 & {[}3,644, 12,752{]} & {[}1,618, 14,805{]}\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 2 × 5\n", + "\n", + "| Model <chr> | Short ATE <chr> | Bias bound <chr> | ATE bounds <chr> | 95% conf. bounds <chr> |\n", + "|---|---|---|---|---|\n", + "| Partially linear | 9,114 | 4,201 | [4,918, 13,311] | [2,725, 15,573] |\n", + "| Nonparametric | 8,198 | 4,529 | [3,644, 12,752] | [1,618, 14,805] |\n", + "\n" + ], + "text/plain": [ + " Model Short ATE Bias bound ATE bounds 95% conf. bounds\n", + "1 Partially linear 9,114 4,201 [4,918, 13,311] [2,725, 15,573] \n", + "2 Nonparametric 8,198 4,529 [3,644, 12,752] [1,618, 14,805] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dollars <- function(v) formatC(round(v), format = \"d\", big.mark = \",\")\n", + "\n", + "row <- function(bounds, cb, label) {\n", + " b <- coef(bounds)[, \"ate\"]\n", + " data.frame(\n", + " Model = label,\n", + " `Short ATE` = dollars(b[\"theta.s\"]),\n", + " `Bias bound` = dollars(b[\"bias.bound\"]),\n", + " `ATE bounds` = sprintf(\"[%s, %s]\", dollars(b[\"theta.m\"]), dollars(b[\"theta.p\"])),\n", + " `95% conf. bounds` = sprintf(\"[%s, %s]\", dollars(cb[\"ate\", 1]), dollars(cb[\"ate\", 2])),\n", + " row.names = NULL,\n", + " check.names = FALSE\n", + " )\n", + "}\n", + "\n", + "table2.3 <- rbind(row(bounds.plm, cb.plm, \"Partially linear\"),\n", + " row(bounds.npm, cb.npm, \"Nonparametric\"))\n", + "\n", + "table2.3\n" + ] + }, + { + "cell_type": "markdown", + "id": "89ff8ba9", + "metadata": {}, + "source": [ + "Even granting an adversarially aligned confounder as strong as three years of maximum employer match,\n", + "the lower confidence bound on the ATE remains positive in both specifications." + ] + }, + { + "cell_type": "markdown", + "id": "d6f07816", + "metadata": {}, + "source": [ + "### Group Effects Under the Scenario\n", + "\n", + "The same groups again, now with the sensitivity bounds in red and the wider confidence bounds in\n", + "blue.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9b0a3776", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.104074Z", + "iopub.status.busy": "2026-09-11T14:55:07.103749Z", + "iopub.status.idle": "2026-09-11T14:55:07.236165Z", + "shell.execute_reply": "2026-09-11T14:55:07.235716Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "bds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0)),\n", + " c(\"lwr.bound\", \"upr.bound\"))\n", + "cbds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0.95)),\n", + " c(\"lwr.cbound\", \"upr.cbound\"))\n", + "\n", + "df2 <- cbind(df, bds[-1, ], cbds[-1, ])\n", + "\n", + "p2 <- ggplot(df2, aes(x = groups, y = estimate)) +\n", + " geom_line() +\n", + " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), alpha = 0.1, col = \"red\", fill = \"red\") +\n", + " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), alpha = 0.1, col = \"blue\", fill = \"blue\") +\n", + " theme_bw() +\n", + " xlab(\"Income Groups by Quartiles\") +\n", + " ylab(\"ATE\") +\n", + " ggtitle(\"With unobserved confounding\")\n", + "\n", + "ggsave(\"figures/401K-groups-confounding.pdf\", p2, width = 4.5, height = 4.5)\n", + "p2" + ] + }, + { + "cell_type": "markdown", + "id": "0ba2ae32", + "metadata": {}, + "source": [ + "The posited confounding scenario substantially widens the range of plausible effects but leaves most of the adjusted region positive. Only the second quartile has a small excursion below zero." + ] + }, + { + "cell_type": "markdown", + "id": "651c1a8d", + "metadata": {}, + "source": [ + "## Benchmarking\n", + "\n", + "Rather than posit $C_Y$ and $C_D$ in the abstract, we can calibrate them against a covariate we do observe. The procedure is as follows: drop the covariate from $X^o$, refit, and read off the gains it supplied. Latent confounding is then described relative to the chosen benchmarking variable." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b4a7321f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:55:07.238274Z", + "iopub.status.busy": "2026-09-11T14:55:07.237941Z", + "iopub.status.idle": "2026-09-11T14:56:25.265989Z", + "shell.execute_reply": "2026-09-11T14:56:25.265444Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "set.seed(2)\n", + "invisible(capture.output(\n", + " bench.plm <- dml_benchmark(dml.401k.plm,\n", + " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", + "))\n" + ] + }, + { + "cell_type": "markdown", + "id": "085c0865", + "metadata": {}, + "source": [ + "`gain.Y` is the implied $C_Y^2$ and `gain.D` the implied $q_\\alpha$. Income is a strong predictor of both eligibility and financial assets; IRA participation and two-earner status are considerably weaker.\n", + "\n", + "The `rho` column records the alignment each covariate actually exhibits. None is anywhere near $\\pm 1$, which is a first indication of how conservative the worst-case analysis is.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9837c2cf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.271430Z", + "iopub.status.busy": "2026-09-11T14:56:25.271070Z", + "iopub.status.idle": "2026-09-11T14:56:25.284321Z", + "shell.execute_reply": "2026-09-11T14:56:25.283894Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13660.0502-0.3372
pira0.04870.0081-0.1317
twoearn0.03990.0112 0.2589
\n" + ], + "text/latex": [ + "A matrix: 3 × 3 of type dbl\n", + "\\begin{tabular}{r|lll}\n", + " & gain.Y & gain.D & rho\\\\\n", + "\\hline\n", + "\tinc & 0.1366 & 0.0502 & -0.3372\\\\\n", + "\tpira & 0.0487 & 0.0081 & -0.1317\\\\\n", + "\ttwoearn & 0.0399 & 0.0112 & 0.2589\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 3 × 3 of type dbl\n", + "\n", + "| | gain.Y | gain.D | rho |\n", + "|---|---|---|---|\n", + "| inc | 0.1366 | 0.0502 | -0.3372 |\n", + "| pira | 0.0487 | 0.0081 | -0.1317 |\n", + "| twoearn | 0.0399 | 0.0112 | 0.2589 |\n", + "\n" + ], + "text/plain": [ + " gain.Y gain.D rho \n", + "inc 0.1366 0.0502 -0.3372\n", + "pira 0.0487 0.0081 -0.1317\n", + "twoearn 0.0399 0.0112 0.2589" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "out <- summary(bench.plm)$benchmarks\n", + "round(out[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" + ] + }, + { + "cell_type": "markdown", + "id": "70083858", + "metadata": {}, + "source": [ + "## Contour Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e65ef369", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.285890Z", + "iopub.status.busy": "2026-09-11T14:56:25.285593Z", + "iopub.status.idle": "2026-09-11T14:56:25.289940Z", + "shell.execute_reply": "2026-09-11T14:56:25.289562Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "bumps.plm <- list(pira = c(0.010, 0.009),\n", + " twoearn = c(0.020, -0.002),\n", + " inc = c(0.010, 0.009),\n", + " inc4 = c(0.015, -0.005))\n", + "\n", + "bumps.npm <- list(pira = c(0.005, 0.009),\n", + " twoearn = c(0.020, 0.005),\n", + " inc = c(-0.010, 0.009),\n", + " inc4 = c(0.015, 0.000))\n", + "\n", + "contour_panel <- function(fit, bench, rho2, title, bumps = bumps.plm) {\n", + " ovb_contour_plot(fit,\n", + " which.bound = \"lwr\",\n", + " cf.y = NULL,\n", + " rho2 = rho2,\n", + " col.contour = \"blue\",\n", + " label.bump.x = 0.015,\n", + " label.bump.y = 0.003,\n", + " lim.x = 0.16)\n", + " title(main = title)\n", + "\n", + " add_bound <- function(cfd, cfy, label, bump.x, bump.y, ...) {\n", + " sensemakr::add_bound_to_contour(\n", + " r2dz.x = cfd, r2yz.dx = cfy,\n", + " bound_label = label,\n", + " bound_value = confidence_bounds(fit, cf.y = cfy, cf.d = cfd, rho2 = rho2),\n", + " label.bump.x = bump.x, label.bump.y = bump.y, ...)\n", + " }\n", + "\n", + " add_bound(0.03, 0.04, \"Max Match\", 0.015, 0.003, point.pch = 25, point.bg = \"red\")\n", + "\n", + " # one-times each observed benchmark\n", + " add_bound(bench[\"pira\", \"gain.D\"], bench[\"pira\", \"gain.Y\"], \"1 x Part. in IRA\",\n", + " bumps$pira[1], bumps$pira[2])\n", + " add_bound(bench[\"twoearn\", \"gain.D\"], bench[\"twoearn\", \"gain.Y\"], \"1 x Two Earners\",\n", + " bumps$twoearn[1], bumps$twoearn[2])\n", + " add_bound(bench[\"inc\", \"gain.D\"], bench[\"inc\", \"gain.Y\"], \"1 x Income\",\n", + " bumps$inc[1], bumps$inc[2])\n", + "\n", + " # a quarter as strong as income\n", + " add_bound(bench[\"inc\", \"gain.D\"] * 0.25, bench[\"inc\", \"gain.Y\"] * 0.25, \"1/4 x Income\",\n", + " bumps$inc4[1], bumps$inc4[2])\n", + "\n", + " invisible(NULL)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "2522b94d", + "metadata": {}, + "source": [ + "### Partially Linear Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "445dba1a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.291459Z", + "iopub.status.busy": "2026-09-11T14:56:25.291151Z", + "iopub.status.idle": "2026-09-11T14:56:25.693510Z", + "shell.execute_reply": "2026-09-11T14:56:25.693103Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title \"paste(\"PLM, |\", rho, \"| = 1\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "options(repr.plot.width = 7, repr.plot.height = 7)\n", + "\n", + "pdf(\"figures/401K-contour-plm-lower.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3f14d055", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:25.695962Z", + "iopub.status.busy": "2026-09-11T14:56:25.695610Z", + "iopub.status.idle": "2026-09-11T14:56:26.093514Z", + "shell.execute_reply": "2026-09-11T14:56:26.093039Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title \"paste(\"PLM, |\", rho, \"| = 1/2\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "rho2.half <- (1/2)^2\n", + "\n", + "pdf(\"figures/401K-contour-plm-lower-rho2.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))" + ] + }, + { + "cell_type": "markdown", + "id": "8c69bfb3", + "metadata": {}, + "source": [ + "Under worst-case alignment a confounder as strong as income crosses the zero contour, whereas the matching scenario and the one-quarter-income benchmark do not.\n", + "\n", + "The second panel makes a different assumption: errors in the outcome regression and in the Riesz representer are only half as aligned. The lower endpoint then rises substantially. This comparison separates the strength of a confounder from the degree of adversity with which its two effects align, and none of the observed covariates exhibits alignment close to the worst case.\n" + ] + }, + { + "cell_type": "markdown", + "id": "69a6db36", + "metadata": {}, + "source": [ + "### Nonparametric Model\n", + "\n", + "The gains are target-specific, so this specification needs its own benchmarks. Here `gain.D` is read as a relative gain in propensity-score precision rather than as a reduction in residual treatment variance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "629fe818", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:56:26.095791Z", + "iopub.status.busy": "2026-09-11T14:56:26.095475Z", + "iopub.status.idle": "2026-09-11T14:57:52.635257Z", + "shell.execute_reply": "2026-09-11T14:57:52.634727Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13150.1288-0.218
pira0.02670.0000 0.000
twoearn0.01130.0000 0.000
\n" + ], + "text/latex": [ + "A matrix: 3 × 3 of type dbl\n", + "\\begin{tabular}{r|lll}\n", + " & gain.Y & gain.D & rho\\\\\n", + "\\hline\n", + "\tinc & 0.1315 & 0.1288 & -0.218\\\\\n", + "\tpira & 0.0267 & 0.0000 & 0.000\\\\\n", + "\ttwoearn & 0.0113 & 0.0000 & 0.000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 3 × 3 of type dbl\n", + "\n", + "| | gain.Y | gain.D | rho |\n", + "|---|---|---|---|\n", + "| inc | 0.1315 | 0.1288 | -0.218 |\n", + "| pira | 0.0267 | 0.0000 | 0.000 |\n", + "| twoearn | 0.0113 | 0.0000 | 0.000 |\n", + "\n" + ], + "text/plain": [ + " gain.Y gain.D rho \n", + "inc 0.1315 0.1288 -0.218\n", + "pira 0.0267 0.0000 0.000\n", + "twoearn 0.0113 0.0000 0.000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set.seed(2)\n", + "invisible(capture.output(\n", + " bench.npm <- dml_benchmark(dml.401k.npm,\n", + " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", + "))\n", + "\n", + "out.npm <- summary(bench.npm)$benchmarks\n", + "round(out.npm[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "2aacd3da", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:57:52.642707Z", + "iopub.status.busy": "2026-09-11T14:57:52.642270Z", + "iopub.status.idle": "2026-09-11T14:57:53.089117Z", + "shell.execute_reply": "2026-09-11T14:57:53.088690Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title \"paste(\"NPM, |\", rho, \"| = 1\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pdf(\"figures/401K-contour-npm-lower.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5f1eab78", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-11T14:57:53.091609Z", + "iopub.status.busy": "2026-09-11T14:57:53.091253Z", + "iopub.status.idle": "2026-09-11T14:57:53.519948Z", + "shell.execute_reply": "2026-09-11T14:57:53.519500Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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wA2oQiF0NhA1TuXbzqc7VSOdSAw/TDXo+dBv7FWbLFG7V9qyaalVRuCH7fSzkuqAGAeVqUBY2WDtWi6KwIesWaNgW1ftiSlOcyqlSh3Ki1a1c5ulcr+vWrTC7qf44dftVyzsVu5wyMQW9oAYB1DAZFy0KPWHD8ak6V7W/WT92X1b5bv2GwQqzm252xa5cE7L7XL2ubDigBgHUMJ3Q1DDn4A8zrs2X6u++tu+qFeAqhivMboYTryqqwOJolq8o7QPUILCiGuhskFDihj8z6jvc19MuRys57c223Weghl3ZoOgfb3OpuiP6xR4ssEI/JGoQQA3TCa5FMePg+z4qKDluKzcM1bBr+hjzfBgkbK7l5l274Z5VS7jcf1o19omNezegBgHUMIOIw4ZmuYV+Tcdzt35To4Zj2du4G+zdpHApn0zTbGjMMF5M7mfcuwE1CGhXg6rOBgfTL9WqYbiCSzbqY8zHNb9M4l4749aawa4aulI4+6pQg0D8alhtjs5SVLhhtGZs1YlYLwHb3qHYf1TDteq0PPfNCLtqKOKGBqtH7UENAqjBPxpuYA77Go5lJ+KpfHpV+aiqeuTCuW5QNAMmBzV/a/4rf1Q3Oa/9UrFW+xq6FoW7hgVqEEAN/tEQNgzvUDQLwt7boY5VEFH1Qrb+aNRQ/jpV24/dHtVbVu9Q5I0UHHY5oAYB9WrADascezSuoVsQtppD0SwBe8zMrl0meqCGfm7F4Nk01sc1FFpw2RmJGgRQgwI0hA3HH67nlySM9dGQXZvCSYcDahBADRrQ4IbM3rOt7c+haIRQNyxsHxw1iOhXg67bl27QoIbrL/V5nIb1mZdNe2LgB7ugBgELaiBs+B0NbujXa/gxDbvrNfRHbxsVto+OGkRQw2yCG/e0hhrXSGLjLCnUIIAa5uJktDRumJyCi4RQg0AAalDmBtTgKY2+VWH9PgVqEEANc3GjBtzw/fib5k6F/fsUqEEANczFydTsHDdMOP5mMCrSatiAGgTSUANhwyo3adZJxMF9CtQgsKoaCBs+Eb4bVknDRW8kahBADfMJUg2xuMHJfQrUIIAa5hNm2BCJG7r7FDZvU6AGgclqoLOhJ0w3xNHd0N6nsHqbAjUIoIYlBKmGaNxgfzoFahBYVw2RtCgCDRvWb1I4qyOWb1OgBoFU1EDY4P7wL4mYf44qie3bFKhBADUsw4kZInODKQTqppbYvk2BGgRQgy4ickNpBkdu6G5TWAI1CCSjhkDcEIMa6lRqM7hxQzedwhKoQSAMNRA2BHL4PpXWDK7cYLW7ATUIpKOGQNzgOpcrNSl6M7jqb+jT+h3UIIAatBGFG4ZmCMANqEEANagjiibFelGDjRKhBoGE1BCKG6IIGxz3NYz5uUSoQWBlNRA2TCAuN6xRT34tEWoQSEkNgQx7ikQNLsc1vPBjkVCDAGpYjKPB0nlUblirlvxWJNQggBoW42qOVR6RG9arJD8VCTUIhKIGpW6werwe51V3LTesk0wJarANalgOYYOadH5MCjUIoIYfCFcNuGEEahBADT8QcNiwVp1d84bx8rRQg4AVNaTsBrsH7MENK6aFGgRQwy+EHDZE6YaFiaEGAdTwE4QNetL5ITHUIBCOGlS6gbBBUTrLE0MNAqjhN0IOG3BDC2oQQA2/4VAN8TQp1LsBNQikpgb7brB8vAHxhA3a71OgBoHV1eC5H3Ld/8B+JB43KA8bUIMAatAMblgnMdQgEJAaEnRDPGrQ7QbUIIAaVIMbVkkMNQjYUUNI/ZBBuWGFvOIG1CCCGnSDG5YmNic11CCwvhp890OGpQaaFGskhxoEQlJDkm4gbFghOdQggBqsEPTAJ9yAGgRQgw2CHi69UhrrJjQvOdQgEJQa1LrB4QRM3LBCcqhBADVYATXoS2lGcqhBIEk1EDZ4SWPtlKYnhxoEUIMdXKoBN7hODjUITFdDTG5ADZ4SWTmlqcmhBoE01RBYiwI3OE4ONQigBks4VUNkTQp1bkANAoGpQa8bCBuUJjUlOdQggBpsEX7YkK4bUIMAarCF27AhNjesPY/l80RM1CCQqhoIGzymsm5KExJEDQI+1KDCDQ4uTZdmwA0uQQ0CyapB1aU5hdjUoOkEoAYB1BAMuMEZqEEANQTDShle0w1azgFqEEhXDbjBbzKrp/UB1CDgRQ063KDkspwBbnAEahBIWA1KLss5RNfdoOQkoAYB1BAUuMEJqEEANdiG0Q1603oHahAITw3K3eB2TCTdDU5ADQJ+1BBx2OB4KkWEYYMCN6AGAdRgG8dqwA0OQA0CqME2cYQNabkBNQjYUgOdDR1xhA0ru8GvHFCDAGqwjuuwIUo3+A0cUIOAJzUoaVGEGTbE2N3g1w2oQSBENRA2OD362sl4Sm4IahBIXA2EDR+TSaVNgRoEUIMDYgkbkulvQA0CQaoBN+AGq6AGAdRg71ADYmlSJNLfgBoEUIO9Qw1xbIZYwwZPbkANAmGqIQA3uAY3WAQ1CFhTA2HDyuAGe6AGAdQQqhvWy3b8XZGoQQA1hKoG3GAP1CAQqBpoUeQ0KeyBGgRQQ7huiDVsWP+EoAYB1BCuGnCDLVCDAGoIWA3RNinWnr2BGgRCVUMYbnA98CleN6ybIGoQ8KaGFMIG58Ol421SrJsgahBADdYP1uN8lhVusANqEEAN1g82wL0acIMNUIPADDXQ2TCbFcIG3GAB1CCAGuwfbABuCCJB1CDgTw1JtCjialJE6wbUIBCuGsJwQ1xhQ6xuQA0CqMHBwYasoAbc8CuoQQA1ODlazxphA274EdQgELAaCBs61qyvMboBNQh4VANhgz1ww0+gBgHU4OZoA9ZQA274CdQggBpcHa5nBTPghp9ADQIW1RB4Z8P6F7xVolaD60naqEEANTg73MrE7Qa3SaIGAZ9qSKZFsQ64YTGoQSBoNeCGEbhhKahBADW4O9zq4IaFoAYB1ODucOuDG5aBGgTCVgNueCJ2NzhKEzUIeFUDYYNtVi2Aj2/LTZqoQQA1OD3e6uCGJaAGgcDVEFTYEN2oyGjcgBoEbKohgs4Glxf7KnMpcMMSUIMAanB7vAGrTMHMU3CD9URRg4BfNSTnBncH71m3tnrpnbGdKGoQQA2OjzcgyrAhCjegBgHU4PyAPSupATfMBTUIoAbnB+yJM2yIwA2oQSB4NYTmBncHH4Ab5oEaBDyrgbDBDbhhFqhBADW4P+CASNXgyQ3WUkUNAuGrISg3xBo2eBphbitV1CAwRw10NvwObtCYKmoQiEANwbnB4dEHpNDdYCtZ1CCAGtY44JCVzIAbZoAaBHyrQWWLwtdlbpc0mhRWkkUNAjGoIbCwYTVww1RQgwBqWOmIPsANE0ENAqhhpSN6YXU3eLpP8WuyqEEgCjXghnesXowwAwfUIOBdDYQNTknFDb+BGgRQw3qH9AJumABqEEAN6x1yRKzDG4J0A2oQ+FaKeZMoIupscHyFrzYoMi03bJb1SKIGAdSw4iEHrDaXwkdN9eCGTfdriRxQg4B/NWhtUeCGgFLcDMIF1GCHSNQQXtiAG2wn2aaJGuyAGtY95oCYuxs8jm+gQWEJBWpItEWxZtiQhBuqFJuGxUw/oAaBWNSAGz6TgBt6J5R+mCUH1CCAGlY+5pCo1eBnctema1Kghh9BDasftCfusGHtJHs1zE49CTVc//amZH+8Tto/GjWEGjbgBnvJ1bcwN6hB4LE1Pbspn7CsBsKGeayoBq83FNdKr09zIInvJKCGo8nOt+rV/ZKZ44RPxKOGIMOG9aZS5H5uKHrr/tzM6YxMQA2ZuXWvbyab8AnU4OGgvkjCDW1zYk5nZAJqMObdH28/8eV91BATKbihuX05qzMyATUkHTXghu+k4YZBUwI1NBxNdrlXrxLsa3B0DeKGINPsOyMndDgkoIZ8N7hDsX1M+IAKNah2Q1xqSMUNXWfkpN7IFNSQX4/VuIZs/+dlXEOEaojMDV5K4+s+xdShkUmoYS5RqYGwYQJpuKFrSUzqckANAqjB11FHxD68wYcbZgyNRA0Cs9TgaqS08hbFCgOfVhwU6csNPlOlr2EBcakh1LBhzbkUua8mkscO0G9JowYB22qIskWBG0JNdeLghgTUYMZM+cSX91dSg3I3uL+ocYObFBnX0HBCDZaOs85hB6yrhoTcMOXRFAmoIb9lk6Zi96AGn4cdsHLYkIwbJpGCGvLbpOHRPbGpIdgWBW7wSBJqKNoUt4/vm3n9EWupgbABN/gjDTXMBDV4PeyIJLoiVboBNQhEp4bA3bBCKh24oQU1CFhXQ6SdDTGGDd7coE4OqEEgPjWE7YYVEhngq45qc0NqavAzrgE1hARuqEAN0k5f3l9NDepbFNouZzvghpLU1DCJCNVA2DAH3JCjBhHU4P+4fsENqEEENWg4sFdwQxpqcPrMS6dq0N/ZoOlitok3N6j5PhNQg+NnXk67iAgbQsNbHdXyfSagBsfPvExeDWtdyysPb0i+UZGAGhw/vQo1rHMtrzwosiTtRkUCanD8zEu3agjCDetcyQm5QUXgkIAago4aglDDamEDbliRBNTg+JmXE8+i/xYFbpiPvxrq3w0JqMHxMy8dq4GwoSctN/iWQwpqcPvMy3DUEHxvQ1LdDd4DhyTUMBfUoOnQA5LqbvAdOKAGgVjVEEXYkJIbvKaNGgRUqYGwYYgXN6TZqEANAtGqATcsI8lGBWoQQA26Dj3ChxqSbFSgBoF5aqCzwf2xh3gwQ5JuQA0C8aohCjd4IT03oAYBXWoIJmzADc7S9pE4ahCIWA2EDYvxWjoPiaMGAdSg8eDeScwNqEEANeg8um/ScgNqEFCmhoDcELcaPLth5dRRgwBqUHr0MT7GNyQUOKAGgajVEE3Y4GXsU0KNCtQggBq0Hn2IlyHTvt2wYuqoQcCJGnCDbVJ0w4qpowYB1KD26CNwg0tQg0DkasANP+LZDSsljxoE1KmBsOENSbphpeRRg0DsaojLDSum1pGCG1CDAGpQffghntSQghtQg4A+NYTlhvh7G/y7wX36qEHAjRoIG1yQqhvcp48aBOJXA274ndjdgBoEUIPyw4/wZAYFbnCbAdQgMFMNIXY2xOQGb3gvpNMMoAYB1KD/+CrwXkiXgQNqEEhBDbjBBt6fWevwa0YNAo7UoKqzATXYwXsxnWUANQhoVANuUIr3YrrKAGoQQA0hHF8L3svpKAOoQQA1hJHAE95uYvqWg5sMBKcGs0IiKtWAG77ga+hTrkEODo4ZoBqM81RQQyAJjPHoBu+tCgdyQg1iGp+JRA2xucHbkOkS326wL4fg1LBGi0KnGgIMG3BDwDkITw0r4EoN6YUNuGHVHFjNQkBqMB0OE6lT+vI+alCVxIDE3WA1CwGpoT64cZ3KhON7UkOQbli/KxI32CEgNZjuF2qwRXRhA26wlgfU8D6lt8SjBtxgFwVusJYH1PA+pbegBnVpDPBoBgWDn+zlISA15G0fZKrdkKG6QUFtWRENpbWSh5DUsBrO1EDYkACxBA6oQSAlNeAG+2go7u95CEwN66gHNQSYhiaiKC9qWJCKNzXghkDQVN7NwtYFaliQynI1pBo2eKkrid+paHKw2Sx0A2pYkEpcaojVDT7HN+QKAofGCP2PmaCGBan4UwNhwwxSd8MgdEENK6USmRpidYPXcZG5CjcMX8xtVqCGBamgBr2pDMENDYUV5nc5BKaGdVCsBtwwB+9u8C+HTe2Ezfy4ISA1RLBeQ9pqSNANngOHPlQY/pxIQGqIYL0GG5dKwG7w0xWZrhv6JsQmbjVEMPMycTWk6QYNctj0WZmcH9TwPqW3vHy5wbcocINDvDYqhjrYzBoAhRrep/QWr2oIOmzADV5S74KGOXFDQGrQu15DBGqI2w3rpznG952KTZuJOV0OIalhNXSrgbAhQPzKobLCZtQb+T1DqEEgRTXgBsd4v43Z5WHiAKig1OB+REOTzpf3o1QDbnCM/3J3QcOkLoeg1LBWUKJcDYGHDQrqiB+8l7sPGgZ/vgU1LEjmFzUQNiioI57wXe62N3LaAKig1BBDgwI1rJrSGN/3KnzfqGh7I7u/PhGWGpwefXo6kaoh+rAh9REOTW/kxFHTqGFBOr7VgBsWosANvuWQTw0awlIDfQ0Wj7HmcX2mNMK/G/qsLVYAACAASURBVBTIoRsA9Zmw1JCvMk7arRo0tyhwwxr4lsOgUfGJANVggp5DQdiwflJDNLhBgRwmZAA1iGl8Jl414IaV8N6q+E5wagh95iVq8JLWANwwjfDUsAL61YAbfgA3TCIgNehdG3LmaSZs8JDWECVuUC6HgNSgd23ImFoUibjBU8JjdLshIDUoXuWJFkUIielDdfFRw/uU3hK1GnDDimhuVKCG9ym9RYMacEMc6C1/QGpQvDYkagglNX2oDRxCUsNqOFaD9hYFYcO6KP0GUINAEGogbLCBinsVOgOHwNSwjnpSV0M6btAxwkFn4IAaFqQSvRpww9ooDBxQw4JUdKghnrABN+QKAwfUsCCVX9VA2OA5uSG44Q2oYUEqStSAG6ygxw265IAaFqSCGkJPboQaN+gKHFDDglSEMxhbZwNu8IKmwEGrGtq512a4ydbBvyb+5X0tasANltDjBkWBg1I1yPOv1T6iJsYWBW7wg5rAQbMahJlUK6zjkgekhqjCBs9u8Jj4E0rkoFoN4gqxGld5ilMNSblBFSq+CN1qeGlC1F7w3dmZihpwgy80BA5K1dA5QexrQA32D+Tl8N7T04t/OWhVg3TseO5QEDaoSVAvvr+KgNSwHqhhteP7T/AZPf2RngOHcNRw27s79hMBqQE3WEbRbUzPctCthuvOmN2tfHXbr9eeQA0rHl9DiiNUucHnl6FaDdd6Nchbfi/EYI5Wj/0J1LBmAgpSHPLvnyo5+AscVKthV+rgaHaXQgz7h9VDf2QFNeAGVSmO0OUGb9+GajU00yhMZvY3qwf+lu6X91FDfEkOIXAoCUIN26vVw35P98v7qtSAGxygyw1+5BCEGqwedEq6X95PTA3pNSnUBQ4+5IAapHS/vG9DDSG5IcGwQV3gsP43ghqkdL+8n5oacIMG1g4clKthhNVjf0z3y/vK1BCpGzTIwXcOxqz7jaAGKd0v76OGVfDvBm2sqkvVavDFAjX47IfEDemwohxQg8AqaggrbMANWljtO0ENAsGpIdawQUOHgzrW+k5QgwBq8JOEomRVs44cUINAeGrADY5RdrNiDTmgBgHU4C0NPcmOUDbEIV9DDqhBYB014AbdyY7QNv4pd/+1oAYB1OAvDU3pDtHoBrffC2oQCFENuMExCt3g9ntBDQKowWsiitIdoVAOLgMH1CCwRA24Icp0Ryh0g8MvBjUIoAa/iahKeIhKN7j6ZlCDQJhqwA0roNANruSAGgRQg/dUNCU8Qp8ZckdyQA0Ca6khSDf4q6E63KATB3JADQKowX8qulIOgI1tO6AGAdSgIRlVKQeBXTmgBoFQ1YAbUsemHFCDwGpqCDRswA0VOnskrR0JNQgEqwbcsCIa72PmFgMH1CCAGrSkIyWtRg4aB0CVWPqGUIPAIjWo6GxIwQ16Agdtj7jqsPINoQYB1KAoIWVpj1DrBhugBoH11IAbgkt7TMRyQA0CqEFXSrrSfkK7HBb3PKAGgZDVkIob9MhBpxvq72ezWT5IEjUIBK2GNJoUvlMfodAM/bjpxd8TahBYUQ2EDYGmrp/aDZsufJj7edQgELYacAP0tGaY/3WhBgHUoDApKXXk8I1h6DDz20INAoGrIR03+E5eP5vxzzntCtQgsEwNetyQjhq8p/+Crh7JUdCwmdeuQA0CoasBN3hD2X3McdAwr12BGgRQg9K0xPR9Z2CMxkEOnRoGf30HNQisqgbc8HMGvOdgiMIRUE2wMGhdTOlzQA0CqEFvYkpzMECfG2oZDIKGSX0OqEEgfDXgBp+ok0M9XHpwn6J58RHUIIAaVKemMwcjtLmhohPCtD4H1CCwrhpwg5Uc+M/CEI1u6JoSwxHU70ENAqhBe3JKszBEnRnadsXUGxWoQSAGNeAGeANqWM7KasANtrKgIA8BMHHgE2oQQA0hpCehIQ/qafscvoAaBOJQA27QgrZuh2lLP6EGgbXVEEnYoKJeKmxUqBvmMA3UIBCJGtJ0g45MjPgXpBxQg0AsakjVDSpyMSRIN6AGgdXVEE3YoMMNSnIxIjw5oAaBaNSAGxQRmhtQgwBqCCtJAX2NiuDcgBoE4lFDum7Qko0RQbkBNQgsVANu8JakhJJsjAjIDKhBAjWElqSIlnwECmoQiEkNSbtBS0aCBDUIRKWGlN2gJyMBghoEPKghrrBBT5VUk5FXtPc7oAaBuNSQuBvU5OQJ9bcyUYNAZGrw4wY1VVJNRp7QPnoaNQj4UENsYYOiKqnHUmP+6ZYDahCITQ2pu0FRTsaolgNqEPCihvjCBkU1Uk9OntHrBtQgEJ0acIPWRkWutz8SNQgsVYPeFgVu0JSVZ1SaATVI+FFDjGGDpgqpOHBQCWoQiFANuCFHDvNADQKe1BBl2KDKDchhBqhBIEY14IYaXbl5RU+/A2oQiFINuKFGd+Cg6HYFahCIUw24oUG1HPQMgkINAovVoNsN/mqEtrqoWQ5qRkiiBoFI1eDTDdrqoroMDVAiB9QgEKsafP7vra4qqsvQEA1uQA0C0aoBNwzQHDho6I9EDQLe1IAbVkW7HPymjxoElquBsOFT2vpqor4c6QE1CPhTQ9xuUFgTFepKC6hBIGY14IYnFGbpFR+NC9QgELUacMMTAQQOXvokUYOARzXEHjZodEMAcvBxMxM1CMStBt9u0FgPVWZqiIdRUOmo4bQ1Zn+ZtKtPNcTvBt/Jy6jM1JDVx0gmoAZTZWpnKo6TPvHl/dDV4LseqKyG6gOHteWQihqO5vjI8/vRnKZ84sv7ny6iEFoU3iunzloYjBzWSSwVNWTmUb5+mO2UT3x536kaUggbtNZCnbkaQdRgkUoNxgz++PqJL++Hrwb/lcB7BkSUKssLqajh0Kohm/KJL++7VQNu8AlyaElCDfu/09mci5eP46R+yBTU4L9qaq2EWvMl4LRxkYQaaqqX2WPKJ768H4Ua/LtBQQ5kQpGD2y7JBNSQ326n035fdUYep5jBtxpSCRtUZEEmEDk4vV+Rghpmk4YaNFRMBVl4g96cDXF5NxM1CPhWQ0puUJAHGb05G/LP3TioJNRw/dtXvQ3743XS/qmoQYMbVORBJnE5JKCGx9b07KZ84mspnI6UtncULcl8RkUmZBRnbYgbNySghqPJzrfq1f2SWbl5SdhgFRWZkAkjcMidDJJMQA2ZuXWvb1aGPLlXA27QQjBysE4CahiNjbYyUBo1WEZ1/VOdOYckoIYgo4bE3KAlGzKhycFO6yIBNRxNdrlXr8Lpa1ixrii57JVk4w1BycFSr2QCamhXcanYisMhzRPfjhiTGrRUSuW1T3n2Rti5nZmCGvLrsRrXkO3/7IxrWEMN6blBe+1Tnr0RNgY7JKGGufykhvDCBjVu0JMRmZDk8BQ6LLAEahBQoYY03aAmJzLa8zdi4Ibi52w3oAaB5NSgxw2KciITlBzy3gzzF3dITQ3uxzXYu7hxg0rCkkNF74cZoAZpp287xKcGRTVSf9XTn8Mx/1CDLX5TA274FUVZeUNYcvg3+Dkd1CCQpBo0VcgAal4AWexADdbQooZ03RBCzdsEkMeap/bExBuZSajB9lIukapBlRtCkEModvjX/aheTBwMlYAa1l3K5eu7s0jZDUHIIYhMlh74N/xzkhwSUMPKS7mErAZlbtCWHZkQQocnD0waR52AGlaelG33gk7dDcry84ZAsjnkuxsSUMPKS7mErQZtbgil1gWSzSHf3JCAGlaPGnCDVdRlSCZAOXweBZWAGlZeyuX727OYe6xsf2oKe9q/1+DbvU7ZMMVJMZZzQql0oeRzIgmoYcJSLk9oUsPcgxVlrO13+LQkzdu9yr82o780EEqdi0oOKahh3aVcvr89j9lq2NZhQLb9qIY3e1V/bUZ/aSCYOhdMRr+ThBrmokoNM49mzF/Vt3ItfpfluBZWzIoIYV9uvZnt572a1e/+e1/9VarhWL/yTjBVLoR7mZNADQJhq6Go7sXvv+J3UY5L3Y465o9ytNeu7ZF9t1ethkdWjR0td9ubru3hmYBqXEBZ/QBqENClhnmHK6p2VoYGW1M1B7bmXAYLprTA5WwO3/aqgoajOWyu9V+7R/HBKXd1ViCgGhdD6IAaBH5Vg8+woajRB3PP74UF6p6C++VvV73KMpM9vu1V/bU1jzpRY+65oh6HkOQQvh1Qg4AyNcw6XFGPL0UMcCr+VVV61y2ffzbm9HWv6q+6L3LTSkGPGsKSQ1iZfQE1CASuhrJbYVf8x19W6YPZni73Vg3nr3v1aiiT1aeGgPojS0IOHVCDgDY1zDleWY+LGl92OnZV/VE3KLbb7OteXYOiSlajGkL7vziw7PagBoHQ1XAy+/L+Q13Vr/lj13RDXqq7Eh/3arohj4NOSW1qCK62BRo6oAaBn9Xg0Q1lPS6aBuVdyvLlsX1U36Mc0rBrwoG3exV/FZHFvbp5uVWrhsBaFXlwMqtADQKhqyHPqvuN1cuDMbvyRuS+CAyKUGD/ea8ilCi33YoI4nAv3+s7I3URXF0LLsOoQUKfGjz+N6n1kg6uroWWYdQgoFANuOGV0OpaYBlGDQKoQUvSX9CbszeEJAfUIPC7GnDDOoRU1Wo2wdyvQA0CqEFR2l8IpaINCUMPqEFApRpwwxtCqGav6LcDahDQqQbc8A71tUxGebZRgwBqUJb4V5TXsneoDh1Qg4BSNcw66OWQ5+et2daTLW87kx3bGdm7rny7cUmPzeDHZmjk4bI0cQ9ormSf0Jtv1CBgQQ2+3XDNqqmWpp6IfateZfUi0qdudONpPM7xaIZqKA6QjZbS1HsR12jP3zu05hs1CESghuxUTqC85pdq4POhFMSpXsjt1q0h3b+qGC0uXa7mUK48vzB5P2itZN/QmW/UIKBWDdUSCmPk/Y6j6dftNKnqWcDbrP1Q/6oky66DP6vl4fLnFWNVXsJDdFay72jsdEANAnrVULrh34A3OX10K71eTLMydPvzz5wbA/SvKo7DeVRHc6l/PT22Q98F/IzCOjYNdXZADQLBq+HPND2I+3qq5a5sUFRdCeXcy9oA/auO7s9Hsyh9v8BDl76y61cggCy+QZcdUIOADTWs4oZ3Gd2buscxP24rN1zLpke1VEuWPRoD9K86uj//mqXi7u0k7lEG1KOqhs1DkR1Qg4BmNeSbCWoYVPlz1aK4FIp4VMtIX7oVGi75WzVk5vVAfQbUXLzvCSGP79AyjBo1COhWQx82vM3nsEZ3r+9m13di5lI/Zn/rYiccaJgF/eioXkvRYAfUIKBaDQM3fFFDVnUidrX72jyd6rsaTt2q9G/ugPi/biegoHb9gnc7oAYBK2pw6AbzxQxNX8Ox7EQ8lU+s2hath8e+f6pdu9+bBkW3p9TXUGchiGoXRi7f4zf/qOGF45v/Kpv/ZQ83odY/DQ6qn/Hysqn9j3p/7Y64KIeNG95/uL5DUT+6shwDeTLDR1e+qKHd0A14aO9Zvt6h6POwKOdr4/2/3h/xmX/U8MzxXYXtQvDba72Q/vt9r4Zybfe8fgjtaJrCVOomxYfvuhnXcD/Uq78WbuhmU+QT1NDt8DKuYZiJQCpdKPl8hzc7oIYnDm+HGDabj2b3VQ0Vkhqql49jPWzgYPbd82nnsfmihmai1M98fkZ2KHUu9NDBUwFQw5hquHBbiqrNfWuG/wz/T93k130RqtcDDW/Z7lkntQfue9OPNB6ooR+d+FjaoijChs+fzE4f357G8xyKl2wEU+XCyekbPNzRRA1jjsMegOrBb7vn3rtSDRfTtt7LoUQvkUbtgf94buCPo4ZLETIclrUoyrDh81d9tfHg+/HMSzEfFlJZh+BDh9WLgBpeGFTzP3M5dzF/vflxKDZstuVwwebZb/0khcEhyn//z6N54ku/adjXUM5TuCxsURRu+LLDZeGBBxwmaCukChdSXt+wph1QwwvDCCDLTPbot9fcy/8s75e/5iGR9/yNGv47f2pDtAc4VHFINeRw8ZOh1FzmIVW4kPL6jtWaFqjhhaEazsYMOvare4H1zctdO15IfCxk/cYmf1FDXrYj6qGG18YTy1oUitygKCffiUEOKwUPqOGFJzWc++3d5s3BbE+X+xc1lHXmVQ3VLY68fwTt0sBf0TUeVH0LKrPvcW8H1PDCqEGx3Y4XRanZVK8fy9SQb6uBRPUw5odZ3F+o6RLXlJevRNAjWeG4aYEaXhh3Q/YDAsdquOaP3UI13MpRU9cmXDiYr7cB3qHpAg+stgWW3fc4tANqeGF483Jb3rx8tNu7XTZta6BXQ5YLHtiIaiiMsy3aE7USLp+HFX1E1fUdWG2LJXRwVxLU8MJwyNO1Xg2p2d7tsikHTe6ufXU/zVJD2aTI2oZE9sMIBF1Xt67cfCcaObhpWqAGga+lmHoenF977xNoh0o3a7nt3pRp8c1TMTeh1bV4QgcHekANAtbU4NwNb4/fTR+tV3k6vVOAVTUEKIcAc/wBwQ7Li4caBMJRw7sE+mdK7OpG0UpqCK9VEVfo8BI8/FA21CAQkBrkFAbPlKg6QYZPnHidMzaYCNa/aOdzFzuUy1Lfy8Ul82qcuDm8n6mdh/nfcIh5/kBXnM4TS/SHGgRCUoOYRP9MieoGyOiJE69zxozJmolg9eIv+24VmEf55r54dd42g7Oq7dsvOQqwosUVOjSnYPPyYg6oQcCeGny5oVNDOWzi6YkTL3PGjNk1E8GOxfYq3jjW/qgUcyjHhB7zyi5/5aaj+TbjO8hqFpkdajZPP2aUETUIhKUGOY3BAk7PT5wYzxkbzhDrVn7blpvuZXhQvWnMo92h+tCbFSMHWQqzlmlZ590eZXG6yGFe9IAaBAJTg5hI7YJruybEUA3jOWPDAZ0va800r7ofwjLU7/IUah2Lyg6bkRrmRQ+oQeClFNXiB936ivdqvFP56twtuXjd9avB1jQDCzbNmIIpix8sRjjP7VSuSz+ZvH3rec6YEzUE2qqoiEcOmyZWqH7OjB5Qg8BzKa71g+hLTmWc3a7Gcm43lTOt6gVlO9qBBZt2TMH3JZN+yOzreW4SHVTnrlTPc8Z6D4gNinzwYzvnBAdcw2IJHfo4YaiGwdYPoAaB51KUCy3eyr66c1lZDua4yas13LaFHy7V3cFT2T13GCzN3g0s2LRjCr4ttLg0r/9kN1SJ3p+WqKp4mTPWq+FYFOP23A2ZD36UOxTfwi6fRsgVLAo7DJsQQvTwEdQg8FSKY1mpj10Qnr1OqWwHC3Tdc4OBBf+rHVOQLZ9H9SGr7dLSz2e6SrR7YvZADa9zxno13Nt7k8Obl/ngR719GB19IejqFbwdBhLYSA2Lj6AGgXEp6oc67NqHT5eUamhu7tczJ7OudtX0Awv6MQWfHuqwOKf9s2qeTnWVaNdAGGTtdc7YYF75bdeMdBoOeRr+6PtZJhJ47Qo8+xVtV+Rz9PAF1CAwLkX9X281KnDb/Ce8KYxQdz/u6zomrNpQ/3Ez/7sfffT2UVCLMzp8+KXWizjw2hV86NDdk32OHr6AGgTGpdg3t/137VLQxdd6z5rm9nFbueGtGrLsf9rNbx8guTyf//6F4IbQ5RB8/hueo4cvoAaBcSnaan8sextrI/x31nfEnbvOOkEN5ZiCTTedwfaX/e9fIG4IvnJFEDrkL9HDF1CDgKSGYW/C7T92T+83asiePza6cWhZDUMzqHdD8HKIxA4l/TCoj6AGAUkN+752n83/rt+pV36t7/eP71DkK6jhNWpQ7Ybw5RDNOOpp5UANAlJfw7luUBzKwce75os9lh2L1bZyXlLRehhOO+pFsFZfQ4nqSzeGihWLHr6DGgSkOxTNQ2nu7Yv+Ln92r9dKMaYLIvJcUIPrOxQ1uq/bOOpVGnpADQLSuIZy5JPZlWN9Bm2E7u5/NYeivt//Vg2OxzW0aL9oI6lVkRTjA6hB4Hk05Eup5l0Wzd4/rCr/HvNshtfMNQ/GbRaO7eeB9TZ7HcfUFLmdPmZ3blgktSr20AE1CEhzKEbMvCaq3d3OoXhJruNap9tM8urngV26R27288Vamrlh/fQxy3PDYqlUUbcsUIPAcymuz7V6iRqczrwU0mupvdZN8urmgZVzw/JrM18sr+eLNbRzwwbTx2x7LZ4qFa0eUIPASykuT8+sXaAGp+s1SAk2HOtK3S4c288D62+oPk8AGcwN67bbnxsWU4WK0g6oQeB7KZaEDWvSJdj0oXaTvHoN7Ouoobuh2kcNx6EnmgfvuehDjao+xWcH1CAQvhq6FOs7r/3CsYOfx2YZ6ZqLPCijmT7m4s5rHlt9iqxpgRoErKvBhxvqJOvxWv3CsYOf+2Zh+Yp7NlqepVNDM33MwXitcT5jISI7oAaBGNTQpNlN8npRQ7Vk/KkJG57MMBzTXT8Zz/oo70E+o6lMNbHYATUI2FeDNzc8z+To54ENZ4TdnswwEoGbCSCjjMZRl3qisANqEIhEDd06dQM1bIU7FEVg8NxaaPss+5HfLtUQSV0aEX7HA2oQiEUNxfW579atq+t2Pw9sXy52WS1AcX1dBrbduZ0+5q6vYZDXwKvSK2EXCTUIOFCDr6kN/+dp4dh+HtitGxfZzxd7mgDSTx9zdIfiibCrkkjAwQNqEIhIDY/u9qTp51DU8yVue2P2T/PFnuaG9dPHXIxrkAi2In0gUD2gBgEXavDlhqOxkrCTuWEiYdajb2zC8wNqEIhJDXl2spCyo7lhbwitEk0lLD2gBoGPpajeDEgN18xC0q7mhr0jqDo0i3D0gBoEPpWingS95Oz6uiIuh5+TXnVuWEMoNWgBYegBNQh8KEWzdEpIaqjSDuBSfCGICrQU/YVDDQLvS9EuuLbotHq9FrRfiDLq688vKA8eUIPA21J0y7SGp4ZQa5ny+vMrG723LlCDwLtSCIu7z8LzJaD0EvxOsBmfiko7oAaBN6UQHgkzD+/n33sGlqKy7lhFX/CAGgTkUggPkpuL95PvPQPL0VZzHKBLD6hBwFXUoKBqarr25qKq4rhCT98DahBw1degQA0qsrAcLdXGMSr8gBoEHN2hKPF+wsMOHHJtUbdDfN+8QA0CjsY1lKi4qlVk4hdSsUPu04SoQcDNaMgaFdd0BDUrITt4KitqEHAzh6JGyQUdQ8VKzA5rFxY1CDiZedmi5XKOol5FUYipCJ0PLrsjUIOAk/UarHzWKmoy8gtJyaFkJINN3nwDDgyBGgQSUUMk1SqZWxY9bZGbn40iLCeCGgTcqkGRGzRl5ScS1EPPpg0cqj+sfRGoQSAdNUQSOFSkaoei2HXBN4N/Fr4N1CDgWA2q3BCTHJK0w2bTySDvwwcLLQzUIOBaDbrcEJUcUmta1KXt1VBvHLYwFoMaBBJTQ2RySEkPo97IQfjQtzCWgxoEnKtBnRviC8VVzFByTFvA7kbFWA1EDfZJUA15dKFDSeR66ErX3cN8UcMPXwBqEHCvBpVuiFEOMeth08VGXcdj44hhO2Nx/IQaBFZQg043xCmHCJtLz7QFHKthvMPc7wA1CKSrhmjlEL8dKkb3LV8KPG8FCNQgsIYa1LohWjlE3LToGYygflfWqX5ADQKrqEGvG+KVQxp6KPlWzAl+QA0Cqashajmko4evfP4eUIPAOmpQ7Ya45ZDGsIcfQQ0CK6lBtxtil0OOHz6DGgTWUoNyN6TRq48f3oAaBFBDRyK1Bj28ghoEVlNDAG5IRQ6JxEgzQA0C66khBDckVGfSKekEUIPAimoIwg0p1Rm6HlpQg8CaagjEDQk1LPK5I4ojBTUILFfD8d1Dtjs+H+rDfv5JrbYk7gfUILBYDcd3VfpTld9M3E8D6VWVdP2AGgSWquHwsUq/fW8zYR81JFlRkvQDahCYUArpQsmya1+19+aW5zezHRy1eu9ijsXPq7lW+1zye+GT//c+3qfhujcmO1Zbb9mu+Hnf13/nj+JDh0f3Tv6Xme1pTgl/IsFqUpKaH1CDwEI1HIdV+2GKKrsr/dAdtX7PZOWupq7y+SMrWw//8RjvU3GpWxbVUXfmUPzMmr/z6sU2b985Vjuu54aEhwgl5AfUILBQDfmoav+Zy7moti/vHUpdZKUgblWtrgzyn90+fVfD1pzLfUy5tRbJ7pGfyg/+lX8fSxe079yLOCRbUNJfSKaOvJCGH1CDgBU1FO0Lkz1e37uYv6IeH4sWxaloT2yLWp3fzXbT7jPshbxf/na1Gu55/7OURrXvvt2WmcNlXgktkUAFeUv0NzhRg4AdNZyfYvz2vSJOOJpH8d/93rQbi5+bl88XwURjiW6n5mfvj6b/omhgbO+5D+KuHd/ZdPjOiXVQg4A1NZyl9w7mke3zfdb8t9++tXn+/MFsT5f7FDUU7Y6tya5TiuaAGKvFAqLzA2oQsNSg2G4z6b2iRVE441y0Kc5516BoDjn8fPX6Ialha56PmRetE4/ffWSVYjkx+QE1CNjqhiy7FYT3TNlDUNR588jbbshjc8yxGq75Yyepobq/cS4/WW/Lij1vq3dDjomnSvxMJH5ADQI21PAoI4GdeQjvHaogYVtW7ebmZdNduRmNhjyad30N9YfKOx31tnrPoYa8EEeNsET4PRCoQcCGGvbloKZb2Z3w8l496umv7okohzwd2i7EzWigdPHO7iqpofrQ7tof85iZzLsZKgKvDtYJuJMSNQgsV8OvBHkNPRNqXXBJgN8JahDwp4Zw5mh/I7yq4J6wQgjUIOBTDdG4Iadr8h1hKAI1CHhVQxyNihb9NcAjuv2AGgT8qiGqwKFE8/XvH7UhBGoQ8K2G2NxA8DABdYZADQLe1RBXo6JB14WvFT1+QA0C/tUQYeBQoea6183nRkb7juMvEzUIuFDDpVy64bRtVmPqxizl53ZTft0Zs+/nSBWnvV+EdlcNgfIz89o2ev5fVI84r3OT11Fl+8+VI1CDgAM1XMsZDod2NaZ7PeLxWk3PbBZoeph29HPLf3bjJ5u5U94mV9oHbSDotQAAD0tJREFUP8zj2RCb5hIcesKyIlCDwJRSzDwH2akcNr17FC7Ylo6olmnaljMprvmlmhl1KrcdBjMh+kVob82rk98ZVNbBD0spvrZNr4bmRR9GWAE1CNhXw7FeELJdvyEbTIpoXzTrzHaTLqpFaOszvc2aXetFY+MCP8xmM2hLDF80vy2BGgSsq+FRTajamfFKTO1y05dmKdhqW5d2swhtceL/zLmdRjWcyRkT+GEekho2qME91tXwZ8oexKJ+782260u8NOvD7etIwTypofvj/xbvD9aVjBf8MJ1NcwGihnWxroZ9s+Lrrul8LLmXD5AoOW4rN7xVQ/Yf/9Nuvg8neccJfvhCq4RxO2KsBivDp1CDgHU1tNX+WPY21kbozJCXtymO79VwKCKOTbfkbBxf8Dfww3uaexHd7z5qqN8e7fqLIlCDgCM1DHsTbgMztGvBVq+y54+N1p9PRA0V2gYOa2GghU4P41sWr5+Qxkd8AzUIOFLDvq/d57ZlkJlH3q4Fe3taFupJDc/LyqYBfnih/UL638OhDV8/O1USqEHgYymaNxf0NZzrBsWhfORlGzMcy47Fatux/HEYPbqiF0H1arNJoK9BBD98pBs4PWdYw1dBoAaBT6Uw/+p3F9yhaJ45c+8ePtOtAJvd63FN9SrT+WvPQvPqv6K+Q/EFdVMT1WH320ENAh9KYf79q92wYFxDtbzrrhwJPeg+6JeNLedQ1PMq3qrhaP4n+aqhdn2D2EANAu9LUZqhdsPM0ZB2vpjSMFSKGvzgGNQg8LYUtRkqNyyYQ/EzzRwKakQHIYQ7UIPAu1K0ZijdMPNqvNqYGdXNvKQuPIEi7IMaBN6UojdD4Ya5V2G1XsNvDNdroBaIoAd7oAYBuRRDMzR9kT6hEryBCMIOqEHARdTgAq7/TywaAggdqEHAQV+DK7jwJ4AiloAaBBzcoXAIF/1UEMQcUIOA/XENruGSnw4hxDRQg4D10ZCrwOU+C7oivoAaBKbMoVDoBuywDCQhghoEpsy8VKmGnKbFTyxb2CBSUIPApFIovni4sn8HSaAGgdDVkGMHi6QqCdQgEIEacuxgn7QkgRoE4lBDzsRlZ6TQKYEaBKJRQ0XcF7B3NgK+82QF1CAQlxoq4rliQyAKSaAGgQjVUBH81RoeAUsCNQjEqoaaMC/U4JEaHqpPA2oQmFYK1ef1G+ovzDTQLAnUIJCAGiqUXpNJ4iio+OEQqEEgFTVU4Ae9/C6J9pPtAZ5/fwA1CCSlhgq1US3ULIwpNu1zrTbNY+/Gvz+CGgTSU0OD3oYvvPJdE50J8upyff79GdQgkKwaWhBEaIgRRfkQzOb3mwdsfwI1CCxQQ/cMus/Psi7ffbPHSXpShdcnYxNCBM6muUgbUbz8/gxqEPCiBnGzVzU0aL7BBh/omg2owRoTSzH8bmeoYc5bGtTQgyRCoj9Tbavi+fdnUIPAr2ow5r43WfVw7OveNK/uO7PvooZ69+rnX2a2pzxvHpz9KB+c/RjtrxGr997BEe0JQg3W+F0NWVnTCyNcjGlePcpN+1c1HKsdTq0aqg9u8+H+ykESmiFqsMvUUgy+3Cc17B75yWR5vjXnPL/VCtjlj92rGooII7+Wu9YhRGmRY2GKwf7BgCT0MbhDwc1LC/yuhnu36X75qyr4ttx2f1VDZpqn3FabtvX2/XD/AEESuug6HZ5+fwQ1CEwuRf/9Pqmh27SrWxTjOGH416VoOGw7kxjzun/Q0CkRLKhBYHopuuv9jRoOZnu63D+poWhvbE12jVUNA55H5aAL3aAGgRmlaC/unambBReze9ZA/njToOiaC6du07ZLOugGxWTQhV5Qg8CcUjRX8slkpRuK5sFprIZr05n4V3ZNdt2QmTk327Nij1vXDXksuyHPpV76/VMDS+ggCTVc//ZVmL4/XiftP6sUzfXbdCqUtfrl1mT5anzzstr+1+/xV93xbPYy5hbQzUuXSEEF4liJBNTw2Jqe3ZRPzCtFc4meS/3sz9Xn+36CQ5HktW4+7AdDnvJjVuigfZUVZqhvdub36gP5cH94A75wSgJqOJrsfKte3Yt4/zjhEzNLMfNypLq7BF3YIgE1ZObWva5a9V+ZW4pZ1170XYv6+NwuwRwyCahhVBUn1cvZpZhxdf0Zc5h7eHBKGObo8/Lut2USUIP7qGGOGw71ACcIBiXm6BITRjw7yUUCajia7FLXRkd9DSVa/m+BtfliDmsK6RZ8XTQhYgEJqKG7r1hNanxM+MCSUqgJPEEnPyqke2PZNMoFpKCG/HqsxjVk+z8H4xo69DRLIWDe2GLTrQ5d7ySoYap8JkYxSahhLotLYSt4BHhiM2o7WIgavl6pqEHgYymmFLFT838d2ilT5ebrrh3P1G87b6tFnvJmajbAe5Yv2bSAJNRgc6C0mTMs4ZptNv+31kDpifrVf23KxV0aNZybRZ7yPJuWN0gY1GAXuwOlZ6khO5VzMdubIltT1P6r2eaFJP6zCSv+l/n/ir/+ozi14mLzADWdAbobmE+/pc+03RNvfn8mATVYHSjdNQQmpVzW9pM5d59tf/bbujc2m//4z808pmYDIqDrZ2g7HZ7/CR9pfsyQyZAE1GB1yJOZ4YZH5aGDuezMrszCvo4a9sNtJU1ccTRTbqwOmGkSZBM03flpXzz/fv1E8wM1vMPmQGkzRw1/1fIudTdHtVrksVlderit/GNf/b6UU7P9Yl82KvD9tXpiPBICNbxiM2owc9ywb1aPvZQtiEPeGGE/3lYIY1u74d4oAmzjW00eqIrdDIVADe+wOFDazFLDcLd61ZZTKYTjy/vnehtTMsEm3VDqXP79hQTUMGGgtHni3ZG+7zHee/y66YbMXt+Pb41Y8A99Dd+xNVB6ij1Gu5c/s7rab4cC6LdlVd8jagD7oAb7WFJD3ddwqJsRp/Lvc/lqN9x2LPse614H+hrAKqjBPm9KMa3R0VPfobh3j7FsxkDehtvqdWKz0iEK7lBATKAG+1gqRT2uoVz/1RyrLo5b8Wp/G28r14k9VL2ks8c1AHyku1Xx5vdnUlODmwXg3nCcdaBJN08AVgI1SDvZSq2cQzEV5lCAKlJTwySsleI6o7oz8xJUgRoE7JXiMnn1aNZrAF2gBoE4SgHwC0mowekzLwGiJAE1uH7mJUCMJKAG58+8BIiQBNSwxtOrAGIjATWs8MxLgOhIQA1EDQDzSUANqzzzEiAyElDDOs+8BIiLFNSw0jMvAWIiCTXMJY5SAPwCahCIoxQAv4AaBOIoBcAvoAaBOEoB8AuoQSCOUgD8AmoQiKMUAL+AGgTiKAXAL6AGgThKAfALqEEgjlIA/AJqEHh+BCZAgliuVHYPp5HYi0j5wkZr+bTmyyKxF5HyhY3W8mnNl0ViLyLlCxut5dOaL4vEXkTKFzZay6c1XxaJvYiUL2y0lk9rviwSexEpX9hoLZ/WfFkk9iJSvrDRWj6t+bJI7EWkfGGjtXxa82WR2ItI+cJGa/m05ssisReR8oWN1vJpzZdFYi8i5QsbreXTmi+LxF5Eyhc2WsunNV8Wib2IlC9stJZPa74sEnsRKV/YaC2f1nxZJPYiUr6w0Vo+rfkCAK+gBgAQQA0AIIAaAEAANQCAAGoAAAHUAAACqAEABFADAAigBgAQQA0AIIAaAEAANQCAAGoAAAHUAAACqAEABFADAAigBgAQQA0AIIAaAEAANQCAAGoAAAHUAAACqAEABOJTwzEz2fEhb3h5L0A+lM/UeMqYJYRzdDLv3wuOD+XTdf605MMau+rb3YobXt4LkA/lu+m6tJYhnKNbW6Qoz19fPmXnT0s+bHE12S2/ZeYqbHh5L0A+le9m9j6zZgXhHBV/mXfvBcen8ik7f7Gp4Wguxc+z+RM2vLwXIJ/Kdwq6ZDWv5+hkdk3VifL8Dcqn7PzFpoa9uecj/w42vLwXIJ/KdzInfxmzxOs5Mse8qTpRnr9B+ZSdv9jU0HzLfYNtsOHlvQD5VL69OR9MdvSTMUu8nqPb88a4zt+gfMrOX8hfs0TaaqjY+cmZHcRzFLUa8oEaVJ2/kL9miZTVYMw5zx9HXWHpTFJWg7LzF/LXLJGyGmoeQd/dS1kNNWrOX8hfs0T2/NUPNry8FyCfytcQV/kGf0V5/r795Q8l2bBG3QN8f+7Bv/d3KO4R9HDL5WvQcmktQjxHozsUkZ2/EtSwBn/VfeOLOQobXt4LkE/ly0w5/jbsqiOeo6ayRHn+SrqoSNX5i00NKY+GPJYX3KMeVBMq4jmKezRkVz5l5y82NeTb/gZQ/Y0PNmxV3RxaxofyPbLqZcj/qQrlG7yI8vz1L5Sdv+jU8KhmtlUv6298sGHwMli+lW+r5NbXUl7KN3gR5fnLx+XTc/6iUwMA2AA1AIAAagAAAdQAAAKoAQAEUAMACKAGABBADQAggBoAQAA1AIAAagAAAdQAAAKoAQAEUAMACKAGABBADQAggBoAQAA1AIAAagAAAdQAAAKoAQAEUAMACKAGABBADQAggBoAQAA1AIAAagAAAdQAAAKoAQAEUANYonwAvO88gD04mWAH0/2AKOBcgh1QQ2RwLsEiXE7xwLkEi3A5xQPnEuzB1RQRnEyYzml8uZia3bX9e/0cgTM4mzCZmxHVYEztBq6lqOB0wlRu2Ysaql9Hs6v+Wj9H4BDOJ0zkZHayGurfdfywfrbAEZxLmIg55p/UAJHBSYWJ3F4cMG5QQFygBpjOu27Im6f8gENQA0xHVsMOM8QIaoDpSA2KbXbxkxlwC2qA6dRq6O5FVD+uxtz95gqcgBpgOpIa8r3Ze80UuAE1wHTEOxQ3uiGjBDXAdOSbl4QNUYIaYDqyGh6EDTGCGmA6b0ZDHgkbIgQ1gCWqrknzeQuEAycO7FDdssibiVbVn/0WCBBOHNihWzbWtOvOs5Bs0HDmwA6vIkANQcOZAzughsjgzIEd2uGRRtjCGi8BwikDS1QdDCMHtFu4yEKEswbuIWgIEM4ZrAHXWXBwysA5dDWECOcMAARQAwAIoAYAEEANACCAGgBAADUAgABqAAAB1AAAAqgBAARQAwAIoAYAEEANACCAGgBAADUAgABqAAAB1AAAAqgBAARQAwAIoAYAEEANACCAGgBAADUAgABqAAAB1AAAAqgBAARQAwAIoAYAEEANACCAGgBAADUAgABqAAAB1AAAAqgBAARQAwAIoAYAEEANACCAGgBAADUAgABqAACB/x/e1jORKm0djwAAAABJRU5ErkJggg==", + "text/plain": [ + "Plot with title \"paste(\"NPM, |\", rho, \"| = 1/2\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pdf(\"figures/401K-contour-npm-lower-rho2.pdf\", width = 6, height = 6)\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", + "invisible(dev.off())\n", + "\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)" + ] + }, + { + "cell_type": "markdown", + "id": "14dd7abd", + "metadata": {}, + "source": [ + "The nonparametric contours are slightly more conservative, but the message is similar. Under perfect adversarial alignment, income-strength confounding can overturn the finding; the contextual matching scenario cannot. At $|\\rho| = 1/2$, even the lower confidence endpoint for the main scenario remains comfortably positive.\n", + "\n", + "The treatment-side gains for IRA participation and two-earner status come out near zero here, placing both markers on the vertical axis. This follows from what `gain.D` measures in this model: a covariate that barely moves propensity scores away from the middle of $[0,1]$ leaves average precision almost unchanged, however much it predicts the outcome.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 01046c53f4fc50e5770453fbd80814f3bfe71cb8 Mon Sep 17 00:00:00 2001 From: chansen776 Date: Thu, 1 Oct 2026 08:52:15 -0500 Subject: [PATCH 21/32] Update r-ADML-401k.irnb Figure changes --- T-ADML/r-ADML-401k.irnb | 105 +++++++++++++++++++++++++++++----------- 1 file changed, 76 insertions(+), 29 deletions(-) diff --git a/T-ADML/r-ADML-401k.irnb b/T-ADML/r-ADML-401k.irnb index a1600b88..08e3ef54 100644 --- a/T-ADML/r-ADML-401k.irnb +++ b/T-ADML/r-ADML-401k.irnb @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "8a03f2af", "metadata": { "execution": { @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "eb62473e", "metadata": { "execution": { @@ -482,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "b6c06ec7", "metadata": { "execution": { @@ -503,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "6a24c23a", "metadata": { "execution": { @@ -606,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "9b0a3776", "metadata": { "execution": { @@ -665,6 +665,53 @@ "The posited confounding scenario substantially widens the range of plausible effects but leaves most of the adjusted region positive. Only the second quartile has a small excursion below zero." ] }, + { + "cell_type": "code", + "execution_count": 16, + "id": "752bb83c", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p_combined <- ggplot(df2, aes(x = groups, y = estimate)) +\n", + " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), fill = \"blue\", alpha = 0.03, color = NA) +\n", + " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), fill = \"blue\", alpha = 0.12, color = NA) +\n", + " geom_line(color = \"black\") +\n", + " geom_line(aes(y = lwr.ci), color = \"black\", linetype = \"dashed\") +\n", + " geom_line(aes(y = upr.ci), color = \"black\", linetype = \"dashed\") +\n", + " geom_line(aes(y = lwr.bound), color = \"blue\") +\n", + " geom_line(aes(y = upr.bound), color = \"blue\") +\n", + " geom_line(aes(y = lwr.cbound), color = \"blue\", linetype = \"dashed\") +\n", + " geom_line(aes(y = upr.cbound), color = \"blue\", linetype = \"dashed\") +\n", + " theme_bw() +\n", + " theme(plot.title = element_text(hjust = 0.5)) +\n", + " xlab(\"Income Groups by Quartiles\") +\n", + " ylab(\"ATE\") +\n", + " ggtitle(\"Confidence and Sensitivity Bounds\")\n", + "\n", + "ggsave(\"figures/401k-groups-sensitivity-combined.pdf\", p_combined, width = 4.5, height = 4.5)\n", + "p_combined\n" + ] + }, { "cell_type": "markdown", "id": "651c1a8d", @@ -677,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "b4a7321f", "metadata": { "execution": { @@ -711,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "9837c2cf", "metadata": { "execution": { @@ -787,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "e65ef369", "metadata": { "execution": { @@ -859,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "445dba1a", "metadata": { "execution": { @@ -875,9 +922,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "Plot with title \"paste(\"PLM, |\", rho, \"| = 1\")\"" + "Plot with title \"paste(\"Partially Linear Model, |\", rho, \"| = 1\")\"" ] }, "metadata": { @@ -893,15 +940,15 @@ "options(repr.plot.width = 7, repr.plot.height = 7)\n", "\n", "pdf(\"figures/401K-contour-plm-lower.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))\n", + "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1\")))\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"PLM, |\", rho, \"| = 1\")))" + "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1\")))" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "3f14d055", "metadata": { "execution": { @@ -917,9 +964,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "Plot with title \"paste(\"PLM, |\", rho, \"| = 1/2\")\"" + "Plot with title \"paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")\"" ] }, "metadata": { @@ -935,10 +982,10 @@ "rho2.half <- (1/2)^2\n", "\n", "pdf(\"figures/401K-contour-plm-lower-rho2.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))\n", + "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")))\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"PLM, |\", rho, \"| = 1/2\")))" + "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")))" ] }, { @@ -963,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "id": "629fe818", "metadata": { "execution": { @@ -1037,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "2aacd3da", "metadata": { "execution": { @@ -1053,9 +1100,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "Plot with title \"paste(\"NPM, |\", rho, \"| = 1\")\"" + "Plot with title \"paste(\"Nonparametric Model, |\", rho, \"| = 1\")\"" ] }, "metadata": { @@ -1069,15 +1116,15 @@ ], "source": [ "pdf(\"figures/401K-contour-npm-lower.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"NPM, |\", rho, \"| = 1\")), bumps = bumps.npm)" + "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps.npm)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "5f1eab78", "metadata": { "execution": { @@ -1093,9 +1140,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "Plot with title \"paste(\"NPM, |\", rho, \"| = 1/2\")\"" + "Plot with title \"paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")\"" ] }, "metadata": { @@ -1109,10 +1156,10 @@ ], "source": [ "pdf(\"figures/401K-contour-npm-lower-rho2.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", + "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"NPM, |\", rho, \"| = 1/2\")), bumps = bumps.npm)" + "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps.npm)" ] }, { From f3beebe04ac90dd80191b1bb7363c1180bc7b1e6 Mon Sep 17 00:00:00 2001 From: chansen776 Date: Thu, 1 Oct 2026 15:16:53 -0500 Subject: [PATCH 22/32] T-ADML notebooks Update to 401k r notebook fixing a labeling issue Creation of first pass at colab ready gasoline example notebook. --- .gitignore | 1 + ..._gas.ipynb => python_ADML_gas_local.ipynb} | 0 T-ADML/python-ADML-gas.ipynb | 273 ++ T-ADML/r-ADML-401k.irnb | 485 +-- data/data_BHP2_with_region.csv | 3641 +++++++++++++++++ 5 files changed, 4007 insertions(+), 393 deletions(-) rename T-ADML/gasoline_example/{python_ADML_gas.ipynb => python_ADML_gas_local.ipynb} (100%) create mode 100644 T-ADML/python-ADML-gas.ipynb create mode 100644 data/data_BHP2_with_region.csv diff --git a/.gitignore b/.gitignore index 83e4b313..d99b1e5b 100644 --- a/.gitignore +++ b/.gitignore @@ -136,3 +136,4 @@ dmypy.json # Notebook updates /TEMP_updated_notebooks /tools +.vscode/settings.json diff --git a/T-ADML/gasoline_example/python_ADML_gas.ipynb b/T-ADML/gasoline_example/python_ADML_gas_local.ipynb similarity index 100% rename from T-ADML/gasoline_example/python_ADML_gas.ipynb rename to T-ADML/gasoline_example/python_ADML_gas_local.ipynb diff --git a/T-ADML/python-ADML-gas.ipynb b/T-ADML/python-ADML-gas.ipynb new file mode 100644 index 00000000..3f5a1b0e --- /dev/null +++ b/T-ADML/python-ADML-gas.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c001", + "metadata": {}, + "source": "# Empirical Example for Sensitivity Analysis: Gasoline Demand\n\nThis is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n*Long Story Short: Omitted Variable Bias in Causal Machine Learning* \n\nThe original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c002", + "metadata": {}, + "outputs": [], + "source": "!pip install econml" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c003", + "metadata": {}, + "outputs": [], + "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy\nimport scipy.special\nimport scipy.stats\nimport joblib\nfrom statsmodels.api import OLS\nfrom sklearn.model_selection import StratifiedGroupKFold, KFold\nimport pandas as pd\nimport math\nfrom sklearn.base import clone, BaseEstimator, TransformerMixin\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import StandardScaler, PolynomialFeatures\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import LassoCV, LogisticRegressionCV, LinearRegression, Lasso, LogisticRegression\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\nfrom scipy.optimize import fmin_l_bfgs_b\nfrom econml.dml import LinearDML\nfrom econml.sklearn_extensions.model_selection import GridSearchCVList\nfrom econml.utilities import cross_product\n# BaseGRF is an internal econml API; may need updating across econml versions\nfrom econml.grf._base_grf import BaseGRF" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c004", + "metadata": {}, + "outputs": [], + "source": "import warnings\nfrom sklearn.exceptions import ConvergenceWarning\n\nwarnings.filterwarnings('ignore', category=ConvergenceWarning)" + }, + { + "cell_type": "markdown", + "id": "c005", + "metadata": {}, + "source": "## Module Definitions\n\nThe following cells define helper classes and functions used by this notebook.\nThey are inlined here so the notebook runs in Google Colab without requiring local files." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c006", + "metadata": {}, + "outputs": [], + "source": "def mean_ci(data, confidence=0.95):\n a = 1.0 * np.array(data).flatten()\n n = len(a)\n m, se = np.mean(a), scipy.stats.sem(a)\n h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n return m, se, m-h, m+h" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c007", + "metadata": {}, + "outputs": [], + "source": "class Featurizer(TransformerMixin, BaseEstimator):\n ''' Creates features of the form X[:, 1:] * poly(X[:, 0])\n i.e. some polynomial of the treatment, multiplied by some other feature.\n '''\n\n def __init__(self, *, degree=1, normalize=True):\n self.degree = degree\n self.normalize = normalize\n\n def _transform(self, X):\n Tfeats = PolynomialFeatures(degree=self.degree, include_bias=True).fit_transform(X[:, [0]])\n feats = cross_product(Tfeats, X[:, 1:])\n return feats\n\n def fit(self, X, y=None):\n if self.normalize:\n self.scaler_ = StandardScaler().fit(self._transform(X))\n return self\n\n def transform(self, X):\n if self.normalize:\n return self.scaler_.transform(self._transform(X))\n return self._transform(X)\n\n\nclass SieveRR(BaseEstimator):\n ''' Linear sieve representation learning with some feature map and l2 penalty. '''\n\n def __init__(self, *, featurizer, moment_fn, alpha=0):\n self.alpha = alpha\n self.featurizer = featurizer\n self.moment_fn = moment_fn\n\n def fit(self, X):\n self.featurizer_ = clone(self.featurizer).fit(X)\n n_samples = X.shape[0]\n Xfeats = self.featurizer_.transform(X)\n Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats])\n n_feats = Xfeats.shape[1]\n Mfeats = np.zeros((n_samples, n_feats))\n for t in np.arange(n_feats):\n if t == 0:\n feat_fn = lambda x: np.ones(x.shape[0])\n else:\n feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n Mfeats[:, t] = self.moment_fn(X, feat_fn)\n Sigma = Xfeats.T @ Xfeats / n_samples\n reg = self.alpha * np.eye(n_feats)\n reg[0, 0] = 0\n Sigma += reg\n params = np.linalg.pinv(Sigma) @ np.mean(Mfeats, axis=0)\n self.coef_ = params[1:]\n self.intercept_ = params[0]\n return self\n\n def predict(self, X):\n return self.featurizer_.transform(X) @ self.coef_ + self.intercept_\n\n\nclass SparseSieveRR(BaseEstimator):\n ''' Linear sieve representation learning with l1+l2 (elastic net) penalty. '''\n\n def __init__(self, *, featurizer, moment_fn, alpha=0.0, l1_ratio=1.0, tol=1e-4):\n self.featurizer = featurizer\n self.moment_fn = moment_fn\n self.alpha = alpha\n self.l1_ratio = l1_ratio\n self.tol = tol\n\n def fit(self, X):\n self.featurizer_ = clone(self.featurizer).fit(X)\n n_samples = X.shape[0]\n Xfeats = self.featurizer_.transform(X)\n Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats])\n n_feats = Xfeats.shape[1]\n Mfeats = np.zeros((n_samples, n_feats))\n for t in np.arange(n_feats):\n if t == 0:\n feat_fn = lambda x: np.ones(x.shape[0])\n else:\n feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n Mfeats[:, t] = self.moment_fn(X, feat_fn)\n Sigma = Xfeats.T @ Xfeats / n_samples\n mfeats = np.mean(Mfeats, axis=0)\n\n alpha_l1 = self.alpha * self.l1_ratio\n alpha_l2 = self.alpha * (1 - self.l1_ratio)\n\n def loss_and_jac(extended_coef):\n coef = extended_coef[:n_feats] - extended_coef[n_feats:]\n loss = - 2 * mfeats @ coef + coef.T @ Sigma @ coef\n loss += alpha_l1 * np.sum(extended_coef[1:n_feats])\n loss += alpha_l1 * np.sum(extended_coef[n_feats + 1:])\n loss += 0.5 * alpha_l2 * np.sum(extended_coef[1:n_feats]**2)\n loss += 0.5 * alpha_l2 * np.sum(extended_coef[n_feats + 1:]**2)\n grad = 2 * (Sigma @ coef - mfeats)\n jac = np.concatenate([grad, -grad])\n jac[1:n_feats] += alpha_l1 + alpha_l2 * extended_coef[1:n_feats]\n jac[n_feats + 1:] += alpha_l1 + alpha_l2 * extended_coef[n_feats + 1:]\n return loss, jac\n\n w, _, _ = fmin_l_bfgs_b(loss_and_jac, np.zeros(2 * n_feats),\n bounds=[(0, None)] * 2 * n_feats,\n pgtol=self.tol)\n params = w[:n_feats] - w[n_feats:]\n self.coef_ = params[1:]\n self.intercept_ = params[0]\n return self\n\n def predict(self, X):\n return self.featurizer_.transform(X) @ self.coef_ + self.intercept_" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c008", + "metadata": {}, + "outputs": [], + "source": "def poly_feature_fns(degree):\n def poly(d, sign=1.0):\n return lambda x: sign * x[:, [0]]**d\n return [poly(t) for t in np.arange(0, degree + 1)]\n\n\nclass RFrr(BaseGRF):\n \"\"\"\n A forest that estimates a Riesz representer of a linear functional.\n Minimizes the Riesz loss E[(phi(T,X)' alpha(x))^2 - 2 * | X=x].\n\n Parameters\n ----------\n riesz_feature_fns : list of lambda\n Features phi(T,X) used to approximate the Riesz representer locally.\n moment_fn : lambda (x, fn) -> float\n The linear moment function m([T;X], g).\n l2 : float, default=0.01\n L2 penalty on the local covariance matrix (no penalty on intercept).\n \"\"\"\n\n def __init__(self, *, riesz_feature_fns, moment_fn, l2=0.01,\n n_estimators=100, criterion='mse', max_depth=None,\n min_samples_split=10, min_samples_leaf=5,\n min_weight_fraction_leaf=0., min_var_fraction_leaf=None,\n min_var_leaf_on_val=False, max_features='auto',\n min_impurity_decrease=0., max_samples=.45,\n min_balancedness_tol=.45, honest=True, inference=True,\n fit_intercept=True, subforest_size=4, n_jobs=-1,\n random_state=None, verbose=0, warm_start=False):\n self.riesz_feature_fns = riesz_feature_fns\n self.moment_fn = moment_fn\n self.l2 = l2\n super().__init__(n_estimators=n_estimators, criterion=criterion,\n max_depth=max_depth, min_samples_split=min_samples_split,\n min_samples_leaf=min_samples_leaf,\n min_weight_fraction_leaf=min_weight_fraction_leaf,\n min_var_fraction_leaf=min_var_fraction_leaf,\n min_var_leaf_on_val=min_var_leaf_on_val,\n max_features=max_features,\n min_impurity_decrease=min_impurity_decrease,\n max_samples=max_samples,\n min_balancedness_tol=min_balancedness_tol,\n honest=honest, inference=inference,\n fit_intercept=False, subforest_size=subforest_size,\n n_jobs=n_jobs, random_state=random_state,\n verbose=verbose, warm_start=warm_start)\n\n def _get_alpha_and_pointJ(self, X, T, y):\n n_riesz_feats = len(self.riesz_feature_fns)\n TX = np.hstack([T, X])\n riesz_feats = np.hstack([feat_fn(TX) for feat_fn in self.riesz_feature_fns])\n mfeats = np.hstack([self.moment_fn(TX, feat_fn) for feat_fn in self.riesz_feature_fns])\n alpha = np.zeros((X.shape[0], n_riesz_feats))\n alpha[:, :n_riesz_feats] = mfeats\n riesz_cov_matrix = cross_product(riesz_feats, riesz_feats).reshape(\n (X.shape[0], n_riesz_feats, n_riesz_feats))\n penalty = self.l2 * np.eye(n_riesz_feats)\n penalty[0, 0] = 0\n pointJ = riesz_cov_matrix + penalty\n return alpha, pointJ.reshape((X.shape[0], -1))\n\n def _get_n_outputs_decomposition(self, X, T, y):\n n_relevant_outputs = len(self.riesz_feature_fns)\n return n_relevant_outputs, n_relevant_outputs\n\n def _translate(self, point, TX):\n riesz_feats = np.hstack([feat_fn(TX) for feat_fn in self.riesz_feature_fns])\n return np.sum(point[:, :riesz_feats.shape[1]] * riesz_feats, axis=1)\n\n def fit(self, X):\n return super().fit(X[:, 1:], X[:, [0]], X[:, [0]])\n\n def predict(self, X_test):\n point = super().predict(X_test[:, 1:])\n return self._translate(point, X_test)\n\n\nclass RFreg(BaseGRF):\n \"\"\"\n A forest that estimates a regression function with pre-specified features.\n Minimizes E[( - Y)^2 | X=x].\n\n Parameters\n ----------\n reg_feature_fns : list of lambda\n Features phi(T,X) used to approximate the regression locally.\n l2 : float, default=0.01\n L2 penalty on the local covariance matrix (no penalty on intercept).\n \"\"\"\n\n def __init__(self, *, reg_feature_fns, l2=0.01,\n n_estimators=100, criterion='mse', max_depth=None,\n min_samples_split=10, min_samples_leaf=5,\n min_weight_fraction_leaf=0., min_var_fraction_leaf=None,\n min_var_leaf_on_val=False, max_features='auto',\n min_impurity_decrease=0., max_samples=.45,\n min_balancedness_tol=.45, honest=True, inference=True,\n fit_intercept=True, subforest_size=4, n_jobs=-1,\n random_state=None, verbose=0, warm_start=False):\n self.reg_feature_fns = reg_feature_fns\n self.l2 = l2\n super().__init__(n_estimators=n_estimators, criterion=criterion,\n max_depth=max_depth, min_samples_split=min_samples_split,\n min_samples_leaf=min_samples_leaf,\n min_weight_fraction_leaf=min_weight_fraction_leaf,\n min_var_fraction_leaf=min_var_fraction_leaf,\n min_var_leaf_on_val=min_var_leaf_on_val,\n max_features=max_features,\n min_impurity_decrease=min_impurity_decrease,\n max_samples=max_samples,\n min_balancedness_tol=min_balancedness_tol,\n honest=honest, inference=inference,\n fit_intercept=False, subforest_size=subforest_size,\n n_jobs=n_jobs, random_state=random_state,\n verbose=verbose, warm_start=warm_start)\n\n def fit(self, X, y):\n return super().fit(X[:, 1:], X[:, [0]], y)\n\n def _get_alpha_and_pointJ(self, X, T, y):\n n_reg_feats = len(self.reg_feature_fns)\n TX = np.hstack([T, X])\n reg_feats = np.hstack([feat_fn(TX) for feat_fn in self.reg_feature_fns])\n alpha = y.reshape(-1, 1) * reg_feats\n reg_cov_matrix = cross_product(reg_feats, reg_feats).reshape(\n (X.shape[0], n_reg_feats, n_reg_feats))\n penalty = self.l2 * np.eye(n_reg_feats)\n penalty[0, 0] = 0\n pointJ = reg_cov_matrix + penalty\n return alpha, pointJ.reshape((X.shape[0], -1))\n\n def _get_n_outputs_decomposition(self, X, T, y):\n n_relevant_outputs = len(self.reg_feature_fns)\n return n_relevant_outputs, n_relevant_outputs\n\n def _translate(self, point, TX):\n reg_feats = np.hstack([feat_fn(TX) for feat_fn in self.reg_feature_fns])\n return np.sum(point * reg_feats, axis=1)\n\n def predict(self, X_test):\n point = super().predict(X_test[:, 1:])\n return self._translate(point, X_test)" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c009", + "metadata": {}, + "outputs": [], + "source": "class DebiasedMoment:\n\n def __init__(self, *, moment_fn, get_reisz_fn, get_reg_fn, n_splits=5):\n self.moment_fn = moment_fn\n self.get_reisz_fn = get_reisz_fn\n self.get_reg_fn = get_reg_fn\n self.n_splits = n_splits\n\n def fit(self, X, y):\n y = y.flatten()\n if self.n_splits == 1:\n splits = [(np.arange(X.shape[0]), np.arange(X.shape[0]))]\n elif isinstance(self.n_splits, int):\n splits = list(KFold(n_splits=self.n_splits).split(X))\n else:\n splits = self.n_splits\n\n reisz_pred = np.zeros(X.shape[0])\n reg_pred = np.zeros(X.shape[0])\n moment_pred = np.zeros(X.shape[0])\n moment_reisz = np.zeros(X.shape[0])\n self.reisz_models_ = []\n self.reg_models_ = []\n reisz_fn = self.get_reisz_fn(X)\n reg_fn = self.get_reg_fn(X, y)\n for train, test in splits:\n reisz = reisz_fn().fit(X[train])\n reisz_pred[test] = reisz.predict(X[test])\n reg = reg_fn().fit(X[train], y[train])\n reg_pred[test] = reg.predict(X[test])\n moment_pred[test] = self.moment_fn(X[test], reg.predict)\n moment_reisz[test] = self.moment_fn(X[test], reisz.predict)\n self.reisz_models_.append(reisz)\n self.reg_models_.append(reg)\n\n self.reg_pred_ = reg_pred\n self.reisz_pred_ = reisz_pred\n self.moment_ = moment_pred + reisz_pred * (y - reg_pred)\n self.moment_reisz_ = moment_reisz\n self.moment_pred_ = moment_pred\n self.y_ = y.copy()\n\n epsilon = LinearRegression(fit_intercept=False).fit(\n reisz_pred.reshape(-1, 1), y - reg_pred).coef_[0]\n moment_pred = moment_pred + moment_reisz * epsilon\n reg_pred = reg_pred + epsilon * reisz_pred\n self.tmle_moment_ = moment_pred + reisz_pred * (y - reg_pred)\n return self\n\n def avg_moment(self, alpha=0.05, tmle=False):\n if tmle:\n return mean_ci(self.tmle_moment_, confidence=1 - alpha)\n return mean_ci(self.moment_, confidence=1 - alpha)" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c010", + "metadata": {}, + "outputs": [], + "source": "def first_stage_reg(numeric_columns=[], cv=None):\n return GridSearchCVList([\n Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n remainder='passthrough')),\n ('lasso', Lasso())]),\n Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n remainder='passthrough')),\n ('feat', Featurizer(degree=1, normalize=False)),\n ('lasso', Lasso())]),\n RandomForestRegressor(n_estimators=100, random_state=123),\n RFreg(reg_feature_fns=poly_feature_fns(10),\n min_var_leaf_on_val=True, min_impurity_decrease=1e-4,\n max_samples=.85, inference=False, subforest_size=1, random_state=123)\n ],\n param_grid_list=[\n {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n {'max_depth': [3, 5], 'min_samples_leaf': [10, 50]},\n {'reg_feature_fns': [poly_feature_fns(2)], 'min_samples_leaf': [10],\n 'min_var_fraction_leaf': [0.01, 0.1], 'l2': [1e-3, 1e-2]}\n ],\n cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n\n\ndef reg_fn_gen(numeric_columns, cv=None, verbose=0):\n def get_reg_fn(X, y):\n est = first_stage_reg(numeric_columns, cv=cv).fit(X, y)\n if verbose > 0:\n print('Chose: ', est.best_score_, est.best_estimator_)\n return lambda: clone(est.best_estimator_)\n return get_reg_fn\n\n\ndef neg_reisz_score_gen(moment_fn):\n def neg_reisz_score(model, X):\n return np.mean(2 * moment_fn(X, model.predict) - model.predict(X)**2)\n return neg_reisz_score\n\n\ndef first_stage_rr(moment_fn, numeric_columns, cv=None):\n return GridSearchCVList([\n SieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn),\n SieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn),\n SparseSieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn, tol=1e-4),\n SparseSieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn, tol=1e-4),\n RFrr(riesz_feature_fns=poly_feature_fns(10), moment_fn=moment_fn,\n min_var_leaf_on_val=True, min_impurity_decrease=0.001,\n max_samples=.65, inference=False, subforest_size=1, random_state=123)\n ],\n param_grid_list=[\n {'alpha': np.geomspace(1e-6, 1e3, 20)},\n {'alpha': np.geomspace(1e-6, 1e3, 20)},\n {'alpha': np.geomspace(1e-6, 1e3, 20), 'l1_ratio': [.5, 1.0]},\n {'alpha': np.geomspace(1e-6, 1e3, 20), 'l1_ratio': [.5, 1.0]},\n {'riesz_feature_fns': [poly_feature_fns(2), poly_feature_fns(3)],\n 'min_samples_leaf': [10, 50], 'min_var_fraction_leaf': [0.01, 0.1],\n 'l2': [1e-5, 1e-3], 'max_depth': [5, None]}\n ],\n scoring=neg_reisz_score_gen(moment_fn), cv=cv, verbose=0, n_jobs=-1)\n\n\ndef rr_fn_gen(moment_fn, numeric_columns, cv=None, verbose=0):\n def get_rr_fn(X):\n est = first_stage_rr(moment_fn, numeric_columns, cv=cv).fit(X)\n if verbose > 0:\n print('Chose: ', est.best_score_, est.best_estimator_)\n return lambda: clone(est.best_estimator_)\n return get_rr_fn\n\n\nclass RegRandomForestClassifier(BaseEstimator):\n def __init__(self, *, max_depth=None, min_samples_leaf=1):\n self.max_depth = max_depth\n self.min_samples_leaf = min_samples_leaf\n\n def fit(self, X, y):\n self.model_ = RandomForestClassifier(n_estimators=100, random_state=123,\n max_depth=self.max_depth,\n min_samples_leaf=self.min_samples_leaf)\n self.model_.fit(X, y)\n return self\n\n def predict(self, X):\n return self.model_.predict_proba(X)[:, 1]\n\n\nclass RegLogisticRegression(BaseEstimator):\n def __init__(self, *, C=1):\n self.C = C\n\n def fit(self, X, y):\n self.model_ = LogisticRegression(C=self.C, max_iter=1000, random_state=123)\n self.model_.fit(X, y)\n return self\n\n def predict(self, X):\n return self.model_.predict_proba(X)[:, 1]" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c011", + "metadata": {}, + "outputs": [], + "source": "def moment_estimate(est, inds=None):\n if inds is None:\n inds = np.arange(est.moment_reisz_.shape[0])\n theta = np.mean(est.moment_[inds])\n psi_theta = est.moment_[inds] - theta\n return theta, np.sqrt(np.mean(psi_theta**2) / psi_theta.shape[0])\n\n\ndef sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None):\n if inds is None:\n inds = np.arange(est.moment_reisz_.shape[0])\n nusq = np.mean(2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2)\n sigmasq = np.mean((est.y_[inds] - est.reg_pred_[inds])**2)\n S = np.sqrt(sigmasq * nusq)\n Casq = eta_asq / (1 - eta_asq)\n Cgsq = eta_ysq\n theta = np.mean(est.moment_[inds])\n error = S * np.sqrt(Casq * Cgsq)\n if alpha is None:\n return theta - error, theta + error\n psi_theta = est.moment_[inds] - theta\n psi_sigmasq = (est.y_[inds] - est.reg_pred_[inds])**2 - sigmasq\n psi_nusq = 2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2 - nusq\n phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0])\n phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0])\n return theta - error, stderr_minus, theta + error, stderr_plus\n\n\ndef sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n if alpha is None:\n lower, upper = zip(*[sensitivity_bounds_single(est, eta_ysq, eta_asq,\n alpha=alpha, inds=inds) for est in est_list])\n return np.mean(lower), np.mean(upper)\n else:\n lower, std_lower, upper, std_upper = zip(\n *[sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds)\n for est in est_list])\n std_lower = np.array(std_lower)\n std_upper = np.array(std_upper)\n lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower))\n upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper))\n return lower, upper\n\n\ndef tvalue(est_list, value=0, leq=None, alpha=None, inds=None):\n for t in np.linspace(0, 1, 1000):\n l, u = sensitivity_bounds(est_list, t, t, alpha=alpha, inds=inds)\n if (leq is None) & (value >= l) & (value <= u):\n break\n if (leq == True) & (value >= l):\n break\n if (leq == False) & (value <= u):\n break\n return t\n\n\ndef sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None):\n xlist = np.linspace(0, a_upper, 100)\n ylist = np.linspace(0, y_upper, 100)\n X, Y = np.meshgrid(xlist, ylist)\n Zlower = np.zeros(X.shape)\n Zupper = np.zeros(X.shape)\n for itx in np.arange(X.shape[1]):\n for ity in np.arange(X.shape[0]):\n l, u = sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds)\n Zlower[ity, itx] = l\n Zupper[ity, itx] = u\n return X, Y, Zlower, Zupper\n\n\ndef dml_estimate(est, inds=None):\n if inds is None:\n inds = np.arange(est.residuals_[0].shape[0])\n yres, Tres, _, _ = est.residuals_\n yres, Tres = yres[inds], Tres[inds]\n nusq = np.mean(Tres ** 2)\n theta = np.mean(yres * Tres) / nusq\n psi_theta = (yres - Tres * theta) * Tres / nusq\n return theta, np.sqrt(np.mean(psi_theta**2) / psi_theta.shape[0])\n\n\ndef dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None):\n if inds is None:\n inds = np.arange(est.residuals_[0].shape[0])\n yres, Tres, _, _ = est.residuals_\n yres, Tres = yres[inds], Tres[inds]\n nusq = np.mean(Tres ** 2)\n theta = np.mean(yres * Tres) / nusq\n sigmasq = np.mean((yres - Tres * theta)**2)\n S = np.sqrt(sigmasq / nusq)\n Casq = eta_asq / (1 - eta_asq)\n Cgsq = eta_ysq\n error = S * np.sqrt(Casq * Cgsq)\n if alpha is None:\n return theta - error, theta + error\n psi_theta = (yres - Tres * theta) * Tres / nusq\n psi_sigmasq = (yres - Tres * theta)**2 - sigmasq\n psi_nusq = Tres**2 - nusq\n phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (\n -(sigmasq / (nusq**2)) * psi_nusq + (1 / nusq) * psi_sigmasq)\n stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0])\n phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (\n -(sigmasq / (nusq**2)) * psi_nusq + (1 / nusq) * psi_sigmasq)\n stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0])\n return theta - error, stderr_minus, theta + error, stderr_plus\n\n\ndef dml_sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n if alpha is None:\n lower, upper = zip(*[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq,\n alpha=alpha, inds=inds)\n for est in est_list])\n return np.mean(lower), np.mean(upper)\n else:\n lower, std_lower, upper, std_upper = zip(\n *[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds)\n for est in est_list])\n std_lower = np.array(std_lower)\n std_upper = np.array(std_upper)\n lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower))\n upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper))\n return lower, upper\n\n\ndef dml_tvalue(est, value=0, leq=None, alpha=None, inds=None):\n for t in np.linspace(0, 1, 1000):\n l, u = dml_sensitivity_bounds(est, t, t, alpha=alpha, inds=inds)\n if (leq is None) & (value >= l) & (value <= u):\n break\n if (leq == True) & (value >= l):\n break\n if (leq == False) & (value <= u):\n break\n return t\n\n\ndef dml_sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None):\n xlist = np.linspace(0, a_upper, 100)\n ylist = np.linspace(0, y_upper, 100)\n X, Y = np.meshgrid(xlist, ylist)\n Zlower = np.zeros(X.shape)\n Zupper = np.zeros(X.shape)\n for itx in np.arange(X.shape[1]):\n for ity in np.arange(X.shape[0]):\n l, u = dml_sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds)\n Zlower[ity, itx] = l\n Zupper[ity, itx] = u\n return X, Y, Zlower, Zupper" + }, + { + "cell_type": "markdown", + "id": "c012", + "metadata": {}, + "source": "## Setting\n\nThe data come from the 2001 National Household Travel Survey and contain household gasoline consumption, gasoline prices, income brackets, and 24 observed controls describing population density, urbanization, demographics, and Census regions. After the sample restrictions used in the earlier demand studies, the analysis contains 3,466 households.\n\nThe outcome $Y$ is log gasoline consumption, the treatment $D$ is log gasoline price, and $X^o$ collects the observed controls. Under conditional ignorability, the short regression is\n\n$$g_s(d, x^o) = \\mathrm{E}[Y \\mid D = d, X^o = x^o],$$\n\nand the target is the average causal derivative\n\n$$\\theta_s = \\mathrm{E}\\big\\{\\partial_d\\, g_s(D, X^o)\\big\\}.$$\n\nBecause both variables are in logarithms, $\\theta_s$ is the average price elasticity of gasoline demand. Its short Riesz representer is the negative conditional density score,\n\n$$\\alpha_s(D, X^o) = -\\partial_d \\log f_{D \\mid X^o}(D \\mid X^o).$$\n\nThe moment function below encodes $m(W_s, g) = \\partial_d\\, g(D, X^o)$. The derivative with respect to log price is approximated by a central finite difference, $\\{g(D + \\epsilon, X^o) - g(D - \\epsilon, X^o)\\}/(2\\epsilon)$ with $\\epsilon = 0.01$." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c013", + "metadata": {}, + "outputs": [], + "source": "# The moment function, whose average we want to estimate: E[m(X; g)]\ndef moment_fn(x, test_fn):\n epsilon = 0.01\n t1 = np.hstack([x[:, [0]] + epsilon, x[:, 1:]])\n t0 = np.hstack([x[:, [0]] - epsilon, x[:, 1:]])\n return (test_fn(t1) - test_fn(t0)) / (2*epsilon)" + }, + { + "cell_type": "markdown", + "id": "c014", + "metadata": {}, + "source": "## Data\n\nFollowing the earlier demand studies, households with very low prices or incomes are dropped. Besides the full sample (`q=5`), the analysis uses four income groups. The first three (`q=1,2,3`) are overlapping groups centered near annual incomes of \\$42,500, \\$57,500 and \\$72,500. The fourth (`q=4`) contains high-income households with income above roughly \\$110,000 ($\\log y \\geq 11.6$).\n\nCross-validation and cross-fitting are grouped by state, so households from the same state remain in the same fold. The folds are stratified by Census region, and region dummies are included as controls." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c015", + "metadata": {}, + "outputs": [], + "source": "# Data loading and manipulation\n\n# for semi-synthetic data generation\ndef true_f(X):\n y = (-.7 - .4 * scipy.special.expit(3 * (1 - 2*(X[:, 1] - 10.5)))) * X[:, 0]\n y += scipy.special.expit(3 * (1 - 2 * (X[:, 1] > 11.5)))\n return y\n\n\ndef get_data(q=None, synthetic=False, region=True, state=False, region_ids=None, random_seed=123):\n data_url = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/update-notebooks/data/data_BHP2_with_region.csv\"\n df = pd.read_csv(data_url)\n # Throw away low prices and low income due to sample quality as in Chetverikov\n df = df[df['log_p'] > math.log(1.2)]\n df = df[df['log_y'] > math.log(15000)]\n if not ((q == 4) | (q == 5)): # all data in first three brackets\n df = df[df['log_y'] >= math.log(42500) - .5]\n df = df[df['log_y'] <= math.log(72500) + .5]\n if q == 1:\n df = df[df['log_y'] >= math.log(42500) - .5]\n df = df[df['log_y'] <= math.log(42500) + .5]\n if q == 2:\n df = df[df['log_y'] >= math.log(57500) - .5]\n df = df[df['log_y'] <= math.log(57500) + .5]\n if q == 3:\n df = df[df['log_y'] >= math.log(72500) - .5]\n df = df[df['log_y'] <= math.log(72500) + .5]\n if q == 4:\n df = df[df['log_y'] >= 11.6]\n if region_ids is not None:\n df = df[np.any([df['region'] == r for r in region_ids], axis=0)]\n Xdf = df.iloc[:, 1:] # Covariates\n X_nostatedum = Xdf.drop(['distance_oil1000', 'share'], axis=1).values\n if state:\n state_dum = pd.get_dummies(Xdf['state_fips'], prefix='state', drop_first=True)\n Xdf = pd.concat([Xdf, state_dum], axis=1)\n if region:\n region_dum = pd.get_dummies(Xdf['region'], prefix='region', drop_first=True)\n Xdf = pd.concat([Xdf, region_dum], axis=1)\n Xdf = Xdf.drop(['distance_oil1000', 'state_fips', 'share', 'region'], axis=1)\n column_names = Xdf.columns\n X = Xdf.values\n y = df['log_q'].values\n if synthetic:\n y = true_f(X) + np.random.normal(0, .2, size=X[:, 0].shape)\n print(np.mean(moment_fn(X, true_f)))\n cv = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=random_seed)\n splits = list(cv.split(df, y=df['region'], groups=df['state_fips']))\n return X, y, [0, 1, 6, 7, 8], splits, column_names" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c016", + "metadata": {}, + "outputs": [], + "source": "# global result dictionaries and global parameters\nests = {}\nres = {}\nres_d = {}\nq_grid = np.arange(1, 6)\nsynthetic = False # use synthetic outcome\nverbose = 0\nregion = True # control for region" + }, + { + "cell_type": "markdown", + "id": "c017", + "metadata": {}, + "source": "## Short Estimates Under Conditional Ignorability" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c018", + "metadata": {}, + "outputs": [], + "source": "# General pipeline for all types of estimators\ndef generic_pipeline(estimator, q_grid, name, n_seeds=5):\n ests = {}\n res = {}\n for q in q_grid:\n print(f'q={q}')\n ests[f'q={q}'] = []\n estimate_list = []\n for random_seed in np.arange(123, 123 + n_seeds):\n X, y, num_cols, splits, column_names = get_data(q, region=region, synthetic=synthetic,\n random_seed=random_seed)\n estimates, model = estimator(X, y, num_cols, splits)\n print(estimates)\n estimate_list.append(estimates)\n ests[f'q={q}'].append(model)\n estimate_list = np.array(estimate_list)\n res[f'q={q}'] = {}\n res[f'q={q}'][f'{name}'] = {}\n res[f'q={q}'][f'{name}']['point'] = np.mean(estimate_list[:, 0])\n if estimate_list.shape[1] > 2:\n res[f'q={q}'][f'{name}']['stderr'] = np.sqrt(np.mean(estimate_list[:, 1]**2)\n + np.var(estimate_list[:, 0]))\n res[f'q={q}'] = pd.DataFrame(res[f'q={q}']).transpose()\n return pd.concat(res), ests" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c019", + "metadata": {}, + "outputs": [], + "source": "# Format results as a printable dataframe\ndef transform_res_to_df(res, q_grid, alpha=None):\n res_f = res.reset_index()\n res_d = {}\n for q in q_grid:\n key = 'all <= 11.6' if q == 0 else ('all' if q == 5 else f'q={q}')\n res_d[key] = {}\n for method in np.unique(res_f['level_1'].values):\n p = res.loc[(f'q={q}', method), 'point']\n if alpha is not None:\n s = res.loc[(f'q={q}', method), 'stderr']\n error = scipy.stats.norm.ppf(1 - alpha) * s\n l, u = p - error, p + error\n res_d[key][method] = f\"{p:.2f} ({s:.2f}) [{l:.2f}, {u:.2f}]\"\n else:\n res_d[key][method] = f\"{p:.2f}\"\n return pd.DataFrame(res_d)" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c020", + "metadata": {}, + "outputs": [], + "source": "if synthetic:\n def oracle_estimator(X, y, num_cols, splits):\n return (np.mean(moment_fn(X, true_f)),), None\n res['true'], _ = generic_pipeline(oracle_estimator, q_grid, 'true', n_seeds=1)\n res_d['true'] = transform_res_to_df(res['true'], q_grid)\n display(res_d['true'])" + }, + { + "cell_type": "markdown", + "id": "c021", + "metadata": {}, + "source": "### Nonparametric Model\n\nThe first specification leaves the short regression $g_s(D, X^o)$ unrestricted and targets $\\theta_s = \\mathrm{E}\\{\\partial_d g_s(D, X^o)\\}$ directly. Rather than estimating the conditional density derivative in $\\alpha_s$, which can be unstable, we learn the Riesz representer from the Riesz loss. Model selection is used for both the regression and the representer. Candidate procedures include expanded-feature lasso, forests and local polynomial forests for the regression, and penalized linear Riesz regression or ForestRiesz for the representer. The target and score stay fixed while the approximation class changes." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c022", + "metadata": {}, + "outputs": [], + "source": "# fully non-parametric average derivative estimation\ndef nonlinrr(X, y, num_cols, splits):\n get_reisz_fn = rr_fn_gen(moment_fn, num_cols, cv=splits, verbose=verbose)\n get_reg_fn = reg_fn_gen(num_cols, cv=splits, verbose=verbose)\n est = DebiasedMoment(moment_fn=moment_fn,\n get_reisz_fn=get_reisz_fn,\n get_reg_fn=get_reg_fn,\n n_splits=splits)\n est.fit(X, y)\n return est.avg_moment(alpha=.1), est" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c023", + "metadata": {}, + "outputs": [], + "source": "res['rr'], ests['rr'] = generic_pipeline(nonlinrr, q_grid, 'dr')" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c024", + "metadata": {}, + "outputs": [], + "source": "res_d['rr'] = transform_res_to_df(res['rr'], q_grid, alpha=0.05)\nres_d['rr']" + }, + { + "cell_type": "markdown", + "id": "c025", + "metadata": {}, + "source": "### Partially Linear Model\n\nThe second specification is the partially linear model,\n\n$$Y = \\theta_s D + f_s(X^o) + \\varepsilon, \\qquad \\mathrm{E}[\\varepsilon \\mid D, X^o] = 0,$$\n\nin which the elasticity is constant." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c026", + "metadata": {}, + "outputs": [], + "source": "# partially linear average derivative estimation, with doubleML\ndef dml_estimator(X, y, num_cols, splits):\n num_cols = [t - 1 for t in num_cols[1:]]\n model_y = reg_fn_gen(num_cols, cv=splits, verbose=verbose)(X[:, 1:], y)\n model_t = reg_fn_gen(num_cols, cv=splits, verbose=verbose)(X[:, 1:], X[:, 0])\n est = LinearDML(model_y=model_y(),\n model_t=model_t(),\n linear_first_stages=False,\n cv=splits, random_state=123)\n est.fit(y, X[:, 0], W=X[:, 1:], cache_values=True)\n p = est.intercept__inference().point_estimate\n s = est.intercept__inference().stderr\n l, u = est.intercept__inference().conf_int(alpha=.1)\n return (p, s, l, u), est" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c027", + "metadata": {}, + "outputs": [], + "source": "res['dml'], ests['dml'] = generic_pipeline(dml_estimator, q_grid, 'dml')" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c028", + "metadata": {}, + "outputs": [], + "source": "res_d['dml'] = transform_res_to_df(res['dml'], q_grid, alpha=.05)\nres_d['dml']" + }, + { + "cell_type": "markdown", + "id": "c029", + "metadata": {}, + "source": "## Sensitivity Results for the Full Sample\n\nFor the full sample we report the short elasticity, the sensitivity bounds and their confidence bounds under the scenario $C_Y^2 = 0.03$, $C_D^2 \\approx 0.03$ and $|\\rho| = 1$, together with robustness values." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c030", + "metadata": {}, + "outputs": [], + "source": "table = {}\ntable['point'] = '{:.3f} ({:.3f})'.format(res['rr'].loc[(f'q={5}', 'dr'), 'point'],\n res['rr'].loc[(f'q={5}', 'dr'), 'stderr'])\n\nest_list = ests['rr'][f'q={5}']\ntable['Bounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=None))\ntable['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=.05))\ntable['RV(v=-1.5)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=None))\ntable['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=.05))\ntable['RV(v=0)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=None))\ntable['RV(v=0,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=.05))" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c031", + "metadata": {}, + "outputs": [], + "source": "table2 = {}\ntable2['point'] = '{:.3f} ({:.3f})'.format(res['dml'].loc[(f'q={5}', 'dml'), 'point'],\n res['dml'].loc[(f'q={5}', 'dml'), 'stderr'])\n\nest_list = ests['dml'][f'q={5}']\ntable2['Bounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=None))\ntable2['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=.05))\ntable2['RV(v=-1.5)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=None))\ntable2['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=.05))\ntable2['RV(v=0)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=None))\ntable2['RV(v=0,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=.05))" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c032", + "metadata": {}, + "outputs": [], + "source": "tabledf = pd.DataFrame({'Non-parametric': table, 'Partially linear': table2}).T[\n ['point', 'Bounds', 'CIBounds', 'RV(v=-1.5)', 'RV(v=-1.5,a=.05)', 'RV(v=0)', 'RV(v=0,a=.05)']]\ntabledf" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c033", + "metadata": {}, + "outputs": [], + "source": "qgr = np.arange(1, 5)\n\npoint = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] for q in qgr]\nlower = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] - 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n for q in qgr]\nupper = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] + 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n for q in qgr]\nsens_band = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\nsens_band_unc = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n\nif synthetic:\n true_moment = [res['true'].loc[(f'q={q}', 'true'), 'point'] for q in qgr]\n\nplt.figure(figsize=(5, 5))\nplt.plot(qgr, point)\nplt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\nplt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\nplt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\nif synthetic:\n plt.plot(qgr, true_moment, label='true')\nplt.xticks(qgr)\nplt.title('Non-parametric')\nplt.legend()\nplt.grid(color='0.85', linewidth=0.6)" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c034", + "metadata": {}, + "outputs": [], + "source": "qgr = np.arange(1, 5)\n\npoint = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] for q in qgr]\nlower = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] - 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n for q in qgr]\nupper = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] + 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n for q in qgr]\nsens_band = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\nsens_band_unc = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n\nplt.figure(figsize=(5, 5))\nplt.plot(qgr, point)\nplt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\nplt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\nplt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\nif synthetic:\n plt.plot(qgr, true_moment, label='true')\nplt.xticks(qgr)\nplt.title('Partially linear')\nplt.legend()\nplt.grid(color='0.85', linewidth=0.6)" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/T-ADML/r-ADML-401k.irnb b/T-ADML/r-ADML-401k.irnb index 08e3ef54..f75e6aec 100644 --- a/T-ADML/r-ADML-401k.irnb +++ b/T-ADML/r-ADML-401k.irnb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "170f2037", "metadata": { "execution": { @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "267a6538", "metadata": { "execution": { @@ -57,16 +57,7 @@ "languageId": "r" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dml.sensemakr 0.2.2 \n", - "sensemakr 0.1.6 \n" - ] - } - ], + "outputs": [], "source": [ "suppressMessages({\n", " library(sensemakr) # attached first: we only need add_bound_to_contour()\n", @@ -100,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "641048b9", "metadata": { "execution": { @@ -113,15 +104,7 @@ "languageId": "r" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "households: 9915 covariates: 8 eligible: 37.1% \n" - ] - } - ], + "outputs": [], "source": [ "data(\"pension\")\n", "\n", @@ -154,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "f4dad1cf", "metadata": { "execution": { @@ -167,35 +150,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Debiased Machine Learning\n", - "\n", - " Model: Partially Linear \n", - " Cross-Fitting: 5 folds, 5 reps \n", - " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 26.474%), treatment (ranger, R2 = 11.599%)\n", - " Tuning: dirty \n", - "\n", - "Average Treatment Effect : \n", - "\n", - " Estimate Std. Error t value P(>|t|) \n", - "ate 9114 1323 6.888 0.00000000000567 ***\n", - "---\n", - "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "Note: DML estimates combined using the median method.\n", - "\n", - "Verbal interpretation of DML procedure:\n", - "\n", - "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "set.seed(1)\n", "dml.401k.plm <- dml(y, d, x, model = \"plm\", cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", @@ -220,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "c886bdf1", "metadata": { "execution": { @@ -243,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "e281ccde", "metadata": { "execution": { @@ -256,46 +211,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Debiased Machine Learning\n", - "\n", - " Model: Nonparametric \n", - " Cross-Fitting: 5 folds, 5 reps \n", - " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 25.572%), treatment (ranger, R2 = 11.48%)\n", - " Tuning: dirty \n", - "\n", - "Average Treatment Effect : \n", - "\n", - " Estimate Std. Error t value P(>|t|) \n", - "ate 8198 1196 6.852 0.00000000000726 ***\n", - "---\n", - "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "\n", - "Group Average Treatment Effect: \n", - "\n", - " Estimate Std. Error t value P(>|t|) \n", - "g.ate.q1 4646.2 794.2 5.850 0.00000000492 ***\n", - "g.ate.q2 2729.4 1296.7 2.105 0.035305 * \n", - "g.ate.q3 6854.3 1838.0 3.729 0.000192 ***\n", - "g.ate.q4 18406.6 4061.6 4.532 0.00000584674 ***\n", - "---\n", - "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "\n", - "Note: DML estimates combined using the median method.\n", - "\n", - "Verbal interpretation of DML procedure:\n", - "\n", - "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "set.seed(1)\n", "dml.401k.npm <- dml(y, d, x, groups = g1, model = \"npm\",\n", @@ -317,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "5c41f860", "metadata": { "execution": { @@ -341,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "522bd397", "metadata": { "execution": { @@ -354,23 +270,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 450, - "width": 525 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "p1 <- ggplot(df, aes(x = groups, y = estimate)) +\n", " geom_line() +\n", @@ -386,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "90b555b3", "metadata": { "execution": { @@ -399,17 +299,7 @@ "languageId": "r" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RV_alpha (alpha = 0.05, threshold theta = 0)\n", - " partially linear : 5.6%\n", - " nonparametric : 4.6%\n" - ] - } - ], + "outputs": [], "source": [ "rv.plm <- robustness_value(dml.401k.plm)\n", "rv.npm <- robustness_value(dml.401k.npm)\n", @@ -438,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "8a03f2af", "metadata": { "execution": { @@ -462,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "eb62473e", "metadata": { "execution": { @@ -482,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "b6c06ec7", "metadata": { "execution": { @@ -503,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "6a24c23a", "metadata": { "execution": { @@ -516,52 +406,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 5
ModelShort ATEBias boundATE bounds95% conf. bounds
<chr><chr><chr><chr><chr>
Partially linear9,1144,201[4,918, 13,311][2,725, 15,573]
Nonparametric 8,1984,529[3,644, 12,752][1,618, 14,805]
\n" - ], - "text/latex": [ - "A data.frame: 2 × 5\n", - "\\begin{tabular}{lllll}\n", - " Model & Short ATE & Bias bound & ATE bounds & 95\\% conf. bounds\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t Partially linear & 9,114 & 4,201 & {[}4,918, 13,311{]} & {[}2,725, 15,573{]}\\\\\n", - "\t Nonparametric & 8,198 & 4,529 & {[}3,644, 12,752{]} & {[}1,618, 14,805{]}\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 5\n", - "\n", - "| Model <chr> | Short ATE <chr> | Bias bound <chr> | ATE bounds <chr> | 95% conf. bounds <chr> |\n", - "|---|---|---|---|---|\n", - "| Partially linear | 9,114 | 4,201 | [4,918, 13,311] | [2,725, 15,573] |\n", - "| Nonparametric | 8,198 | 4,529 | [3,644, 12,752] | [1,618, 14,805] |\n", - "\n" - ], - "text/plain": [ - " Model Short ATE Bias bound ATE bounds 95% conf. bounds\n", - "1 Partially linear 9,114 4,201 [4,918, 13,311] [2,725, 15,573] \n", - "2 Nonparametric 8,198 4,529 [3,644, 12,752] [1,618, 14,805] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dollars <- function(v) formatC(round(v), format = \"d\", big.mark = \",\")\n", "\n", @@ -606,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "9b0a3776", "metadata": { "execution": { @@ -619,23 +464,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 450, - "width": 525 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "bds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0)),\n", " c(\"lwr.bound\", \"upr.bound\"))\n", @@ -667,30 +496,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "752bb83c", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 450, - "width": 525 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "p_combined <- ggplot(df2, aes(x = groups, y = estimate)) +\n", " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), fill = \"blue\", alpha = 0.03, color = NA) +\n", @@ -724,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "b4a7321f", "metadata": { "execution": { @@ -758,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "9837c2cf", "metadata": { "execution": { @@ -771,54 +584,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13660.0502-0.3372
pira0.04870.0081-0.1317
twoearn0.03990.0112 0.2589
\n" - ], - "text/latex": [ - "A matrix: 3 × 3 of type dbl\n", - "\\begin{tabular}{r|lll}\n", - " & gain.Y & gain.D & rho\\\\\n", - "\\hline\n", - "\tinc & 0.1366 & 0.0502 & -0.3372\\\\\n", - "\tpira & 0.0487 & 0.0081 & -0.1317\\\\\n", - "\ttwoearn & 0.0399 & 0.0112 & 0.2589\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 3 × 3 of type dbl\n", - "\n", - "| | gain.Y | gain.D | rho |\n", - "|---|---|---|---|\n", - "| inc | 0.1366 | 0.0502 | -0.3372 |\n", - "| pira | 0.0487 | 0.0081 | -0.1317 |\n", - "| twoearn | 0.0399 | 0.0112 | 0.2589 |\n", - "\n" - ], - "text/plain": [ - " gain.Y gain.D rho \n", - "inc 0.1366 0.0502 -0.3372\n", - "pira 0.0487 0.0081 -0.1317\n", - "twoearn 0.0399 0.0112 0.2589" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "out <- summary(bench.plm)$benchmarks\n", "round(out[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" @@ -834,7 +600,41 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, + "id": "c850176c", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "ovb_contour_without_origin_label <-\n", + " getS3method(\"ovb_contour_plot\", \"dml\")\n", + "\n", + "plot_body <- as.list(body(ovb_contour_without_origin_label))\n", + "\n", + "is_origin_label <- vapply(\n", + " plot_body,\n", + " function(expr) {\n", + " is.call(expr) &&\n", + " identical(expr[[1L]], as.name(\"text\")) &&\n", + " \"label.unadjusted\" %in% all.names(expr)\n", + " },\n", + " logical(1)\n", + ")\n", + "\n", + "# Verify that we found exactly the intended annotation.\n", + "stopifnot(sum(is_origin_label) == 1L)\n", + "\n", + "plot_body[is_origin_label] <- list(quote(invisible(NULL)))\n", + "\n", + "body(ovb_contour_without_origin_label) <- as.call(plot_body)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "e65ef369", "metadata": { "execution": { @@ -860,21 +660,28 @@ " inc4 = c(0.015, 0.000))\n", "\n", "contour_panel <- function(fit, bench, rho2, title, bumps = bumps.plm) {\n", - " ovb_contour_plot(fit,\n", - " which.bound = \"lwr\",\n", - " cf.y = NULL,\n", - " rho2 = rho2,\n", - " col.contour = \"blue\",\n", - " label.bump.x = 0.015,\n", - " label.bump.y = 0.003,\n", - " lim.x = 0.16)\n", + " ovb_contour_without_origin_label(\n", + " fit,\n", + " which.bound = \"lwr\",\n", + " level = 0.95,\n", + " cf.y = NULL,\n", + " rho2 = rho2,\n", + " col.contour = \"blue\",\n", + " label.bump.x = 0.015,\n", + " label.bump.y = 0.003,\n", + " lim.x = 0.16\n", + " )\n", + "\n", " title(main = title)\n", "\n", " add_bound <- function(cfd, cfy, label, bump.x, bump.y, ...) {\n", " sensemakr::add_bound_to_contour(\n", " r2dz.x = cfd, r2yz.dx = cfy,\n", " bound_label = label,\n", - " bound_value = confidence_bounds(fit, cf.y = cfy, cf.d = cfd, rho2 = rho2),\n", + " bound_value = confidence_bounds(\n", + " fit, cf.y = cfy, cf.d = cfd, rho2 = rho2,\n", + " level = 0.95, combine.method = \"median\"\n", + " )[\"ate\", \"lwr\"],\n", " label.bump.x = bump.x, label.bump.y = bump.y, ...)\n", " }\n", "\n", @@ -892,6 +699,9 @@ " add_bound(bench[\"inc\", \"gain.D\"] * 0.25, bench[\"inc\", \"gain.Y\"] * 0.25, \"1/4 x Income\",\n", " bumps$inc4[1], bumps$inc4[2])\n", "\n", + " # unadjusted\n", + " add_bound(0, 0, \"Unadjusted\", bump.x = 0.015, bump.y = 0.003, point.pch = NA) \n", + "\n", " invisible(NULL)\n", "}" ] @@ -906,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "445dba1a", "metadata": { "execution": { @@ -919,23 +729,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Plot with title \"paste(\"Partially Linear Model, |\", rho, \"| = 1\")\"" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "options(repr.plot.width = 7, repr.plot.height = 7)\n", "\n", @@ -948,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "3f14d055", "metadata": { "execution": { @@ -961,23 +755,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Plot with title \"paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")\"" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "rho2.half <- (1/2)^2\n", "\n", @@ -1010,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "629fe818", "metadata": { "execution": { @@ -1023,54 +801,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13150.1288-0.218
pira0.02670.0000 0.000
twoearn0.01130.0000 0.000
\n" - ], - "text/latex": [ - "A matrix: 3 × 3 of type dbl\n", - "\\begin{tabular}{r|lll}\n", - " & gain.Y & gain.D & rho\\\\\n", - "\\hline\n", - "\tinc & 0.1315 & 0.1288 & -0.218\\\\\n", - "\tpira & 0.0267 & 0.0000 & 0.000\\\\\n", - "\ttwoearn & 0.0113 & 0.0000 & 0.000\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 3 × 3 of type dbl\n", - "\n", - "| | gain.Y | gain.D | rho |\n", - "|---|---|---|---|\n", - "| inc | 0.1315 | 0.1288 | -0.218 |\n", - "| pira | 0.0267 | 0.0000 | 0.000 |\n", - "| twoearn | 0.0113 | 0.0000 | 0.000 |\n", - "\n" - ], - "text/plain": [ - " gain.Y gain.D rho \n", - "inc 0.1315 0.1288 -0.218\n", - "pira 0.0267 0.0000 0.000\n", - "twoearn 0.0113 0.0000 0.000" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "set.seed(2)\n", "invisible(capture.output(\n", @@ -1084,7 +815,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "2aacd3da", "metadata": { "execution": { @@ -1097,23 +828,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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OCTXEGX43UANBOmrgdkQBN/iKvh+ogcCDGiI6oshXDZG7YWd8qIHA/ZSnmNQAN8QZfe85CqiBAGqwHsNNMGchPYT2EX5XfKiBgJ0a0hmHdNIaonWDj0OKg2TLi6EGgoTUkIUbolWDp6HIbW6AGgigBvsx3ARzGFNTzewLiy7TW4APN2w8roAaCNxflL3SDFBDiKC1NIOgQ9tSA+MZ01ADAbP7NewLmoUbHDWws3CuBvfjDVsTQA0EUIODGG6COQ1aFI9GAW3oUl7b/xRH/UyqQYhXKQp1Ke/woLHJ+aU3KEVZv46ilFf0veVy8tI+P2cpNoxFQg0EUIOTIE6CuQxaaQXo0G95X9FTd+8PrYZCXrXXbPVWD8ruQfGWK+UlfTd5X9JzLW8aIi/79VbyIfhh+LMyEdRAADU4CeIkmNOgphrqi7jfVDPvlgtxetdXeXOgqlkuOxjNA+UP5ZRzfZPeuMnlF7moElefRTdCj/8sBGog2KUGdns+3LADNabQhi4K1R/olzcHFO2jo2iXH+Wil+weqJVCvLsN1ItKn0U34uvDiZWHFFADAdTgJoiTYG6j6uFGHbr5/r+ay/W64VH9saj/r7uTkM+iD9H1zCccUFgAanATxFE0l0Hb1qxiN2q4mcujUcO2HFADQVpqYOiGeLoNphqK47Ewlw8eIA8oauO/44+G4XYoUv2nuw4rTlRADQQe1OBvzhNHNfDT5xzdF/1BDUPexcVYPqihElX9nA5D1sZ/coOm1zH/22ku3dCfnlh3ngJqIHCvBo8zpfNRg4uo/THA4S37AqeuezBWw6s7N2mevKyN//Ry41cPfJR9iH049GZYPhgJNRAkpoZs3OBUDaV4yClP5bB8UEP9PLUzncwpT+Z/cvnp602LXZ+m6Kc4LN0eaiBgqAZ0G8JFdR/bR4KutwA17AFqcBfFTTSXQb0Edx9/9bQnqIEAanAXxU00t1Hdx/aQoD2cOCw+TQE1EOxTw5KPePWNY5MbbIAbPCfQ8yEPy09TQA0E7tUQ4zhk9t2G6N0wzJZe0m2AGgg4qiG5Iwq4IUiC5acpoAaC5NTA0g1Qg/8Ma05TQA0EUIPLKK7COY3qI7iHDGtOU0ANBFCDyyiuwtkOez8bT9rbx77+1s1cepyMOUzd2o95Te2K21Ec5UWb5/vvtB7U1o5I/hhwgBoI0lNDTm6wFPVRGE8qPfPxpa+fbFr/XT+602snL7up5dINxdcJkVbLT8duT1MsOU8BNRCwVAO6DX7DFsYtmc7t9dRnUR2a9n6Ul1I2jfyhb+lmrK312snL5MZ3eTeo+mr6xmnx54Ivv5wCaiDwoIbVl16mqAbObqiMRqxvHysfqJvITq6fMNd2y8Yvq4fl+uay3/EwoLHkPAXUQOBDDethoIZ8ug1vYbThymzwh1r2C0rdayina+t66DWMVtx1wEqQt5Ue49oNy85TQA0ECaoB3YZ1XMR4xNBo/P+qRg2q9mbS07X30b1h+xVle8nmcMeHb7g+hbkoC9RAADW4DuMonL24pbol7MDQ+F/FX/QG+vbyH2tP5Muqo974NXfr2BFe1PBrRiTUQAA1uA7jLJ6tsO2dFvrbOfZtvGn7Mri6cfy16zaYaz/DaG5642U/e+V4dkN3nuIrUAMBTzWkONjA872q59XwlG3/0A9DFsTajzCjxwt/Ec+1GxZcRAE1EKSohszcYEUNn89v+njgMGnn47UfLyvU2CMjNSwCaiCAGpyHcRbPUlx6rOHR3fj1UMr7zl+7p5O105dVcuzxqn75atlYQ83BDVADAdTgPIzDgFbi0mcoTv3PSfxDmPeBna6ddCfam8lK2Sw7Q7G//BaAGgigBg9xXMWzE3c0r6Eejga6xn94lkKUz5m10yON/mayy+Y12KjAfqAGAi9qWD8dksU4ZC7dhurHrrwxulgwG3JnCltADQReJkqvv4gCavAZt6B/1npf+CXXUOxMYQ2ogSBJNcAN63j8asVbwi+58nJfBntADQQ71bDoI4Ua3AW0E3h0vwY70Zfcr2FnCntADQSe1OD3suwM3eDnUoTIU8wCNRD4UAO6DQ4DOg/sJbynFHNADQSJqiE/N8SvhoBugBoIoAY/cdwFdB7YS3idI5QcoAYCqMFPHJcRXQf2Et5jkk+gBoJU1cDXDe72frhhK1ADgS81oNvgMKLrwF7Ce80yAWog8KKGmI8o0G3wFd5rljFQAwHU4C2Qw4juI/sI7zHJBKiBIFk1MD6iiPaQIlk3QA0EftTg/dpLC6+3H8hhRPeR/cT3l2QE1EDgSQ1bSPeIIl43rAy/sY14dwPUQJCuGji7IVo1rIsv/kTiBqiBAGrwGMhpSOeR2/jLE4g/fyJxA9RAADV4DOQ0pIfQ6xJIM0TiBqiBIGE1ZOoGLmrQZojDDVADAWM1oNvALvSKBJ0ZonAD1EAANfiN5DKk88grMgxmiMENUAMB1OA3ksuQHkIvTWCaIQI3QA0Ee9XAehfP1A0cug0Weg1QQ1i8qSHAfMhc1cDMDdsbijc3QA0EvtSw5SoKRkcUcMPqDDvPUCzMYgeogSBpNWTbbeAw3LBvXsPyNDaAGgigBv+hHIb0Entpgh2zIdeksQDUQAA1+A/lNKaP2EsTbL6GYl2a/UANBKzVkLobolbDIjf4SbMbqIEAaggQymlMH7F9ZfCVBmoggBqCxHIX0lNwTxk8pYEaCDyqIcRgA7oNTknFDVADwW415NNtcLKDwg0c0kANBFBDmFguY/oJ7imDlzxQAwHUECaW26BegnvK4CMP1EDAXA3puyFyNaThBqiBAGoIFMttUC/BvaVwngdqIPCmhi2XXq6I7i6Ao1hug3oJ7jGH4zxQA4E/NWyD1elLuCFYDrdpoAYC7mpAt4F1dF8pHOeBGgighnDBnAb1EtxjDqd5oAaC9NWAboN7YncD1ECwUA37Z0pvhZca4IZwORzmgRoIMlADug0eiNsNUAMB1BAymNOgnqL7y+EuEdRAwF4NWRxRwA1hE0ENBB7VsG3OE7oN3KP7TOIoEdRA4E8NG2dK56EGuCFoIqiBAGoIGsx5WC/RfSZRiWxnghoIslBDDG7gP5rLJImLTFADgVc1hLn20lIMN8Gch/US3WcSF6mgBgKfw5CJHFHADWGTOEgFNRBADaGjuQ7rKbzHJPZTQQ0E+9WAwQbucf2E95jEeiqogQBqCB7NfVw/4T0msZ0KaiDwqwYcUQSJ6ye81yx2U0ENBB7VgG5DsLh+wnvNYjUV1EAANYSP5j6ur/g+s9hMBTUQQA3ho/kI7Cm+zywWU0ENBD7VsPUCq4zcADUEyQU1EHhVw1byUQPcECQX1ECQjRrgBj/xfWaxlgtqIIAaOETzEdhbAo9ZbAE1EFhQQyRusFzKaN0ANXwCNRDko4ZYug1wg3+gBgKogUk4L5H9xPeZxQ5QA0EcauDohnjV4MsNvuSwPw/UQAA1cAnnJbKvBD7THFoHNX+22ghqIIAa+MRzH9hjBq9pOjdADRbxrIaQ8yHhBq8ZAqTZfAgDNRD4VcPWqyjQbYgvg988KtfmZFADAdTAKqDzwB4z+M2za9gTaiCAGlgFdB7YawqPafadEIEaCLJSA9zgOYXPNEae1SmhBgKogVdAD5F9pvCWpjfD4bDhRAXUQOBdDWkdUaDbwCSPefpyfT6ogcD3yUt0G8JH9pnCb57D8P8qoAYCqIFdRPeRfabQeTwOOKhcKw8qoAYCqIFdRB+hvWXwnKlVQq+IhUANBNGoIUM3JNRt8Huioj2wWP4qqIEAauAX0Udojyl8ZpKXWPWXVCx/GdRAADVwDOkhtLcMXlMNfQYcUOwlNzWg2+A/hedMh/XpoAaCpWqw5oblm+7I4jyK65AeQntM4TfT0GlYfJ4CaiDwrYYdoNsQbw6fmfrJDcvPU0ANBFADz5AeQnvN4TNTO1V6xXkKqIEAauAZ0ktsfyn8pjLOYEINm4lIDXm6Ia1ug79Uq85TQA0EUAPTkF5i+8zhOdWq8xRQAwHUwDSkp+Aec/hNZXQafp+ogBoIMlRDbG7wM43QRxKfqfrzFEtOVEANBFAD15CegntN4jPVwbjU6ldOqIEgJjXADdEn8ZlqdPcGqGE1/tUQfD4k1BAyi+dUy05UQA0EVtTg6SoKzmqAG3imWnaiAmogiEoNnN3gdmdP7ZDC8zEozlBsIIQa0G3gFt1nEr+5zPGGeaAGAu9qQLeBYXS/WbzmWnTveaiBAGqwBtzAMteSqdJQAwHUYA+4gW+u70ANBEHUkKgbUlBDnm6AGgj8qwHdBpbR/WYJkewLUANBrmpAtyFwmgDJ5oEaCKAGm8ANrJPNAjUQhFFDqm5wvKPDDY6AGggCqAHdBq7hPacJkGwGqIHAjhpiPKJAtyF8Hv/JaKAGghBq2HPxJboNXsjMDVADQRA17IJ3twFu4J6MAmoggBpsAzcwT0YANRDEpwbubnD9VsAN9oEaCKAG66DbwD7bFKiBAGqwDroNm7KFlAPUQAA12AduiCGdCdRAEKEa4Aa4wTJQAwHU4IBU1JCNG6AGgrzVADdwSRQkXQ/UQBBGDbumQ6LbkLAbwsgBaiAIM1F6z0UUq7P5CuUhrMcE3hMFyqeAGgigBjfADbHkk0ANBFGqIQI3pKOGHNwANRCEUgO6DewTBMgUJh/UQBJoGJKPGuAGPomCJYQaCKCGaNUAN1gDaiBYrIZkBxvidUO6hxS+E0INBFBDvGqAG2wBNRAEUwOfIwq4gVWmEEANBGHUwGqwIWI1wA12gBoIIlUD3OArQ4hU3oEaCKAG28E8xPWbIkAq30ANBIHUsPcCqzjUADdEAtRAEEoNu4nCDT7elVTd4DMZ1EAANVgP5iWw5xwBUrXXZx8OB/dXakMNBFCDg2g+AntNESbXQSf0cA8HqIEAanAQzUdgzzlC5GozekgJNRDYUgPc4Duw5xwhcnlLCDUQQA0uovmJ7DdHiFx9OsfjDVADAdTgJpyXyD5ThEp26FK6TAs1EMSrBnQbPOcIkczsLDhMCzUQhFPD3klP0agBbtieaMUOuAeogSCYGnZPlY5HDXCDlZxQg18iVgPc4D1HiGSHDoc5oAaCkGpAtyG+JN6TjY8pHAkCaiAIONbATA1wA9Nkgw7kIyd5s1DD41IKSVk9Fm0PNbgK5zG41yT+k3VuODjLm4Ea3kcxcFryCmtqiH+wIf5uQ9puMP+3TAZqqERxe6pHr3shqgWvgBqchfMY3GuSANnUlCd3nYYc1FCIZ//4KYoFrwiqBmZHFHAD52zdjGkXg5EZqEGIuSezr/i61qUa0G2IN0uIbP3hhIPByAzUEFOvgaEa4AbG2bqDChfHFRmooRLF/aUe8R9r4KgGuIFxNj3tyclgZAZqqE/GGYrje8ELwqqBnRsSUEO6blDpnAxG5qCG+lGpeQ1FefE8r2HDp8VQDSm4wXdjDZBN3xvOXtchCzWsJaQa9l98CTUETBMk3XA4YXM0EmogCKoGC8AN4dKESNfPcLB6YAE1ECxXA083QA0h8wRI116DaXc0EmogiF0NcEPQPGHSWR+NhBoIoAbnAT1G958nTDpzNNIGGahBjFnyiq9rc1QD3LApXYATFThDsYJrfmqAGwLnCZLPGG6wES4DNdTPYtGl2ANQg4+IPsN7zxMk33BHOBtpc1BD/Vw0PXogfjVE5wZv71PSbjDS7s+bhRqaY4rn1/VizXgE1OACuIFb3jzUsJLAatg/H9LJDpmIG/yfU/Sd0FJeqIEgrBosXEWxLbH3iD7DB0gULKGdvFADgUU1hLnAalPeECE9hg+QKFjCLu+uxFADQXA18HRDKmrIxw27EuemBt/zGlI6ooAb4kloITHUQG30dS3U4Ip0DylidENualhEeDXk6YaEuw0RugFqIAisBnQb3AM3/ARqIIAafMb0GT9EpnAZu8TbMmehBne/eclZDeg2cEnVZYyr45CBGlz+5qUzNaDb4JwADTUqN2SgBpe/eelGDRkfUcANjhKvz5yBGlz+elV2akip25CVG9ZnzkANLn/zcsEbntZgQ1LdhszcsDJ1BmoI22vY2G3Y8iI7qYME9ZogUK4uZSwdhwzU4PI3L12pwQ5uUsMNEebckDkDNbj8zUveakC3gVmygDnbzCtS56AGh795maMa4IYoc65OnYUa1gI1BAnrMUGwZAFzdqmX5oYaCJJRA7oN/H0Y9XoAACAASURBVLK1OfnLAWogsKoGdBs4JgiYLWTSNbmhBoJ01BBrtwFucJx7QXKogQBqCBTWa4Zw2bqkvDsOUAPBCjVwH2yAGzhmC5t1YW6ogYCFGuxMiIQaeKYLmnVZbqiBwK4aQl5h5W7ngxvsZOV7pgJqIEhJDfF2G/JwA9+OA9RAADUEjOs3RcB0gdP+zA01EEANAeP6zhEuXeC0v4AaCJioAW6AGwICNRBwUAO6Df6SBEvXp+UoB6iBIC01RO0G320mVBtl6AaogYCLGtBtgBvCATUQWFYDug3skwTMFzrvLFADAdQQOrD3JAHz9XmZyQFqIGCjBu5u8LMz5+IGZh0HqIGAhxrQbfCbJVy+8IkpoAaC5NQAN/DOFz4xAdRAwEQNti6+3FGCkJH9ZwmXL3ziT6AGAi5qsEjk3YaM3MBgZ9FADQRQA4vI/rOETMgg8xiogQBqYBE5RBojYe4HFVADQYJqgBtiyNgl5rDDQA0UUAOPyGHyhMwYPvMA1EAANXAJHSBN4JTBM/dADQQpqgHdhkhSBs/cATUQ2FYDg885hW5DVocU4QccoAYCRmqIYdZTwmoIKvXAboAaCPioweJcaaduwHBDarmhBhKogVdw72lGKXM9qIhODcJDkjTV4LRdJXxMkWvHIUI1COdZWKkB3YYgeULn7HMHSw41kDm+4XcYEt2GUInMnDl2HKJTg48jCqiBW/AgiYInDZs7PjV4YI0aYhpsgBsiS9rlDpI8IjWIHodJdKava6GGAMEDZQqdNGTyiNSggwvXWX7G960GuCFgptBJAyaPSA2i/5ORGtBtCJ0qcNI+uffsUMN8phliVgPcEF3SYNmhhvlMM3hXQzTdBrjBbXa/6SNSQ92NQeY0DCndsP21BHDDrqQZdRxiUoM37Ksh9FV0A8moIdBbmk/HAWogSFkNcEOUWQOkj0wNftQDNbANHygVg7Te00MNq7NErga4IdK0vtNDDauzQA0hwwfLZabNYsABalidZZMa4Iboc3HI6zM91LA6C9QQNHzAZAzyekwPNazOEr0a4IaI83bp3eeHGlZngRpCxw+XjEHePr/rAkSmBj+wU4PdCZFwQ9R5fRUgIjXEfL+GfZ+j5UusElNDxm5wWoKI1BDz/RqYqQFusJM3aTlEpIaYr7zcrYbIug2ZuCF8x8FhCaCG+UwzTD8MdBuCJAiYjUPioQSuigA1zGeaAWpgkSBoOiMxAzm4CRuRGpjeryHOIwq4IYHMfQmcFCEmNXiDnRpi7DZk5IY05QA1EOSgBrghjdTuihCVGtzPaGjzfF0bSA3xdRvgBq9FsF2GqNTgq1PCTw1xdhvgBq9YLgPUsDrNRjVkeEThvcGEdAMDOdgtQlRqiPqAgt0RBdyQTm4XRYhLDU6jL80TRA2RdhvghniBGtbn2TZTeu+eY90Miboh8wEHe0SlBh5jDYHU4AAvJfJe7cwHHKwRlxpqL/OkHakhUzf4r3XOBxUH8uEmIlSDCH0vKaiBX5LAGXnklul1x+VwOOzuwUANZI6vpKMGuMFB7uByaP/uDRSdGsJfeZmSGnBIkVz2Nv1h9GcL8anBA1BDhEkCZxxlZ/BJ7z+uiEgNXO4NuVkN2bohQL1ZfHOHLMBh9N8WIlIDl3tDQg08kwRPySm9IYUM1MDlLk9pqQFucJQ+aP5D/9/2rgPUMJ9pjnBqcDAj0lMTCuGG0HIImHrcadg25AA1zGeaI5gaHFxHUafbbQjfcQibue80bOw3RKQGLveGhBr4ZuGQlEv6rtOwdcghJjV4w5Uadl9hBTfEkNRIH3xepuw0bCwK1EDAVQ1RdxuydEPoOduHcadhlR8iU4Mf9WSmBrgh0fx6kKF/uHI0EmpYnyWoGuCGGJKyy39YfxYTalifJZwaIu82ZOoGBqORhw2jkVDD+izpqQFuSLgAbYdh9QQoqGF9lrBqiLrbEMgNoeUQeGrktglQUMP6LAHVEH23IdA3aGg3cCnAmglQUMP6LNO3FUcUPDOFz8qoAOsnQHFVQ3fttTAX2Qr+M/n31YHVELkbArWR0E0z9FHN+glQTNVAX3/N4ydqgqrBWbcBbki8BJ8ToH7AWQ3ElVQe7uNS81aDk6svJakfUoT+2g5egskEqJ+wVgN5h1h2d3nyrAZnJO8GBu9+cD2Zs6Z/bMpbDR+HENoLvC7KTkUNcIOXIgQ/rBhOZH4vClM19E4gxxriVQOHnXMWj2XL2A2hDyvMAnwtClc1ULETOEPBYt+cJf1uA4/3P/yQwxIiUoM/clVDFm7g8AmwKMQv4lHDs3QXewLUkFQqNpkNIpADbzU8TkKcnvLRs/R3PJGvGuAGf/AoxRdYq+Gh7wb5rF+NGERlNfY3HKqB+x4BN3iDe8eBtRpOUgeVON0bMZRvq6G/wl8NruY9wQ0+YVKMGVirob2MQhSifFoN/Cvv99Xh1eBstrTf3TWgG3i0SibFoIlCDceH1bC/835fnbQaMnEDl29sJsWgiEINVoMuyft9NdQQZzI2qU2YFIMAaqDyfl/NQg1wQ8SpTdgeVEANVN7vq3epAd2GkNnGqZk0SibFmMJcDSOsxv6a9/tqHmqAG+LObcDFUWOgBirv99UM1JBQtwFuqHnKgbUaQrFSDUEmNjhUQ1Zu4NImuZRjAGogiEMNcEP8yQ3YSKoDaiBwqoYYug1wQwiYyQFqIIhEDel0GyK5g4Fz2BREAjUQxKCGtLoNgRsFmybJx1JQA0kUanB4iZUEbggDn5JADQRxqMEt3ksJN2jYdBygBgK3amC0G34DbggFk6JADQRQgySvQwo+39bB34kWqIEAalBk5obg+QdYaApqIIAaFJkdUoTPb8CgKFADAdSgyc8NoQswEL4oUAMB1NCSnRvCF2AgtBygBgLHauC0//3AvxtCvzfBC2AQtixQA0E8anA77SmIxII3zeAFMAgpB6iBIBo1uJ0sLYEbAnMIZgeogSAmNcANDgoQvAQjAhUHaiBwrQZ0G/jlnJQgfBFMghQHaiCISg1wQ6pFMAlQHKiBIB41eOg2ZOsGBmUw8F8cqIEgLjWk2G3g4Ibc5QA1EKxVA7oNiSSdwqIQA36LAzUQRKYGuMEZLAox4LXjADUQxKQGH92GQG7g0C5ZFMLAY3mgBgL3akC3gXHaMSwKYeBNDlADQVRqSLfbwKRZsiiEiacCQQ0EcanB+YUUkqzdwKIUBn4KBDUQRKYGL4RyA4s3ikcpDHyUB2og8KAGuIF73jE8SjHgQVZQAwHUQJG5G3gUY8B5gaAGAqiBJG83cCnGgGM5QA0Eq9WQw2BDwCIzea+YFMPAqRygBgIfamC4o/0knBt4vFlMimHisEhQAwHUMEO4MjN5t5gUw8SZHKAGAqhhDriBSTlMHJUJaiCIUg0+Jj7BDVzl4KBQUANBjGrwMV1aAjewlIODQkENBF7UYHlX93GVlSRgs+DTIvmUxMC2HKAGghjVkEG3gVGLZNlxsFwqqIEgVjXADf5gVBQDm3KAGgiiVIO3bkNQN/BpkYyKYmKvWFADwXo1cHCDt25D0G9MRg2SUVFMbMkBaiCIUw1ZdBtYNUimHQdL5YIaCKCGH4R1A6MGyakstoEaCDypId4jisBf3ZzaI3c5bC8f1EAQqRo8dhvghgHWcjgcNs+UhBoIIlYD3BAAvnJQJYMa7OFLDTF3G+AGE2bFqTtdDWpYry+ogSBaNXi6xkoRuDUw+6ZmVpy6u+Sq/7f+wAJqIIhXDT4JXfjQ+SewlMOhVcKWAwuogQBqWETo0ofOP4Vbedojibo/ulj3aqiBwJsa+O1Nqwhd+tD5p3DrOPSDDIfx02VADQRQw0JCF59bW2RWoM4Fh0ENK8YcoAYCqGEpwcsfvABTOBWoG2E49M/WjDlADQRQw2KCVyB4AaZwKlA3CKmfmGMOC7oOUAOBPzWw2pE2EbwCvPrwNcMCEWMOS44roAYCqGEF4WsQvgQTuBWoO5ToxxwWHVdADQSxq8HjxKeaQ0Ng9z3NrUCHgyGDrsMANWzAoxpcNCyf06UlDBpC+BJMYPCefNJ3Goxn80ANBJGrwbsbGLRMfk2RX4m6MYcD1LCZBNSQnRsYNkV2JWrHG4ZzmV+BGghiV0OebmBRhjHc5DA5l/kdqIFggxr4ucFF2C9waAQcyjCBmxwkCydMQw0ESaghw24Dj0JM4CcH4zzmN6AGgvjVkK0bWJRiArtCLbuOAmog8KoGuMEqPEoxgZ0clgA1EKSghkyHG7iUYkqEcoAaCFJRQ6Zu4FGMKUyLNQ/UQJCEGgJ0G7js/kyKMYVpseaAGgj8qsGdGxzF/QaTvZ9pK2RaLBqogSARNQSBS2W4lGNCRHKAGgighh1wqQ2XckyJRg5QA8EWNTAcbAgEl+qwbYNsCzYGaiDwrAY2jckSbKrDpiBTopAD1EAANeyDTX34NkG+JeuBGgh8q4FPW7IEn/rwKckU9nKAGgighr3wqRDjFrj55+39ADUQQA27YVQjzg2Qc9mgBgLvanDakkLMfGLlBlZlmcJXDlADQVJqCDBfWsFpl+fb/mq+hctHDdejEOV90aapqQFuYNv+FDwLl4EahCrUSSiqRa/4vjouNcANGl6lmcBRDrmooRLVu65flbguecX31TMfI9wwhdcOz6s0U/iVLhc1FOItH7/Fcckrvq+OTQ3h3MDsq5pf8zPhVrpc1CCE8eTnK76vjk4NcEMHt+Y3htdEh1zUcO7UUCx5xffVLtSQrhs47e01v/JMYFS8LNRQXq43cWsevqtF45DpqSHYKcyaXceBXXkmsJFDFmrQqIfFe8krvq+GGtbBZV/vYNP4ZmBSvgzUUD+f12tZqsHIaokZgqgh2SOKmp8buDS+WViULwc1rCZBNQTtNvDY1UfwK9EYBuWDGghSVEOgSyk6wu/pU/iVaEzw0xVZqOFxKdVoQ1k9Fm0fRA3s99V98Ktd6Kb3m7AlzEAN76MYOC15xTY1cO82BIZhS2RYpAkhuw4ZqKESxe2pHr3uhcOTl+g2/IBh/RgWaUowOWSghkI8+8dPh1OeoIZfMKwg/45DsDJmoIbR3GiHE6Whhp9wbIgMi/RBkPctAzVE02uIYi/dCcMqcvTVBwEKmYEaKlHcX+oR87EGju3GNiyryLJQUw6+hyQzUEN3FxfFkZwOKSZ8jxe3GsLOb+D5Hc2yUARey5mDGupHpeY1FOXF5bwGCy3bwycfdFakgmUzhBw+yEINa9mohji6DXADDeQwAWogCKYGT92G4G7g2QqZFusDT+WEGgiSVgMHNzDtOEQkBw8FzU0NLuc12Njhs3ED00bItVwfuC8o1EBt9H117Gpg4QauHYeY5OC2pLmpYRFb1QA3rIFtE4xGDm5LCjUQJK8GJm5g2wT5lmyKw64D1EAQUg3+3OAlz3f4tsDgN1JZjqtpklmowc+tXL6uWU5G3QbeX8+cyzbFRVkzUIO3W7lYata+3OAlzU84t7+85ZCBGrzdyiUqNbCBdftjXbgJto8rMlCDt4uybbXqiHZHK7Cub0xysFvYDNTg7VYuUMNGeDc/3qWbYLHrkIEaPPYacESxEd5VjkoO49LuUEUGavB2K5fvq1awJ0pRXtvKXst5Dc5udR09W9THsgLz1hfRucwRsthbS56BGhbcymVCzGpo6qjtd/52S5rZrb49cwv3thejHFSZtxY8BzX4upXL91Vr2BGmsZ/+4i+OX9Uws1U4NfBve+wLaKDLeugfbuj2ZKGGtcSthosaW3k0f2U9Ho0Vi6aHUMqlT3EktvozbNXe/e5VqmdSDZV+5AX2TS8aORyUCw79RMnDhrJDDQTB1bAjjhBNc2/+Xpq/TT3u+jiqqt9yttepG5EdbfXnT7+VVsO7UHNH5Wal6I89PMC/5UUjB4nuL5gP1gA1EMSthuYgofl7FOpw4ChusrMgpAXuN3Gmtvrz55/9VqrTUDXbPfSz07t54ZKzOnaIoOVFUMSO/qBieLCi9FADQXg1bA/UtOizeNWvpnXrkYLX/XJSj4pCFG9qK3k9RbeV2vIo3t1mrxojDlMiKKKmG2roBx5WHVdADQSRq+He9AGuzT/VpE/97fNvQlzprf78+fduK7Vl7wL9wKsaYjiqiOVcpmGEWo8/9AsXVABqIGCghu1no4UaVjg1X/yySZ/F8Xp/dWq4zWz1P8Q//5feioEa4vhWjqGQh4Mxs0H2HtTfemHvAWog2K6G8N0G2Y6bFi8HHfum/tYHFMdjMbtVc0zxpg4ohv99EkO7i6LrYJ7DNI4rFo1KQg0EHNSwNZJsx1dRyvMPuqk/6vepHYa8q7MS5Fb/8ef/DWMNlaiMQckAaojiqKKORGH9SYrhPGa38CtQA0HsamgODeRZSvmw6n6q7y2nNJzM7gC1VfOs6Vm81MnLY0g1RNPqYihnOxL5MSr5HaiBgIUaNobSxw7qfKN6eBbiJE9Elk33oekKlLNb/ft/6K6EXPZsehDnV1A1RCOHKMppDEAOo5I/gBoIYlbDZrjc9skghkYniaLrMEx+GqZPf93HoAYCHmqI5HjbKXG0uTqOgh4O497DD6AGAqiBDzG0OUUMBR26CpjXsI0daoAbrBPNuxCDHCT9SOR3oAYCqIEVsTS5eEq6qJxQAwEXNcANLbE0uWhGJJcANRBkrgaG5ypikmQqcoAaCNioIUiLYPLDVhNianBpdB2gBoI9auDSbbjrWzNUbV2uR3Hsrrus2olM/TWZk6f/+5/in3Lb831zdhdE1d6iKiwN1EDARw2boz30lVRV2/TPqtlfzWWtCrorroant37bYtmtNL0RVXOLvusANRAkoIZCaaC7X/RT3q3ppm8MObqH9H24Trt/ehSPP/9H/Pc/0zvPMyCy1hZZcSdADQSM1PARTkygX1WpRl0UD71BNRigX6afHUcv65/+kZdpN8+93RVyKZG1Nlc/cO8DqIGAkxo+3fDHYKakb32n16q7NOokXt2qfpl6IkbDCcPTu/ifcjCyEkt+tsMv0bW1WPUANRBEr4ZL38b7aydLcRwvq2t9ofbA8LQUpTpRMdzggRMRtrQIiww1UOxSg083zBW07HsJnRrULSIf5rJaGmQ00jA8rY7aDf+3u4ibGRG2tPi6DlADAS81TAMuUMNwyNCpoZK3YjhN1hbj15tPb80rZAKuH2h0Da2OrsxQAwFvNQxumC3nVA3F6JYs3d9n5wrqqdyIsRqia2iKqLoOUAMBMzXMumGxGkpSDdfh5vPm00KNPQa9wdMyYmpnPfHYAWogiEQN88WcjjXc9AHF2VzW/gym+SL9tJJjj3rjF9Oxho5omtmISEoNNXxQzXxVthMJzk/ikx1NDjpQX7b6nqyash8PXPh+0W6Yf/H0DEX7QzWv0bLpPeW7p/oXLwu5Mc8zFCaRNLMJUXQdoIYp1VyD7WcZ/YNaZzz5qYb2XIH8Edpllyl87Ejiqxm6eQ1GwapCnJ6TZXO/RPM665vG1iznNUyJoZUR8LcD1DDhPDvFsF1cib/MrtOQn/nwsw71u+qmLJf979P+gOo2fHuvKzsfhL/fyN4D/1ZGw7zcUMMYNY+4q4U6+n52E4FGX7aPsulzV+rZsziNdXJoPfAqxTDT2FDD8DX9XjzM9+mG768srl9XL8M4TOJ4nbYB70Y2D+eZklDDmMocAVA//HbqButMNdz1cUGlZxOdaTUU7Sb9q8a9hnvTZTgvPKJYrYaHjSujhisved7DwYRvE/sFVztADR8Yzfwi7re+z68Xv8/ib3LI7lZ3v/1W1ZMDivqgV5ze7S++dK8ejzXIKxbuS48oPt3wY/v70sDzmPdr+AM5OIRl0aGGD8weQFGI4j0s1/ybfPa6X9ofiXzVM2oYrTDVcFb9EDUPafnEgcB7TwRu4NnClsGw6wA1fGCq4SaGaUHtvU70yctTd0k0NTXoQKzoF93bSYeP1hMLjyjghiXwa2DL4VZ2qOGDiRpuw/L2wUGeXDhe769NamgOJE76T9uFWFqu4HsO5OAYXl0HqOGD0QHF8VgMy9sHh/bxe5sa6qOaSKTnI7/F8vHC4PtNFG6IWg6c7AA1fDAehhwmBI7V8Kjfpzk11N/V8JQ/Xv9ouwtnsfwGjMH3mjjcwKl9bYDLCU2o4QPz5OVRjiqMphPXqoV2RwODAYqa9AClhsY4xyaCVsJ9xbQiBntMHG6I3Q4sig81fGBOeXrI7/iyW94ulp/aWYjTY2ju11VqkIcURXcgUayYgRB+f+E+98mAQevaQ/DOA9RA8KMWvz8xZ5/p8sDdVOn2rm6nmTqxvup6L5HLIbAeoAYCvmpYHLm/fFRdj930anJUA4+O+U6CVQFqINithuDdhuHHJk76oChTNdQJdB1C2QFqIGCshmWRjR+bUIMgx2JQwOc1Y8aFYMOD7sJsIdT9pV9HUaqTrXI5/yu1RyTQdQhxaAE1EHBWw6LQw49NqBMgF3Eb1PB5zdhwIZi+i0vZ387lLVeWzaPbsZ2cpZa3WolnRDKFroN3xUENBKzVsDB26wI5bUKeYzEOHD6uGRsuBKua5aq/UWl/KMWc5ZzQqlZ2uchFlZ48Hskkh5YUug5+awE1EPBWw7LgrQvkn6L59jfHFMbXjJkXgvW3hDvKRS/ZPVArhXh3G6gX6dO5f+KSQzp28FMNqIFgvxrCdxu0Cx7dPSFMNYyvGTNnYBC3hFOP+v/Gv7T5JzY5pHFg4UlyUAPBRy3UzQ+uR3FUTerf1Hwn+ejWLaofp+FusJKDcQ82NafAvPnBXpbsF92lXPfhYvJu1fSasc1qiFMOydjBcUWgBoJpLdQtk9StnOTX7au/G8utWySvtJIMN28/DPelbucUFMuvlfiJuVN8PStZGM2533B6zdjgAfKAojb+O35ki84N6djBcUWgBoJpLeSNFp9yrO4mG8tZD8UdZUN51Hd1dvAql52NW7P/rW+K3ZyCq41bsnUMu8TcneBU0tfkFlWKj2vGBjVUTTWe02HI2vhPbtC8C6NfvYrPDckcWDi1A9RAMKlFJRt11XfCx78Spx+0kwX6H3Qpiv/abdDPKShs3p652yFmby2tkn78HkVNXTM2qOHVnZs0T17Wxn96+finbeK55sokma6Ds0MLqIFgXAv9ow6n/gehdLPsTu7rKycnuqgrfc+G2pxTYPdHHfTeMP9bNSppf4BgFO3zmjHjQrDnqZ3pZE55Mv979eMsJvGZQZKMHNzoAWogGNdCf/WqWYFH9SUsP4R7O8hf6jb2edeGVg3GnALLPwUlS/Hzxy/BV9LpOtRaDzarAzUQjGtRtqf9T92toA+y590ebldH5YZZNRhzCmz/gORh+MlsuGEzSdmB6D5srx7UQDCuRdfs1S/KSiMcBjPU7aWNn2rQT0ZzCmxfyjSYAW7YQ/A7I7ijrdmmGkINBJQajNGEw7M4Tda3aiimLxudOLSsBtMMfNwQ57BDap2HlqZSB/1nQ+8BaiCg1FAOrfu/dEcG+s6v+nz/+AxF7UENLHsNEU5z6EjPDtIMSg11+9+q3gPUQECNNdz0AcVZTj7u+gyVHFhUy+R1Sc3Rg/lDk4MIHI011CzHGuKbH2mQih20Asx/Sg3reg9QAwF1hqL9UZpX/+s0/Vn+4qXnNQnRdyLqmlCD5TMUKjI7M0TuhnTOaB4OQ1+hbh+t6z1ADQTUvAY580mc5Fwf4xihP/uvrqHQ5/tn1WB3XkMbmp0ZJJADBw7d2cxODSt7D1ADwXQ25OT5wl1nutmKu8ovZ3Y25ED7w7htLQaHPU+iqN7jZYrRtKZt14ZF7YaETlmomrQnKYjew1egBoJpLYrr6OnS3Wa8ndVrKAbmrqHoeei87eVe9/6HNvUxkDwaGpYphsvH6u3XhsXthjoVPRijkIfP3sN3oAaCaS0e41a9TQ02r7w0ET+Ko73W3UdWXhFWP/RVYtf2srBhWa037S4f23VtWNxmUMRvh4P530fv4TtQA8FHLe6j36zdpAab92v4lmeKujZsuI/s9N4M8mzL5NSqeT2Im2vDIiJyOxgDj1CDDX7UYluvwSFfErVjqP2EzVL3EEpTCMMyA9VrcHVtWFTEf2hhDkcaivgB1EBgSQ0c3PBxWXbV3jy6PskDCj0C0S8bUJeP2bo2LP5ji+jtIJn2Hn4ANRBEp4b5TGV/LXnbxsv2dvLyztHqkjFzWY++SMTStWHRD0kqEpDDpPfwA6iBID41zKaazq6o+sHH+i5/dUbfuqlf1qLNYO3asLjnOfQk0XWoh97DD6AGAltqYOCGqRom14G9jGHI4SREe/mYvQtAIp8hOZCGHJaNnkANBDGqYSbXjBq6pQ/qivJbe/Bg89qwP6nYwfYNU/gCNRBEqQb6G2061lDKW1yq204cm8OFt7qn5bBM8RDTS85rG9eGpeIGSRZ6gBoIrKnBrxuobNMzFM/+rvhX0Z2XGJapjU5GX8HqtWEJuaHOQA9QA0GsaiDSvfvRxbaNP0shSnVH6P4nd4xlei4UoQZL8xrSMYMiaT1ADQT21BDeDdNrwzbi5NqwJEhWD1ADQbxqIPJNrg3bhotrwxLqQSRpB6iB4GstBGs1fA5GPmy0agfXhiU18JCiHaAGgm+1EH9+Xeo4xv8OM804vjZsE06uDUtrVLJO7tACaiD4UosFt04ZE2BniWX/TOl0ZkdCdoAaCOZrseGGa3DDPMlMhDJJpfMANRDM1mLLbVpD7Cbx7JpJyiGNzgPUQDBXi003dw+yj0S0Z6bphgQ6D1ADwUwtNv4kTJgdJKbdMkUzKKK+4AJqIKBrsfWH5ALtG9HukqkRqx6gBoIUeg1RHVQkT4x2gBoIrI41BPz+jm93lCR6dBFd5wFqILB6hiJkC41sZ1QkOiqpiEoPUAOB1XkNYb+849kTexI9n9lxiGVsEmog+DUbcu0HG9QNMeyFY4YRndAlcUcEfoAaCH5cQ7G6qYfdB7jvghQZyKHmfnwBJxfwiwAAFJxJREFUNRD8uPJyfVMPvAMw3v/mycENNefjC6iB4FctYlND8PwbycAMLRz9ADUQWFdD8LbJbr8DnzDTA9RAkJ4aOJRgL1l0IRjZAWogsK8GBi0zfAn2kcfQQ82n8wA1ECSphugPKrI4adHCQQ9QA4EDNXBwQ+xyyGG+g0HocxdQA0GqaoheDpnMdzAJ5weogcCFGpi4IXo55DLfYUQQP0ANBCmrIQE5ZDTfweTg+QgDaiBwogY+bkhADgPoQbgCaiBwowZGbkhHDpkNTXb48APUQJC+GtKRw+iufKEL45cDdYhhTxpQA4EjNfByQzJyqLN1Q4vpAvVQ/rffEFADQR5qSEgOdXI/obkV9YkqNQxPNn7MUAOBKzWwcwO/AgELNC7oew+DI9YCNRA4UwO/pphSx2FEzn2Ig9l72HyEATUQZKSGVOWQ39jD0PQPh8mBxfgIYylQA4E7NXB0Q5pyyHFostdA+5GaajAWLwRqIMhMDcnLISc7DFLQBxa9GobFC4EaCByqgakb0pRDfldjmccUphpqqMEOGaohVTl8zKRO2xP9gMMw+KifGZ2JpZOioAYCl2rg6wZONx9zRtrdiEPf8IdhhnoYajD2vQV+gBoInKqBsRtysEMuIxB99+EwPrCgtqGBGgi2q6Ga+5Htnu9qMLcLRPJ2yEUOLYeD2XtYAdRAsFkN1VyTHjX5Lx8QAzXU6Q47DOTkBk1/hLEYqIFgqxrOX5v0sG7+IwqrhIH05ZD6gOQHq3uDUAPBRjUUxWNo2qV41vVTHI2oat1dVE2Ah3iobe71q/HJ+TXepuVRClFUaumzODX/v0r9vH7LF737NfWlEMfrqir+JAc5mGQmigVADQQb1VCZTfstmiZ7kn7oo+p1omj++7vQTb5+F/LooXiPt1Hc9ZGFinoS5+b/on1eqwfHultTqQ0tuyEvOWQ1/LAMqIFg+zCk0bQv4n5rmu3HurPUxb9IQTxVq1YGqfpthqGGo7jJbYRcqkVyetdX+cKLfF5JF3RrXvVDKccuyY9IGmR8S5gZoAYCK2poji+G3oCx7i4uTTv+e3NEcW2OJ45Nq65f/XHHeBTydb+ctBpe9fC/lIbatuyWFeJ8X1XB5WRkB7hhDNRAYEcNt0kfv1vX9BMq8W6+7kvRLexfNhprOHWWMDfSz8dr7s0BxvFVOyIjO2T083m/gRoIrKnhRq07i3dR1uW/tF/7o5eZrz+L4/X+WqKG5rjjKIrHz3pthsPvrAUiX1NADQSWDiiOx4Ja1xxRNM64NccUt/rzgGKy/ZtSw1FMY9bN0Ynj9z5POWR8kAE1ENgahpTDCsQ6IUcImjYv3vXnMKS5/aN+nyg1VHL7m3ylXlY0Wz4dDENOyLLrkO/wJNRAYEMNb9kTOIk3se6sOglHcWrifJy8HA4WKjE31qBfJM906GV6S1NDrshRDnTPIX1PQA0ENtRQyklNTzmc8LFOzXqq5VFFE2g65ck4RdGsOT0oNagXnR5DzKoQhQ8z1Jl2HSRTMyTfjYAaCNxeeekmkkeytYNJ+ocZUAOBPzXE6YasT1l0JD8GATUQeFRDrG6o0XmoyenV6YgCaiDwqYaI3YDOg4Iag0jBD1ADgVc1RO2GGnqYkM5xBtRA4FcNsbuhhh5GpOIGqIHAsxoScEO9/EbFeTA1Q4SigBoIfKshnZlE0APNaE5lJI6AGghcqOEub91wPbZ3Y+rnLNU3vagJ+TgJUY6ukRpuQntSU6BcXXltF9iBIEI3QA0EDtTwkFc4nLu7Mb30jMeHujxTLzq8RTf7uWO4CW177ZTLiyutgs4DAWkGxq6AGggcqKG4ymnTp3fjgqN0hLpN01FeSfGo7+rKqP8sl52NKyGGm9A+20dX51dQWQR6WADnrgTUQGBfDZVs1FV//4aCuEtDKf4xuujCuAntsWgfFd0VmpGAoclfMD7OgBoIrKvhrS6oOonxnZi6uzTc21vByrjD9VnDTWgv4tZdRmVeyRkL8MNPyEmVoV0BNRBYV8NFyBHEpn2X4tiPJd7b+8OVuqegf9hqdEMW/UT2JIz7SsYJ/PALelJlQEFADQTW1VC2d3w9tYOPkpf8AQlJdVRu0D9sRaihKN6dGl7mRd7xATsshnaDV1NADQTW1dDdaqGSJxu0EXoz1PI0RTVsc5i87Cx7HP0tZ2N/g2GHFZBm8NaZgBoIHKnBHHx8Gmbo7gWrHhXG/Ce1aHT/+ejVUOPYYgdzbnBiCqiBwJEayqF137ojg0K86+5esM/uDEXXctJUg+JwgCG2MWMG+10JqIHgay3E5rGGmz6gONf1Q3R9hkoOLKpllfzv3A5N6kYziCCNsYZPDlCEDZwcaEANBN9qIf6IrWco2t+cefU/PtPfAbZ46XlN+i7T7ckKQg3xnqH4Afywl9kDDcIWywQCNRB8qYVo3tX1bni3d5OvCnGSM6GNY4ThtrHyGgp9XYU+WXH4VEOU8xoWAz/sZvZAwzTEn3rRDbGhBoL5Wgj1FovVO3C15Y35bCcistmQG4AfLDJ1w5/BClDDJmZrIdo3eH01i00/cT9pI1FdQ7EH+MEqYzdADTuYq4Xo3+DV9Xxsa9XjBhLNlZdWwAClZbpeg37yc3OogWCmFsKQ7+qKqvs1bMBoHJHcr8Eu8INtoIYd0LUwzbDlmGIraBn9OU68FRaAGnbgotewB7SIHvhhN3qoAWrYhIOxhp2gPZigB7EHY8DhK1ADgYMzFPtBU5hywHHGJhbOmYQaCOzPa7ACmsAskMQqFk2mhhoIrM+GtAZ2/V9AEbaAGgh+XUMR8jdlsNcvA37YC9RA8OvKy8A/N4VdfinoQewAaiD4WYvQOxt291VgvHILUAMBezXU+JmHjcARi4EaCGJQgwR7+HbQk/gF1EAQixpq2MEGkAQJ1EAQkRpqHFpYBF0JA6iBIC41SLAz2waSgBoI4lODJOfd2CUHgtBl8gHUQBCnGiTZ7LeBycESUAPB71qw3iHS3mO5kawkoAaCyNWgSHFnZU5ikoAaCFJQgyKd/TQukhiegBoIklGDIs4dMzmiswTUQJCWGhQx7ZPJ461TsSss1ECQoBo08ANbKF3s/aiaAF2MLtT07xegBoINauh/g+77b1nLtTNbkL8/4+KXsaPq1ebNPl2obbv/yL9fgRoIgqiBXOxCDS2xHfoCzfLuxaH7X+vg8+93oAYCt2pYs8qhGgagiOiZd4V8eGgfTP9+B2og2KsGIV6lKNRP1z5K0T56nUTZ9xr05ur/SyGOV/3j2c2zt/zh7Pdoe2/AD+kxdBCgBhssqMX0fZ2ooZAtvTHCXYj20VsuKj/VUKkNrp0a1AuPtbm9X9CFSAn9OUIN1tivhtO7voqiro/iVtdPrYBT/T59qqHpYdQPuanuQkiLVI0pjO3DgKGIBDj0ww1Qgx32q+HVL3rdL6qBH+Wy16caCtH+yq1adNTLS3P70EASsTI6cwk12GC/GvpFJ31EMe4nmM/uzYHDsTeJEJ/b8wGSiIr+Y4IarGFPDWdxvN5f39TQHG8cRfGIQg0DNifmADcMHw9OXlpjgxpOQh8W3MVpqoH6PXNA0R8uXPtFxz41nwOKn+yZlgN80H0m079fgRoINqjhKgrphubw4DpWw6MdTLzIocl+GLIQt3Z50Wzx7IchKzkMeZN6GbaPEEgiAbJQw+NSqm56WT0Wbb+kFp/dBs2p/jw1KR+NT16q5Zdhi4s649luJcQz3MlLyyyfuweYkYEa3kcxcFryii1qqG9SP+VNvX4YJzg3KR/68KE0pjzVVdHooHtUNGbQJzvrl3pBbW6fHtBFDGSghkoUt6d69Gr6+9WCV2xSw2LSbO57QfeCGxmooRDP/rE6qv+JUzXEMbTIA+giIBmoYdQUF7VLl2q4CHHe+FIgIbsXMId9MlADr17DWU9wAo5I2Rzd5MaZv5bJQA2VKO66Ndoca4j1Rk+ZE7M4hisiyDlMtslADf15RXVR43vBC6CGTOHd5TAvo/z8a50c1FA/KjWvoSgv9uY1QA358cMcu1iQ3bDA6gsiNpCFGtayrBZwA7DHT22MOgikGiwbCWog+FqLYeUC19/P3SVT8tnj1M1nGpbdjuomT3V7aTYAJIffarBMFmqwOVFajE5//lDvo1A3cmk10N7y6W4uu7U3earrYlnZQJ4MN3JT/0ENNrA7UVoQMyNmO2fFVQqhOylyFE3rf8jbu42X3dUZVfJm8wAohgMAqMEeVidK9wcCBB9HcJVq8uLWv7b7f1hmrCiWFA2kQLePzP2de5n5H85Q7MfqlCfxzQ09WhD/Tfy9+f9v4l//Iv7yj+YzL3WvoZTznu4nceqK1fYhKrHkxCqIn7nW/auV9x0E6p91MlCDzYnSYpkaNBd1exc9zCFezef3d/lA6uKvetm/KYP8VfxV/f1X8Z82jSSD2Nilhm33ZdlABmqw2WsQa9xQtnePvcsjiHPdWqIcL2t6C0e1rH7pPzRbTkxFwKL3MTkO5l8fxwabyEANFidKi1VqMDfTd225SiFUH+tvelmGl2SGVpN/VK3b895f1OCzODNkoIYFE6XFhLlIv7cYbz1+3A5DFp/red8jFlimv/Shpv/yIAc1WJsovcQeo83l/4Vu9kdTAMOyQo09Qg1ZsXWswS9ZqGEtltSgxxrO+jDiKp/f5KOTuaySt4XUow5fxxpAQkAN0TJTi2UHHQP6DMWr/xnLdg7k01ym7xNbSIfcpSVABkAN0WKpFm89uvhqjmYqNcTxbB6Vz/EyeZ/YsxolxbyGXIAaOGLtBnBLqFYFWnTyBKRAf6pi5i8LoAZqI1vZ5DUUS8E1FIAVualhEdZq8VjR3HHlJWAF1EBgrxb3xXePxv0aAC+gBoI0agHAHrJQg4vfvAQgbTJQg5vfvAQgbTJQg5vfvAQgbTJQg5tfrwIgbTJQg5vfvAQgbTJQA3oNAKwnAzU4+s1LAJImAzU4+s1LAJImBzW4+c1LAJImCzWsJY1aALAHqIEgjVoAsAeogSCNWgCwB6iBII1aALAHqIEgjVoAsAeogSCNWgCwB6iBII1aALAHqIEgjVoAsAeogSCNWgCwB6iBYPoTmABkiOVGZTccR1KvIuoXN1zrx7VcFkm9iqhf3HCtH9dyWST1KqJ+ccO1flzLZZHUq4j6xQ3X+nEtl0VSryLqFzdc68e1XBZJvYqoX9xwrR/Xclkk9SqifnHDtX5cy2WR1KuI+sUN1/pxLZdFUq8i6hc3XOvHtVwWSb2KqF/ccK0f13JZJPUqon5xw7V+XMtlkdSriPrFDdf6cS2XRVKvIuoXN1zrx7VcFkm9iqhf3HCtH9dyWST1KqJ+ccO1flzLBQAICtQAACCAGgAABFADAIAAagAAEEANAAACqAEAQAA1AAAIoAYAAAHUAAAggBoAAARQAwCAAGoAABBADQAAAqgBAEAANQAACKAGAAAB1AAAIIAaAAAEUAMAgABqAAAQQA0AAAKoAQBAADUAAAjSU0NViKJ60ws+1kXIl/oJTaCCWYL4jK5ifl10fKkfr8+PSzmscVLv7pFc8LEuQr7U78lr19oG8Rk9uyol+fkN9WP2+XEphy0eonjWz0I8iAUf6yLkW/2eogxZNCsQn1HzTMyti45v9WP2+aWmhkrcm/9v4kIs+FgXId/qd426ZprPz+gqTm3TSfLzM+rH7PNLTQ2leNUj/xoLPtZFyLf6XcU1XMEs8fkZiapum06Sn59RP2afX2pqaN/l4YDNWPCxLkK+1a8Ut7MoqjAFs8TnZ/ScLkzr8zPqx+zzi/ltpshbDYpTmJLZgfyMklZDbaiB1ecX89tMkbMahLjV9bvi1S1dSc5qYPb5xfw2U+SsBs076rN7OatBw+bzi/ltpiimb72x4GNdhHyrX0ta9TOeJfn5/XoWDibFsIYeAX5NR/BfwxmKVwIj3HT9WrjsWpsgP6PRGYrEPj8J1OCDizpvfBcVseBjXYR8q18h5PzbuJsO+Rm1jSXJz0/S94pYfX6pqSHn2ZCV3OHeelJNrJCfUdqzIfv6Mfv8UlNDfRxOAOl33FhwZHVyaBtf6vcu1MOYv1SJ+hkPkvz8hgfMPr/k1PBWV7aph/odNxYYD6PlV/2OTE59beWjfsaDJD+/elw/Pp9fcmoAANgAagAAEEANAAACqAEAQAA1AAAIoAYAAAHUAAAggBoAAARQAwCAAGoAABBADQAAAqgBAEAANQAACKAGAAAB1AAAIIAaAAAEUAMAgABqAAAQQA0AAAKoAQBAADUAAAigBgAAAdQAACCAGgAABFADAIAAagAAEEANAAACqAEAQAA1AAAIoAZgCfkD8KHLAOyBDxPYQfT/gSTAZwnsADUkBj5LYBHsTumAzxJYBLtTOuCzBPbA3pQQ+DDBcq7j3UVoTo/uuf8SAWfg0wSLeQpSDUJoN2BfSgp8nGApz+JDDepPJU7qmf8SAYfg8wQLuYoTrQb9V/cf/BcLOAKfJViIqOpvagCJgQ8VLOT54YDxAQVIC6gBLGduGPIZqDzAIVADWA6thhPMkCJQA1gOdUBxLO5hCgPcAjWA5Wg19Oci1H8PIV5hSwWcADWA5VBqqEtRBi0UcAPUAJZDnqF4YhgySaAGsBz65CW6DUkCNYDl0Gp4o9uQIlADWM7MbMgK3YYEgRqAJdTQpPi+BMQDPjhgB3XKom4vtFJPhyUgQvDBATv0t40V3X3ncSPZqMEnB+zwKQKoIWrwyQE7QA2JgU8O2KGbHimIJbjHS4TgIwOWUAMMIwd0S7CTxQg+NeAedBoiBJ8Z8AH2s+jARwacg6GGGMFnBgAggBoAAARQAwCAAGoAABBADQAAAqgBAEAANQAACKAGAAAB1AAAIIAaAAAEUAMAgABqAAAQQA0AAAKoAQBAADUAAAigBgAAAdQAACCAGgAABFADAIAAagAAEEANAAACqAEAQAA1AAAIoAYAAAHUAAAggBoAAARQAwCAAGoAABBADQAAAqgBAEAANQAACKAGAAAB1AAAIIAaAAAEUAMAgABqAAAQQA0AAAKoAQBAADUAAAj+P6Z70sawCcAvAAAAAElFTkSuQmCC", - "text/plain": [ - "Plot with title \"paste(\"Nonparametric Model, |\", rho, \"| = 1\")\"" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pdf(\"figures/401K-contour-npm-lower.pdf\", width = 6, height = 6)\n", "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", @@ -1124,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "5f1eab78", "metadata": { "execution": { @@ -1137,23 +852,7 @@ "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "Plot with title \"paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")\"" - ] - }, - "metadata": { - "image/png": { - "height": 525, - "width": 525 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pdf(\"figures/401K-contour-npm-lower-rho2.pdf\", width = 6, height = 6)\n", "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", @@ -1185,7 +884,7 @@ "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", - "version": "4.3.3" + "version": "4.6.1" } }, "nbformat": 4, diff --git a/data/data_BHP2_with_region.csv b/data/data_BHP2_with_region.csv new file mode 100644 index 00000000..454f0a42 --- /dev/null +++ b/data/data_BHP2_with_region.csv @@ -0,0 +1,3641 @@ 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ADML notebooks R and python notebooks for ADML chapter --- T-ADML/python-ADML-401k.ipynb | 874 ++++++++++++++++++++++ T-ADML/python-ADML-gas.ipynb | 1311 +++++++++++++++++++++++++++++++-- T-ADML/r-ADML-401k.irnb | 423 ++++++++++- T-ADML/r-ADML-gas.irnb | 1166 +++++++++++++++++++++++++++++ 4 files changed, 3664 insertions(+), 110 deletions(-) create mode 100644 T-ADML/python-ADML-401k.ipynb create mode 100644 T-ADML/r-ADML-gas.irnb diff --git a/T-ADML/python-ADML-401k.ipynb b/T-ADML/python-ADML-401k.ipynb new file mode 100644 index 00000000..96a2196a --- /dev/null +++ b/T-ADML/python-ADML-401k.ipynb @@ -0,0 +1,874 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c001", + "metadata": {}, + "source": [ + "# Empirical Example for Sensitivity Analysis: 401(k) Eligibility and Financial Wealth\n", + "\n", + "This is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n", + "*Long Story Short: Omitted Variable Bias in Causal Machine Learning*\n", + "\n", + "The original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__.\n", + "\n", + "This Python notebook uses `rpy2` to call the R `dml.sensemakr` package, which implements the DML estimation and CCNSS sensitivity analysis framework. The group ATE plots are reproduced in matplotlib; contour plots render directly from R via rpy2." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c002", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The rpy2.ipython extension is already loaded. To reload it, use:\n", + " %reload_ext rpy2.ipython\n" + ] + } + ], + "source": [ + "try:\n", + " import rpy2\n", + "except ImportError:\n", + " !pip install rpy2\n", + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c003", + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "repos <- c(CRAN = \"https://cloud.r-project.org\")\n", + "if (!requireNamespace(\"remotes\", quietly=TRUE)) install.packages(\"remotes\", repos=repos)\n", + "if (!requireNamespace(\"sensemakr\", quietly=TRUE)) install.packages(\"sensemakr\", repos=repos)\n", + "if (!requireNamespace(\"ggplot2\", quietly=TRUE)) install.packages(\"ggplot2\", repos=repos)\n", + "if (!requireNamespace(\"ranger\", quietly=TRUE)) install.packages(\"ranger\", repos=repos)\n", + "if (!requireNamespace(\"dml.sensemakr\", quietly=TRUE)) {\n", + " remotes::install_github(\"carloscinelli/dml.sensemakr\", upgrade=\"never\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c004", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "households: 9915 covariates: 8 eligible: 37.1% \n", + "dml.sensemakr 0.2.2 \n" + ] + } + ], + "source": [ + "%%R\n", + "suppressMessages({\n", + " library(sensemakr)\n", + " library(dml.sensemakr)\n", + " library(ggplot2)\n", + "})\n", + "options(scipen=99, repr.plot.width=5, repr.plot.height=5, repr.plot.res=150)\n", + "\n", + "data(\"pension\")\n", + "\n", + "y <- pension$net_tfa\n", + "d <- pension$e401\n", + "x <- model.matrix(~ -1 + age + inc + educ + fsize + marr + twoearn + pira + hown,\n", + " data=pension)\n", + "\n", + "cat(\"households:\", nrow(x), \" covariates:\", ncol(x),\n", + " \" eligible:\", sprintf(\"%.1f%%\", 100*mean(d)), \"\\n\")\n", + "cat(\"dml.sensemakr\", as.character(packageVersion(\"dml.sensemakr\")), \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "c005", + "metadata": {}, + "source": [ + "## Setting\n", + "\n", + "The outcome $Y$ is net financial assets and the treatment $D$ is eligibility to enroll in a 401(k) plan. The observed covariates $X^o$ include income, age, education, family size, marital status, two-earner status, IRA participation, and home ownership.\n", + "\n", + "The threat is that unobserved firm characteristics $A$ may affect both eligibility and financial assets through employer matching policies. Conditional ignorability holds given the full $X = (X^o, A)$, while the data identify only the short parameter $\\theta_s$. The task is to bound its distance from the causal target $\\theta$." + ] + }, + { + "cell_type": "markdown", + "id": "c006", + "metadata": {}, + "source": [ + "## Short Estimates Under Conditional Ignorability\n", + "\n", + "### Partially Linear Model\n", + "\n", + "The first specification is the partially linear model $Y = \\theta_s D + f_s(X^o) + \\varepsilon$, with nuisance functions modeled by random forests, cross-fitted over five folds and repeated five times." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c007", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Partially Linear \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 26.474%), treatment (ranger, R2 = 11.599%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 9114 1323 6.888 0.00000000000567 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + } + ], + "source": [ + "%%R\n", + "set.seed(1)\n", + "dml.401k.plm <- dml(y, d, x, model=\"plm\", cf.folds=5, cf.reps=5, verbose=FALSE)\n", + "summary(dml.401k.plm)" + ] + }, + { + "cell_type": "markdown", + "id": "c008", + "metadata": {}, + "source": [ + "### Nonparametric Model, with Income Groups\n", + "\n", + "The second specification drops the additive structure and targets $\\theta_s = \\mathrm{E}\\{g_s(1,X^o) - g_s(0,X^o)\\}$ with the same nuisance learners." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c009", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Nonparametric \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 25.572%), treatment (ranger, R2 = 11.48%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 8198 1196 6.852 0.00000000000726 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Group Average Treatment Effect: \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "g.ate.q1 4646.2 794.2 5.850 0.00000000492 ***\n", + "g.ate.q2 2729.4 1296.7 2.105 0.035305 * \n", + "g.ate.q3 6854.3 1838.0 3.729 0.000192 ***\n", + "g.ate.q4 18406.6 4061.6 4.532 0.00000584674 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + } + ], + "source": [ + "%%R\n", + "g1 <- cut(x[,\"inc\"],\n", + " quantile(x[,\"inc\"], c(0,.25,.5,.75,1), na.rm=TRUE),\n", + " labels=c(\"q1\",\"q2\",\"q3\",\"q4\"), include.lowest=TRUE)\n", + "\n", + "set.seed(1)\n", + "dml.401k.npm <- dml(y, d, x, groups=g1, model=\"npm\",\n", + " cf.folds=5, cf.reps=5, verbose=FALSE)\n", + "summary(dml.401k.npm)" + ] + }, + { + "cell_type": "markdown", + "id": "c010", + "metadata": {}, + "source": [ + "## Group Effects by Income Quartile" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c011", + "metadata": {}, + "outputs": [], + "source": [ + "%%R -o group_est -o lwr_ci -o upr_ci\n", + "group.names <- setdiff(names(coef(dml.401k.npm)), \"ate\")\n", + "group_est <- coef(dml.401k.npm)[group.names]\n", + "group_cis <- confint(dml.401k.npm)[group.names, ]\n", + "lwr_ci <- group_cis[, 1]\n", + "upr_ci <- group_cis[, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c012", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "groups = np.arange(1, 5)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "ax.plot(groups, group_est, color=\"black\")\n", + "ax.fill_between(groups, lwr_ci, upr_ci, alpha=0.15, color=\"blue\", label=\"95% CI\")\n", + "ax.set_xticks(groups)\n", + "ax.set_xlabel(\"Income Groups by Quartiles\")\n", + "ax.set_ylabel(\"ATE\")\n", + "ax.set_title(\"Under conditional ignorability\")\n", + "ax.legend()\n", + "ax.grid(color=\"0.85\", linewidth=0.6)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c013", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RV_alpha (alpha=0.05, threshold theta=0)\n", + " partially linear: 5.6%\n", + " nonparametric : 4.6%\n" + ] + } + ], + "source": [ + "%%R\n", + "rv.plm <- robustness_value(dml.401k.plm)\n", + "rv.npm <- robustness_value(dml.401k.npm)\n", + "cat(sprintf(\n", + " \"RV_alpha (alpha=0.05, threshold theta=0)\\n partially linear: %.1f%%\\n nonparametric : %.1f%%\\n\",\n", + " 100*rv.plm[\"ate\"], 100*rv.npm[\"ate\"]))" + ] + }, + { + "cell_type": "markdown", + "id": "c014", + "metadata": {}, + "source": [ + "## The Main Confounding Scenario\n", + "\n", + "The main scenario assumes $C_Y^2 = 0.04$ (outcome variation from unobserved firm characteristics equivalent to three years of maximum employer match), $q_\\alpha \\approx 0.03$ on the treatment side, and $|\\rho| = 1$ (worst-case alignment)." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c015", + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "cf.y <- 0.04\n", + "cf.d <- 0.03\n", + "rho2 <- 1\n", + "\n", + "bounds.plm <- dml_bounds(dml.401k.plm, cf.y=cf.y, cf.d=cf.d, rho2=rho2)\n", + "bounds.npm <- dml_bounds(dml.401k.npm, cf.y=cf.y, cf.d=cf.d, rho2=rho2)\n", + "cb.plm <- confidence_bounds(dml.401k.plm, cf.y=cf.y, cf.d=cf.d, rho2=rho2)\n", + "cb.npm <- confidence_bounds(dml.401k.npm, cf.y=cf.y, cf.d=cf.d, rho2=rho2)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c016", + "metadata": {}, + "outputs": [], + "source": [ + "%%R -o plm_ts -o plm_bb -o plm_tm -o plm_tp -o plm_cbl -o plm_cbu -o npm_ts -o npm_bb -o npm_tm -o npm_tp -o npm_cbl -o npm_cbu\n", + "plm_b <- coef(bounds.plm)[, \"ate\"]\n", + "plm_ts <- plm_b[\"theta.s\"]; plm_bb <- plm_b[\"bias.bound\"]\n", + "plm_tm <- plm_b[\"theta.m\"]; plm_tp <- plm_b[\"theta.p\"]\n", + "plm_cbl <- cb.plm[\"ate\", 1]; plm_cbu <- cb.plm[\"ate\", 2]\n", + "\n", + "npm_b <- coef(bounds.npm)[, \"ate\"]\n", + "npm_ts <- npm_b[\"theta.s\"]; npm_bb <- npm_b[\"bias.bound\"]\n", + "npm_tm <- npm_b[\"theta.m\"]; npm_tp <- npm_b[\"theta.p\"]\n", + "npm_cbl <- cb.npm[\"ate\", 1]; npm_cbu <- cb.npm[\"ate\", 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c017", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Short ATEBias boundATE bounds95% conf. bounds
Partially linear$9,114$4,201[$4,918, $13,311][$2,725, $15,573]
Nonparametric$8,198$4,529[$3,644, $12,752][$1,618, $14,805]
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" + ], + "text/plain": [ + " Short ATE Bias bound ATE bounds 95% conf. bounds\n", + "Partially linear $9,114 $4,201 [$4,918, $13,311] [$2,725, $15,573]\n", + "Nonparametric $8,198 $4,529 [$3,644, $12,752] [$1,618, $14,805]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "def fmt(v): return f\"${v:,.0f}\"\n", + "\n", + "table = pd.DataFrame({\n", + " \"Short ATE\": [fmt(plm_ts[0]), fmt(npm_ts[0])],\n", + " \"Bias bound\": [fmt(plm_bb[0]), fmt(npm_bb[0])],\n", + " \"ATE bounds\": [f\"[{fmt(plm_tm[0])}, {fmt(plm_tp[0])}]\",\n", + " f\"[{fmt(npm_tm[0])}, {fmt(npm_tp[0])}]\"],\n", + " \"95% conf. bounds\": [f\"[{fmt(plm_cbl[0])}, {fmt(plm_cbu[0])}]\",\n", + " f\"[{fmt(npm_cbl[0])}, {fmt(npm_cbu[0])}]\"],\n", + "}, index=[\"Partially linear\", \"Nonparametric\"])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "id": "c018", + "metadata": {}, + "source": [ + "### Group Effects Under the Scenario\n", + "\n", + "The same income groups, now with sensitivity bounds (orange) and 95% confidence bounds (blue)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "c019", + "metadata": {}, + "outputs": [], + "source": [ + "%%R -o lwr_bound -o upr_bound -o lwr_cbound -o upr_cbound\n", + "bds <- confidence_bounds(dml.401k.npm, cf.y=cf.y, cf.d=cf.d, rho2=rho2, level=0)\n", + "cbds <- confidence_bounds(dml.401k.npm, cf.y=cf.y, cf.d=cf.d, rho2=rho2, level=0.95)\n", + "lwr_bound <- bds[group.names, 1]\n", + "upr_bound <- bds[group.names, 2]\n", + "lwr_cbound <- cbds[group.names, 1]\n", + "upr_cbound <- cbds[group.names, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c020", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "\n", + "# Left: under ignorability\n", + "ax = axes[0]\n", + "ax.plot(groups, group_est, color=\"black\")\n", + "ax.fill_between(groups, lwr_ci, upr_ci, alpha=0.15, color=\"blue\")\n", + "ax.set_xticks(groups); ax.set_xlabel(\"Income Groups by Quartiles\")\n", + "ax.set_ylabel(\"ATE\"); ax.set_title(\"Under conditional ignorability\")\n", + "ax.grid(color=\"0.85\", linewidth=0.6)\n", + "\n", + "# Right: with confounding\n", + "ax = axes[1]\n", + "ax.plot(groups, group_est, color=\"black\")\n", + "ax.fill_between(groups, lwr_bound, upr_bound, alpha=0.15, color=\"red\", label=\"Bounds\")\n", + "ax.fill_between(groups, lwr_cbound, upr_cbound, alpha=0.10, color=\"blue\", label=\"95% conf. bounds\")\n", + "ax.set_xticks(groups); ax.set_xlabel(\"Income Groups by Quartiles\")\n", + "ax.set_ylabel(\"ATE\"); ax.set_title(\"With unobserved confounding\")\n", + "ax.legend(); ax.grid(color=\"0.85\", linewidth=0.6)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c021", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "ax.plot(groups, group_est, color=\"black\", label=\"Point estimate\")\n", + "ax.plot(groups, lwr_ci, color=\"black\", linestyle=\"--\")\n", + "ax.plot(groups, upr_ci, color=\"black\", linestyle=\"--\")\n", + "ax.fill_between(groups, lwr_bound, upr_bound, alpha=0.15, color=\"blue\", label=\"Bounds\")\n", + "ax.fill_between(groups, lwr_cbound, upr_cbound, alpha=0.05, color=\"blue\")\n", + "ax.plot(groups, lwr_bound, color=\"blue\")\n", + "ax.plot(groups, upr_bound, color=\"blue\")\n", + "ax.plot(groups, lwr_cbound, color=\"blue\", linestyle=\"--\")\n", + "ax.plot(groups, upr_cbound, color=\"blue\", linestyle=\"--\")\n", + "ax.set_xticks(groups)\n", + "ax.set_xlabel(\"Income Groups by Quartiles\")\n", + "ax.set_ylabel(\"ATE\")\n", + "ax.set_title(\"Confidence and Sensitivity Bounds\")\n", + "ax.legend(); ax.grid(color=\"0.85\", linewidth=0.6)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c022", + "metadata": {}, + "source": [ + "## Benchmarking\n", + "\n", + "Rather than posit $C_Y$ and $C_D$ abstractly, we calibrate them against observed covariates. Dropping each covariate, refitting, and reading off the gains provides a reference scale." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "c023", + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "set.seed(2)\n", + "invisible(capture.output(\n", + " bench.plm <- dml_benchmark(dml.401k.plm,\n", + " benchmark_covariates=c(\"inc\",\"pira\",\"twoearn\"))\n", + "))\n", + "out <- summary(bench.plm)$benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c024", + "metadata": {}, + "outputs": [], + "source": [ + "%%R -o bench_plm_r\n", + "bench_plm_r <- as.matrix(round(out[, c(\"gain.Y\",\"gain.D\",\"rho\")], 4))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c025", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PLM benchmarks:\n" + ] + }, + { + "data": { + "text/html": [ + "
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gain.Ygain.Drho
inc0.13660.0502-0.3372
pira0.04870.0081-0.1317
twoearn0.03990.01120.2589
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" + ], + "text/plain": [ + " gain.Y gain.D rho\n", + "inc 0.1366 0.0502 -0.3372\n", + "pira 0.0487 0.0081 -0.1317\n", + "twoearn 0.0399 0.0112 0.2589" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bench_df = pd.DataFrame(bench_plm_r,\n", + " columns=[\"gain.Y\", \"gain.D\", \"rho\"],\n", + " index=[\"inc\", \"pira\", \"twoearn\"])\n", + "print(\"PLM benchmarks:\")\n", + "bench_df" + ] + }, + { + "cell_type": "markdown", + "id": "c026", + "metadata": {}, + "source": [ + "## Contour Plots\n", + "\n", + "Contour plots of the lower 95% confidence bound on the ATE as a function of $(q_\\alpha, C_Y^2)$. Benchmark points from the table above are overlaid." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "c027", + "metadata": {}, + "outputs": [], + "source": [ + "%%R\n", + "ovb_contour_without_origin_label <- getS3method(\"ovb_contour_plot\",\"dml\")\n", + "plot_body <- as.list(body(ovb_contour_without_origin_label))\n", + "is_origin_label <- vapply(plot_body, function(expr)\n", + " is.call(expr) && identical(expr[[1L]], as.name(\"text\")) &&\n", + " \"label.unadjusted\" %in% all.names(expr), logical(1))\n", + "stopifnot(sum(is_origin_label) == 1L)\n", + "plot_body[is_origin_label] <- list(quote(invisible(NULL)))\n", + "body(ovb_contour_without_origin_label) <- as.call(plot_body)\n", + "\n", + "bumps.plm <- list(pira=c(0.010,0.009), twoearn=c(0.020,-0.002),\n", + " inc=c(0.010,0.009), inc4=c(0.015,-0.005))\n", + "bumps.npm <- list(pira=c(0.005,0.009), twoearn=c(0.020,0.005),\n", + " inc=c(-0.010,0.009), inc4=c(0.015,0.000))\n", + "\n", + "contour_panel <- function(fit, bench_out, rho2, title, bumps=bumps.plm) {\n", + " ovb_contour_without_origin_label(fit, which.bound=\"lwr\", level=0.95,\n", + " cf.y=NULL, rho2=rho2, col.contour=\"blue\",\n", + " label.bump.x=0.015, label.bump.y=0.003, lim.x=0.16)\n", + " title(main=title)\n", + " add_bound <- function(cfd, cfy, label, bx, by, ...) {\n", + " sensemakr::add_bound_to_contour(r2dz.x=cfd, r2yz.dx=cfy,\n", + " bound_label=label,\n", + " bound_value=confidence_bounds(fit, cf.y=cfy, cf.d=cfd, rho2=rho2,\n", + " level=0.95, combine.method=\"median\")[\"ate\",\"lwr\"],\n", + " label.bump.x=bx, label.bump.y=by, ...)\n", + " }\n", + " add_bound(0.03, 0.04, \"Max Match\", 0.015, 0.003, point.pch=25, point.bg=\"red\")\n", + " add_bound(bench_out[\"pira\",\"gain.D\"], bench_out[\"pira\",\"gain.Y\"], \"1 x Part. in IRA\",\n", + " bumps$pira[1], bumps$pira[2])\n", + " add_bound(bench_out[\"twoearn\",\"gain.D\"],bench_out[\"twoearn\",\"gain.Y\"],\"1 x Two Earners\",\n", + " bumps$twoearn[1],bumps$twoearn[2])\n", + " add_bound(bench_out[\"inc\",\"gain.D\"], bench_out[\"inc\",\"gain.Y\"], \"1 x Income\",\n", + " bumps$inc[1], bumps$inc[2])\n", + " add_bound(bench_out[\"inc\",\"gain.D\"]*0.25,bench_out[\"inc\",\"gain.Y\"]*0.25,\"1/4 x Income\",\n", + " bumps$inc4[1], bumps$inc4[2])\n", + " add_bound(0, 0, \"Unadjusted\", 0.015, 0.003, point.pch=NA)\n", + " invisible(NULL)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c028", + "metadata": {}, + "source": [ + "### Partially Linear Model" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "c029", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "contour_panel(dml.401k.plm, out, rho2=(1/2)^2,\n", + " title=expression(paste(\"Partially Linear Model, |\",rho,\"| = 1/2\")))" + ] + }, + { + "cell_type": "markdown", + "id": "c031", + "metadata": {}, + "source": [ + "### Nonparametric Model\n", + "\n", + "The nonparametric model requires separate benchmarks; `gain.D` here measures a relative gain in propensity-score precision." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c032", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " gain.Y gain.D rho\n", + "inc 0.1315 0.1288 -0.218\n", + "pira 0.0267 0.0000 0.000\n", + "twoearn 0.0113 0.0000 0.000\n" + ] + } + ], + "source": [ + "%%R\n", + "set.seed(2)\n", + "invisible(capture.output(\n", + " bench.npm <- dml_benchmark(dml.401k.npm,\n", + " benchmark_covariates=c(\"inc\",\"pira\",\"twoearn\"))\n", + "))\n", + "out.npm <- summary(bench.npm)$benchmarks\n", + "round(out.npm[, c(\"gain.Y\",\"gain.D\",\"rho\")], 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "c033", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "contour_panel(dml.401k.npm, out.npm, rho2=(1/2)^2, bumps=bumps.npm,\n", + " title=expression(paste(\"Nonparametric Model, |\",rho,\"| = 1/2\")))" + ] + }, + { + "cell_type": "markdown", + "id": "c035", + "metadata": {}, + "source": [ + "The nonparametric contours are slightly more conservative, but the message is similar. Under perfect adversarial alignment, income-strength confounding can overturn the finding; the contextual matching scenario cannot. At $|\\rho| = 1/2$, the lower confidence endpoint for the main scenario remains comfortably positive." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/T-ADML/python-ADML-gas.ipynb b/T-ADML/python-ADML-gas.ipynb index 3f5a1b0e..98808782 100644 --- a/T-ADML/python-ADML-gas.ipynb +++ b/T-ADML/python-ADML-gas.ipynb @@ -4,270 +4,1439 @@ "cell_type": "markdown", "id": "c001", "metadata": {}, - "source": "# Empirical Example for Sensitivity Analysis: Gasoline Demand\n\nThis is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n*Long Story Short: Omitted Variable Bias in Causal Machine Learning* \n\nThe original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__." + "source": [ + "# Empirical Example for Sensitivity Analysis: Gasoline Demand\n", + "\n", + "This is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n", + "*Long Story Short: Omitted Variable Bias in Causal Machine Learning* \n", + "\n", + "The original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "c002", "metadata": {}, "outputs": [], - "source": "!pip install econml" + "source": [ + "try:\n", + " import econml\n", + "except ImportError:\n", + " !pip install econml" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "c003", "metadata": {}, "outputs": [], - "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy\nimport scipy.special\nimport scipy.stats\nimport joblib\nfrom statsmodels.api import OLS\nfrom sklearn.model_selection import StratifiedGroupKFold, KFold\nimport pandas as pd\nimport math\nfrom sklearn.base import clone, BaseEstimator, TransformerMixin\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import StandardScaler, PolynomialFeatures\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import LassoCV, LogisticRegressionCV, LinearRegression, Lasso, LogisticRegression\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\nfrom scipy.optimize import fmin_l_bfgs_b\nfrom econml.dml import LinearDML\nfrom econml.sklearn_extensions.model_selection import GridSearchCVList\nfrom econml.utilities import cross_product\n# BaseGRF is an internal econml API; may need updating across econml versions\nfrom econml.grf._base_grf import BaseGRF" + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "import scipy.special\n", + "import scipy.stats\n", + "import joblib\n", + "from statsmodels.api import OLS\n", + "from sklearn.model_selection import StratifiedGroupKFold, KFold\n", + "import pandas as pd\n", + "import math\n", + "from sklearn.base import clone, BaseEstimator, TransformerMixin\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LassoCV, LogisticRegressionCV, LinearRegression, Lasso, LogisticRegression\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n", + "from scipy.optimize import fmin_l_bfgs_b\n", + "from econml.dml import LinearDML\n", + "from econml.sklearn_extensions.model_selection import GridSearchCVList\n", + "from econml.utilities import cross_product\n", + "# BaseGRF is an internal econml API; may need updating across econml versions\n", + "from econml.grf._base_grf import BaseGRF" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "c004", "metadata": {}, "outputs": [], - "source": "import warnings\nfrom sklearn.exceptions import ConvergenceWarning\n\nwarnings.filterwarnings('ignore', category=ConvergenceWarning)" + "source": [ + "import warnings\n", + "from sklearn.exceptions import ConvergenceWarning\n", + "\n", + "warnings.filterwarnings('ignore', category=ConvergenceWarning)" + ] }, { "cell_type": "markdown", "id": "c005", "metadata": {}, - "source": "## Module Definitions\n\nThe following cells define helper classes and functions used by this notebook.\nThey are inlined here so the notebook runs in Google Colab without requiring local files." + "source": [ + "## Module Definitions\n", + "\n", + "The following cells define helper classes and functions used by this notebook.\n", + "They are inlined here so the notebook runs in Google Colab without requiring local files." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "c006", "metadata": {}, "outputs": [], - "source": "def mean_ci(data, confidence=0.95):\n a = 1.0 * np.array(data).flatten()\n n = len(a)\n m, se = np.mean(a), scipy.stats.sem(a)\n h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n return m, se, m-h, m+h" + "source": [ + "def mean_ci(data, confidence=0.95):\n", + " a = 1.0 * np.array(data).flatten()\n", + " n = len(a)\n", + " m, se = np.mean(a), scipy.stats.sem(a)\n", + " h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n", + " return m, se, m-h, m+h" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "c007", "metadata": {}, "outputs": [], - "source": "class Featurizer(TransformerMixin, BaseEstimator):\n ''' Creates features of the form X[:, 1:] * poly(X[:, 0])\n i.e. some polynomial of the treatment, multiplied by some other feature.\n '''\n\n def __init__(self, *, degree=1, normalize=True):\n self.degree = degree\n self.normalize = normalize\n\n def _transform(self, X):\n Tfeats = PolynomialFeatures(degree=self.degree, include_bias=True).fit_transform(X[:, [0]])\n feats = cross_product(Tfeats, X[:, 1:])\n return feats\n\n def fit(self, X, y=None):\n if self.normalize:\n self.scaler_ = StandardScaler().fit(self._transform(X))\n return self\n\n def transform(self, X):\n if self.normalize:\n return self.scaler_.transform(self._transform(X))\n return self._transform(X)\n\n\nclass SieveRR(BaseEstimator):\n ''' Linear sieve representation learning with some feature map and l2 penalty. '''\n\n def __init__(self, *, featurizer, moment_fn, alpha=0):\n self.alpha = alpha\n self.featurizer = featurizer\n self.moment_fn = moment_fn\n\n def fit(self, X):\n self.featurizer_ = clone(self.featurizer).fit(X)\n n_samples = X.shape[0]\n Xfeats = self.featurizer_.transform(X)\n Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats])\n n_feats = Xfeats.shape[1]\n Mfeats = np.zeros((n_samples, n_feats))\n for t in np.arange(n_feats):\n if t == 0:\n feat_fn = lambda x: np.ones(x.shape[0])\n else:\n feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n Mfeats[:, t] = self.moment_fn(X, feat_fn)\n Sigma = Xfeats.T @ Xfeats / n_samples\n reg = self.alpha * np.eye(n_feats)\n reg[0, 0] = 0\n Sigma += reg\n params = np.linalg.pinv(Sigma) @ np.mean(Mfeats, axis=0)\n self.coef_ = params[1:]\n self.intercept_ = params[0]\n return self\n\n def predict(self, X):\n return self.featurizer_.transform(X) @ self.coef_ + self.intercept_\n\n\nclass SparseSieveRR(BaseEstimator):\n ''' Linear sieve representation learning with l1+l2 (elastic net) penalty. '''\n\n def __init__(self, *, featurizer, moment_fn, alpha=0.0, l1_ratio=1.0, tol=1e-4):\n self.featurizer = featurizer\n self.moment_fn = moment_fn\n self.alpha = alpha\n self.l1_ratio = l1_ratio\n self.tol = tol\n\n def fit(self, X):\n self.featurizer_ = clone(self.featurizer).fit(X)\n n_samples = X.shape[0]\n Xfeats = self.featurizer_.transform(X)\n Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats])\n n_feats = Xfeats.shape[1]\n Mfeats = np.zeros((n_samples, n_feats))\n for t in np.arange(n_feats):\n if t == 0:\n feat_fn = lambda x: np.ones(x.shape[0])\n else:\n feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n Mfeats[:, t] = self.moment_fn(X, feat_fn)\n Sigma = Xfeats.T @ Xfeats / n_samples\n mfeats = np.mean(Mfeats, axis=0)\n\n alpha_l1 = self.alpha * self.l1_ratio\n alpha_l2 = self.alpha * (1 - self.l1_ratio)\n\n def loss_and_jac(extended_coef):\n coef = extended_coef[:n_feats] - extended_coef[n_feats:]\n loss = - 2 * mfeats @ coef + coef.T @ Sigma @ coef\n loss += alpha_l1 * np.sum(extended_coef[1:n_feats])\n loss += alpha_l1 * np.sum(extended_coef[n_feats + 1:])\n loss += 0.5 * alpha_l2 * np.sum(extended_coef[1:n_feats]**2)\n loss += 0.5 * alpha_l2 * np.sum(extended_coef[n_feats + 1:]**2)\n grad = 2 * (Sigma @ coef - mfeats)\n jac = np.concatenate([grad, -grad])\n jac[1:n_feats] += alpha_l1 + alpha_l2 * extended_coef[1:n_feats]\n jac[n_feats + 1:] += alpha_l1 + alpha_l2 * extended_coef[n_feats + 1:]\n return loss, jac\n\n w, _, _ = fmin_l_bfgs_b(loss_and_jac, np.zeros(2 * n_feats),\n bounds=[(0, None)] * 2 * n_feats,\n pgtol=self.tol)\n params = w[:n_feats] - w[n_feats:]\n self.coef_ = params[1:]\n self.intercept_ = params[0]\n return self\n\n def predict(self, X):\n return self.featurizer_.transform(X) @ self.coef_ + self.intercept_" + "source": [ + "class Featurizer(TransformerMixin, BaseEstimator):\n", + " ''' Creates features of the form X[:, 1:] * poly(X[:, 0])\n", + " i.e. some polynomial of the treatment, multiplied by some other feature.\n", + " '''\n", + "\n", + " def __init__(self, *, degree=1, normalize=True):\n", + " self.degree = degree\n", + " self.normalize = normalize\n", + "\n", + " def _transform(self, X):\n", + " Tfeats = PolynomialFeatures(degree=self.degree, include_bias=True).fit_transform(X[:, [0]])\n", + " feats = cross_product(Tfeats, X[:, 1:])\n", + " return feats\n", + "\n", + " def fit(self, X, y=None):\n", + " if self.normalize:\n", + " self.scaler_ = StandardScaler().fit(self._transform(X))\n", + " return self\n", + "\n", + " def transform(self, X):\n", + " if self.normalize:\n", + " return self.scaler_.transform(self._transform(X))\n", + " return self._transform(X)\n", + "\n", + "\n", + "class SieveRR(BaseEstimator):\n", + " ''' Linear sieve representation learning with some feature map and l2 penalty. '''\n", + "\n", + " def __init__(self, *, featurizer, moment_fn, alpha=0):\n", + " self.alpha = alpha\n", + " self.featurizer = featurizer\n", + " self.moment_fn = moment_fn\n", + "\n", + " def fit(self, X):\n", + " self.featurizer_ = clone(self.featurizer).fit(X)\n", + " n_samples = X.shape[0]\n", + " Xfeats = self.featurizer_.transform(X)\n", + " Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats])\n", + " n_feats = Xfeats.shape[1]\n", + " Mfeats = np.zeros((n_samples, n_feats))\n", + " for t in np.arange(n_feats):\n", + " if t == 0:\n", + " feat_fn = lambda x: np.ones(x.shape[0])\n", + " else:\n", + " feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n", + " Mfeats[:, t] = self.moment_fn(X, feat_fn)\n", + " Sigma = Xfeats.T @ Xfeats / n_samples\n", + " reg = self.alpha * np.eye(n_feats)\n", + " reg[0, 0] = 0\n", + " Sigma += reg\n", + " params = np.linalg.pinv(Sigma) @ np.mean(Mfeats, axis=0)\n", + " self.coef_ = params[1:]\n", + " self.intercept_ = params[0]\n", + " return self\n", + "\n", + " def predict(self, X):\n", + " return self.featurizer_.transform(X) @ self.coef_ + self.intercept_\n", + "\n", + "\n", + "class SparseSieveRR(BaseEstimator):\n", + " ''' Linear sieve representation learning with l1+l2 (elastic net) penalty. '''\n", + "\n", + " def __init__(self, *, featurizer, moment_fn, alpha=0.0, l1_ratio=1.0, tol=1e-4):\n", + " self.featurizer = featurizer\n", + " self.moment_fn = moment_fn\n", + " self.alpha = alpha\n", + " self.l1_ratio = l1_ratio\n", + " self.tol = tol\n", + "\n", + " def fit(self, X):\n", + " self.featurizer_ = clone(self.featurizer).fit(X)\n", + " n_samples = X.shape[0]\n", + " Xfeats = self.featurizer_.transform(X)\n", + " Xfeats = np.hstack([np.ones((Xfeats.shape[0], 1)), Xfeats])\n", + " n_feats = Xfeats.shape[1]\n", + " Mfeats = np.zeros((n_samples, n_feats))\n", + " for t in np.arange(n_feats):\n", + " if t == 0:\n", + " feat_fn = lambda x: np.ones(x.shape[0])\n", + " else:\n", + " feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n", + " Mfeats[:, t] = self.moment_fn(X, feat_fn)\n", + " Sigma = Xfeats.T @ Xfeats / n_samples\n", + " mfeats = np.mean(Mfeats, axis=0)\n", + "\n", + " alpha_l1 = self.alpha * self.l1_ratio\n", + " alpha_l2 = self.alpha * (1 - self.l1_ratio)\n", + "\n", + " def loss_and_jac(extended_coef):\n", + " coef = extended_coef[:n_feats] - extended_coef[n_feats:]\n", + " loss = - 2 * mfeats @ coef + coef.T @ Sigma @ coef\n", + " loss += alpha_l1 * np.sum(extended_coef[1:n_feats])\n", + " loss += alpha_l1 * np.sum(extended_coef[n_feats + 1:])\n", + " loss += 0.5 * alpha_l2 * np.sum(extended_coef[1:n_feats]**2)\n", + " loss += 0.5 * alpha_l2 * np.sum(extended_coef[n_feats + 1:]**2)\n", + " grad = 2 * (Sigma @ coef - mfeats)\n", + " jac = np.concatenate([grad, -grad])\n", + " jac[1:n_feats] += alpha_l1 + alpha_l2 * extended_coef[1:n_feats]\n", + " jac[n_feats + 1:] += alpha_l1 + alpha_l2 * extended_coef[n_feats + 1:]\n", + " return loss, jac\n", + "\n", + " w, _, _ = fmin_l_bfgs_b(loss_and_jac, np.zeros(2 * n_feats),\n", + " bounds=[(0, None)] * 2 * n_feats,\n", + " pgtol=self.tol)\n", + " params = w[:n_feats] - w[n_feats:]\n", + " self.coef_ = params[1:]\n", + " self.intercept_ = params[0]\n", + " return self\n", + "\n", + " def predict(self, X):\n", + " return self.featurizer_.transform(X) @ self.coef_ + self.intercept_" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "c008", "metadata": {}, "outputs": [], - "source": "def poly_feature_fns(degree):\n def poly(d, sign=1.0):\n return lambda x: sign * x[:, [0]]**d\n return [poly(t) for t in np.arange(0, degree + 1)]\n\n\nclass RFrr(BaseGRF):\n \"\"\"\n A forest that estimates a Riesz representer of a linear functional.\n Minimizes the Riesz loss E[(phi(T,X)' alpha(x))^2 - 2 * | X=x].\n\n Parameters\n ----------\n riesz_feature_fns : list of lambda\n Features phi(T,X) used to approximate the Riesz representer locally.\n moment_fn : lambda (x, fn) -> float\n The linear moment function m([T;X], g).\n l2 : float, default=0.01\n L2 penalty on the local covariance matrix (no penalty on intercept).\n \"\"\"\n\n def __init__(self, *, riesz_feature_fns, moment_fn, l2=0.01,\n n_estimators=100, criterion='mse', max_depth=None,\n min_samples_split=10, min_samples_leaf=5,\n min_weight_fraction_leaf=0., min_var_fraction_leaf=None,\n min_var_leaf_on_val=False, max_features='auto',\n min_impurity_decrease=0., max_samples=.45,\n min_balancedness_tol=.45, honest=True, inference=True,\n fit_intercept=True, subforest_size=4, n_jobs=-1,\n random_state=None, verbose=0, warm_start=False):\n self.riesz_feature_fns = riesz_feature_fns\n self.moment_fn = moment_fn\n self.l2 = l2\n super().__init__(n_estimators=n_estimators, criterion=criterion,\n max_depth=max_depth, min_samples_split=min_samples_split,\n min_samples_leaf=min_samples_leaf,\n min_weight_fraction_leaf=min_weight_fraction_leaf,\n min_var_fraction_leaf=min_var_fraction_leaf,\n min_var_leaf_on_val=min_var_leaf_on_val,\n max_features=max_features,\n min_impurity_decrease=min_impurity_decrease,\n max_samples=max_samples,\n min_balancedness_tol=min_balancedness_tol,\n honest=honest, inference=inference,\n fit_intercept=False, subforest_size=subforest_size,\n n_jobs=n_jobs, random_state=random_state,\n verbose=verbose, warm_start=warm_start)\n\n def _get_alpha_and_pointJ(self, X, T, y):\n n_riesz_feats = len(self.riesz_feature_fns)\n TX = np.hstack([T, X])\n riesz_feats = np.hstack([feat_fn(TX) for feat_fn in self.riesz_feature_fns])\n mfeats = np.hstack([self.moment_fn(TX, feat_fn) for feat_fn in self.riesz_feature_fns])\n alpha = np.zeros((X.shape[0], n_riesz_feats))\n alpha[:, :n_riesz_feats] = mfeats\n riesz_cov_matrix = cross_product(riesz_feats, riesz_feats).reshape(\n (X.shape[0], n_riesz_feats, n_riesz_feats))\n penalty = self.l2 * np.eye(n_riesz_feats)\n penalty[0, 0] = 0\n pointJ = riesz_cov_matrix + penalty\n return alpha, pointJ.reshape((X.shape[0], -1))\n\n def _get_n_outputs_decomposition(self, X, T, y):\n n_relevant_outputs = len(self.riesz_feature_fns)\n return n_relevant_outputs, n_relevant_outputs\n\n def _translate(self, point, TX):\n riesz_feats = np.hstack([feat_fn(TX) for feat_fn in self.riesz_feature_fns])\n return np.sum(point[:, :riesz_feats.shape[1]] * riesz_feats, axis=1)\n\n def fit(self, X):\n return super().fit(X[:, 1:], X[:, [0]], X[:, [0]])\n\n def predict(self, X_test):\n point = super().predict(X_test[:, 1:])\n return self._translate(point, X_test)\n\n\nclass RFreg(BaseGRF):\n \"\"\"\n A forest that estimates a regression function with pre-specified features.\n Minimizes E[( - Y)^2 | X=x].\n\n Parameters\n ----------\n reg_feature_fns : list of lambda\n Features phi(T,X) used to approximate the regression locally.\n l2 : float, default=0.01\n L2 penalty on the local covariance matrix (no penalty on intercept).\n \"\"\"\n\n def __init__(self, *, reg_feature_fns, l2=0.01,\n n_estimators=100, criterion='mse', max_depth=None,\n min_samples_split=10, min_samples_leaf=5,\n min_weight_fraction_leaf=0., min_var_fraction_leaf=None,\n min_var_leaf_on_val=False, max_features='auto',\n min_impurity_decrease=0., max_samples=.45,\n min_balancedness_tol=.45, honest=True, inference=True,\n fit_intercept=True, subforest_size=4, n_jobs=-1,\n random_state=None, verbose=0, warm_start=False):\n self.reg_feature_fns = reg_feature_fns\n self.l2 = l2\n super().__init__(n_estimators=n_estimators, criterion=criterion,\n max_depth=max_depth, min_samples_split=min_samples_split,\n min_samples_leaf=min_samples_leaf,\n min_weight_fraction_leaf=min_weight_fraction_leaf,\n min_var_fraction_leaf=min_var_fraction_leaf,\n min_var_leaf_on_val=min_var_leaf_on_val,\n max_features=max_features,\n min_impurity_decrease=min_impurity_decrease,\n max_samples=max_samples,\n min_balancedness_tol=min_balancedness_tol,\n honest=honest, inference=inference,\n fit_intercept=False, subforest_size=subforest_size,\n n_jobs=n_jobs, random_state=random_state,\n verbose=verbose, warm_start=warm_start)\n\n def fit(self, X, y):\n return super().fit(X[:, 1:], X[:, [0]], y)\n\n def _get_alpha_and_pointJ(self, X, T, y):\n n_reg_feats = len(self.reg_feature_fns)\n TX = np.hstack([T, X])\n reg_feats = np.hstack([feat_fn(TX) for feat_fn in self.reg_feature_fns])\n alpha = y.reshape(-1, 1) * reg_feats\n reg_cov_matrix = cross_product(reg_feats, reg_feats).reshape(\n (X.shape[0], n_reg_feats, n_reg_feats))\n penalty = self.l2 * np.eye(n_reg_feats)\n penalty[0, 0] = 0\n pointJ = reg_cov_matrix + penalty\n return alpha, pointJ.reshape((X.shape[0], -1))\n\n def _get_n_outputs_decomposition(self, X, T, y):\n n_relevant_outputs = len(self.reg_feature_fns)\n return n_relevant_outputs, n_relevant_outputs\n\n def _translate(self, point, TX):\n reg_feats = np.hstack([feat_fn(TX) for feat_fn in self.reg_feature_fns])\n return np.sum(point * reg_feats, axis=1)\n\n def predict(self, X_test):\n point = super().predict(X_test[:, 1:])\n return self._translate(point, X_test)" + "source": [ + "def poly_feature_fns(degree):\n", + " def poly(d, sign=1.0):\n", + " return lambda x: sign * x[:, [0]]**d\n", + " return [poly(t) for t in np.arange(0, degree + 1)]\n", + "\n", + "\n", + "class RFrr(BaseGRF):\n", + " \"\"\"\n", + " A forest that estimates a Riesz representer of a linear functional.\n", + " Minimizes the Riesz loss E[(phi(T,X)' alpha(x))^2 - 2 * | X=x].\n", + "\n", + " Parameters\n", + " ----------\n", + " riesz_feature_fns : list of lambda\n", + " Features phi(T,X) used to approximate the Riesz representer locally.\n", + " moment_fn : lambda (x, fn) -> float\n", + " The linear moment function m([T;X], g).\n", + " l2 : float, default=0.01\n", + " L2 penalty on the local covariance matrix (no penalty on intercept).\n", + " \"\"\"\n", + "\n", + " def __init__(self, *, riesz_feature_fns, moment_fn, l2=0.01,\n", + " n_estimators=100, criterion='mse', max_depth=None,\n", + " min_samples_split=10, min_samples_leaf=5,\n", + " min_weight_fraction_leaf=0., min_var_fraction_leaf=None,\n", + " min_var_leaf_on_val=False, max_features='auto',\n", + " min_impurity_decrease=0., max_samples=.45,\n", + " min_balancedness_tol=.45, honest=True, inference=True,\n", + " fit_intercept=True, subforest_size=4, n_jobs=-1,\n", + " random_state=None, verbose=0, warm_start=False):\n", + " self.riesz_feature_fns = riesz_feature_fns\n", + " self.moment_fn = moment_fn\n", + " self.l2 = l2\n", + " super().__init__(n_estimators=n_estimators, criterion=criterion,\n", + " max_depth=max_depth, min_samples_split=min_samples_split,\n", + " min_samples_leaf=min_samples_leaf,\n", + " min_weight_fraction_leaf=min_weight_fraction_leaf,\n", + " min_var_fraction_leaf=min_var_fraction_leaf,\n", + " min_var_leaf_on_val=min_var_leaf_on_val,\n", + " max_features=max_features,\n", + " min_impurity_decrease=min_impurity_decrease,\n", + " max_samples=max_samples,\n", + " min_balancedness_tol=min_balancedness_tol,\n", + " honest=honest, inference=inference,\n", + " fit_intercept=False, subforest_size=subforest_size,\n", + " n_jobs=n_jobs, random_state=random_state,\n", + " verbose=verbose, warm_start=warm_start)\n", + "\n", + " def _get_alpha_and_pointJ(self, X, T, y):\n", + " n_riesz_feats = len(self.riesz_feature_fns)\n", + " TX = np.hstack([T, X])\n", + " riesz_feats = np.hstack([feat_fn(TX) for feat_fn in self.riesz_feature_fns])\n", + " mfeats = np.hstack([self.moment_fn(TX, feat_fn) for feat_fn in self.riesz_feature_fns])\n", + " alpha = np.zeros((X.shape[0], n_riesz_feats))\n", + " alpha[:, :n_riesz_feats] = mfeats\n", + " riesz_cov_matrix = cross_product(riesz_feats, riesz_feats).reshape(\n", + " (X.shape[0], n_riesz_feats, n_riesz_feats))\n", + " penalty = self.l2 * np.eye(n_riesz_feats)\n", + " penalty[0, 0] = 0\n", + " pointJ = riesz_cov_matrix + penalty\n", + " return alpha, pointJ.reshape((X.shape[0], -1))\n", + "\n", + " def _get_n_outputs_decomposition(self, X, T, y):\n", + " n_relevant_outputs = len(self.riesz_feature_fns)\n", + " return n_relevant_outputs, n_relevant_outputs\n", + "\n", + " def _translate(self, point, TX):\n", + " riesz_feats = np.hstack([feat_fn(TX) for feat_fn in self.riesz_feature_fns])\n", + " return np.sum(point[:, :riesz_feats.shape[1]] * riesz_feats, axis=1)\n", + "\n", + " def fit(self, X):\n", + " return super().fit(X[:, 1:], X[:, [0]], X[:, [0]])\n", + "\n", + " def predict(self, X_test):\n", + " point = super().predict(X_test[:, 1:])\n", + " return self._translate(point, X_test)\n", + "\n", + "\n", + "class RFreg(BaseGRF):\n", + " \"\"\"\n", + " A forest that estimates a regression function with pre-specified features.\n", + " Minimizes E[( - Y)^2 | X=x].\n", + "\n", + " Parameters\n", + " ----------\n", + " reg_feature_fns : list of lambda\n", + " Features phi(T,X) used to approximate the regression locally.\n", + " l2 : float, default=0.01\n", + " L2 penalty on the local covariance matrix (no penalty on intercept).\n", + " \"\"\"\n", + "\n", + " def __init__(self, *, reg_feature_fns, l2=0.01,\n", + " n_estimators=100, criterion='mse', max_depth=None,\n", + " min_samples_split=10, min_samples_leaf=5,\n", + " min_weight_fraction_leaf=0., min_var_fraction_leaf=None,\n", + " min_var_leaf_on_val=False, max_features='auto',\n", + " min_impurity_decrease=0., max_samples=.45,\n", + " min_balancedness_tol=.45, honest=True, inference=True,\n", + " fit_intercept=True, subforest_size=4, n_jobs=-1,\n", + " random_state=None, verbose=0, warm_start=False):\n", + " self.reg_feature_fns = reg_feature_fns\n", + " self.l2 = l2\n", + " super().__init__(n_estimators=n_estimators, criterion=criterion,\n", + " max_depth=max_depth, min_samples_split=min_samples_split,\n", + " min_samples_leaf=min_samples_leaf,\n", + " min_weight_fraction_leaf=min_weight_fraction_leaf,\n", + " min_var_fraction_leaf=min_var_fraction_leaf,\n", + " min_var_leaf_on_val=min_var_leaf_on_val,\n", + " max_features=max_features,\n", + " min_impurity_decrease=min_impurity_decrease,\n", + " max_samples=max_samples,\n", + " min_balancedness_tol=min_balancedness_tol,\n", + " honest=honest, inference=inference,\n", + " fit_intercept=False, subforest_size=subforest_size,\n", + " n_jobs=n_jobs, random_state=random_state,\n", + " verbose=verbose, warm_start=warm_start)\n", + "\n", + " def fit(self, X, y):\n", + " return super().fit(X[:, 1:], X[:, [0]], y)\n", + "\n", + " def _get_alpha_and_pointJ(self, X, T, y):\n", + " n_reg_feats = len(self.reg_feature_fns)\n", + " TX = np.hstack([T, X])\n", + " reg_feats = np.hstack([feat_fn(TX) for feat_fn in self.reg_feature_fns])\n", + " alpha = y.reshape(-1, 1) * reg_feats\n", + " reg_cov_matrix = cross_product(reg_feats, reg_feats).reshape(\n", + " (X.shape[0], n_reg_feats, n_reg_feats))\n", + " penalty = self.l2 * np.eye(n_reg_feats)\n", + " penalty[0, 0] = 0\n", + " pointJ = reg_cov_matrix + penalty\n", + " return alpha, pointJ.reshape((X.shape[0], -1))\n", + "\n", + " def _get_n_outputs_decomposition(self, X, T, y):\n", + " n_relevant_outputs = len(self.reg_feature_fns)\n", + " return n_relevant_outputs, n_relevant_outputs\n", + "\n", + " def _translate(self, point, TX):\n", + " reg_feats = np.hstack([feat_fn(TX) for feat_fn in self.reg_feature_fns])\n", + " return np.sum(point * reg_feats, axis=1)\n", + "\n", + " def predict(self, X_test):\n", + " point = super().predict(X_test[:, 1:])\n", + " return self._translate(point, X_test)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "c009", "metadata": {}, "outputs": [], - "source": "class DebiasedMoment:\n\n def __init__(self, *, moment_fn, get_reisz_fn, get_reg_fn, n_splits=5):\n self.moment_fn = moment_fn\n self.get_reisz_fn = get_reisz_fn\n self.get_reg_fn = get_reg_fn\n self.n_splits = n_splits\n\n def fit(self, X, y):\n y = y.flatten()\n if self.n_splits == 1:\n splits = [(np.arange(X.shape[0]), np.arange(X.shape[0]))]\n elif isinstance(self.n_splits, int):\n splits = list(KFold(n_splits=self.n_splits).split(X))\n else:\n splits = self.n_splits\n\n reisz_pred = np.zeros(X.shape[0])\n reg_pred = np.zeros(X.shape[0])\n moment_pred = np.zeros(X.shape[0])\n moment_reisz = np.zeros(X.shape[0])\n self.reisz_models_ = []\n self.reg_models_ = []\n reisz_fn = self.get_reisz_fn(X)\n reg_fn = self.get_reg_fn(X, y)\n for train, test in splits:\n reisz = reisz_fn().fit(X[train])\n reisz_pred[test] = reisz.predict(X[test])\n reg = reg_fn().fit(X[train], y[train])\n reg_pred[test] = reg.predict(X[test])\n moment_pred[test] = self.moment_fn(X[test], reg.predict)\n moment_reisz[test] = self.moment_fn(X[test], reisz.predict)\n self.reisz_models_.append(reisz)\n self.reg_models_.append(reg)\n\n self.reg_pred_ = reg_pred\n self.reisz_pred_ = reisz_pred\n self.moment_ = moment_pred + reisz_pred * (y - reg_pred)\n self.moment_reisz_ = moment_reisz\n self.moment_pred_ = moment_pred\n self.y_ = y.copy()\n\n epsilon = LinearRegression(fit_intercept=False).fit(\n reisz_pred.reshape(-1, 1), y - reg_pred).coef_[0]\n moment_pred = moment_pred + moment_reisz * epsilon\n reg_pred = reg_pred + epsilon * reisz_pred\n self.tmle_moment_ = moment_pred + reisz_pred * (y - reg_pred)\n return self\n\n def avg_moment(self, alpha=0.05, tmle=False):\n if tmle:\n return mean_ci(self.tmle_moment_, confidence=1 - alpha)\n return mean_ci(self.moment_, confidence=1 - alpha)" + "source": [ + "class DebiasedMoment:\n", + "\n", + " def __init__(self, *, moment_fn, get_reisz_fn, get_reg_fn, n_splits=5):\n", + " self.moment_fn = moment_fn\n", + " self.get_reisz_fn = get_reisz_fn\n", + " self.get_reg_fn = get_reg_fn\n", + " self.n_splits = n_splits\n", + "\n", + " def fit(self, X, y):\n", + " y = y.flatten()\n", + " if self.n_splits == 1:\n", + " splits = [(np.arange(X.shape[0]), np.arange(X.shape[0]))]\n", + " elif isinstance(self.n_splits, int):\n", + " splits = list(KFold(n_splits=self.n_splits).split(X))\n", + " else:\n", + " splits = self.n_splits\n", + "\n", + " reisz_pred = np.zeros(X.shape[0])\n", + " reg_pred = np.zeros(X.shape[0])\n", + " moment_pred = np.zeros(X.shape[0])\n", + " moment_reisz = np.zeros(X.shape[0])\n", + " self.reisz_models_ = []\n", + " self.reg_models_ = []\n", + " reisz_fn = self.get_reisz_fn(X)\n", + " reg_fn = self.get_reg_fn(X, y)\n", + " for train, test in splits:\n", + " reisz = reisz_fn().fit(X[train])\n", + " reisz_pred[test] = reisz.predict(X[test])\n", + " reg = reg_fn().fit(X[train], y[train])\n", + " reg_pred[test] = reg.predict(X[test])\n", + " moment_pred[test] = self.moment_fn(X[test], reg.predict)\n", + " moment_reisz[test] = self.moment_fn(X[test], reisz.predict)\n", + " self.reisz_models_.append(reisz)\n", + " self.reg_models_.append(reg)\n", + "\n", + " self.reg_pred_ = reg_pred\n", + " self.reisz_pred_ = reisz_pred\n", + " self.moment_ = moment_pred + reisz_pred * (y - reg_pred)\n", + " self.moment_reisz_ = moment_reisz\n", + " self.moment_pred_ = moment_pred\n", + " self.y_ = y.copy()\n", + "\n", + " epsilon = LinearRegression(fit_intercept=False).fit(\n", + " reisz_pred.reshape(-1, 1), y - reg_pred).coef_[0]\n", + " moment_pred = moment_pred + moment_reisz * epsilon\n", + " reg_pred = reg_pred + epsilon * reisz_pred\n", + " self.tmle_moment_ = moment_pred + reisz_pred * (y - reg_pred)\n", + " return self\n", + "\n", + " def avg_moment(self, alpha=0.05, tmle=False):\n", + " if tmle:\n", + " return mean_ci(self.tmle_moment_, confidence=1 - alpha)\n", + " return mean_ci(self.moment_, confidence=1 - alpha)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "c010", "metadata": {}, "outputs": [], - "source": "def first_stage_reg(numeric_columns=[], cv=None):\n return GridSearchCVList([\n Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n remainder='passthrough')),\n ('lasso', Lasso())]),\n Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n remainder='passthrough')),\n ('feat', Featurizer(degree=1, normalize=False)),\n ('lasso', Lasso())]),\n RandomForestRegressor(n_estimators=100, random_state=123),\n RFreg(reg_feature_fns=poly_feature_fns(10),\n min_var_leaf_on_val=True, min_impurity_decrease=1e-4,\n max_samples=.85, inference=False, subforest_size=1, random_state=123)\n ],\n param_grid_list=[\n {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n {'max_depth': [3, 5], 'min_samples_leaf': [10, 50]},\n {'reg_feature_fns': [poly_feature_fns(2)], 'min_samples_leaf': [10],\n 'min_var_fraction_leaf': [0.01, 0.1], 'l2': [1e-3, 1e-2]}\n ],\n cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n\n\ndef reg_fn_gen(numeric_columns, cv=None, verbose=0):\n def get_reg_fn(X, y):\n est = first_stage_reg(numeric_columns, cv=cv).fit(X, y)\n if verbose > 0:\n print('Chose: ', est.best_score_, est.best_estimator_)\n return lambda: clone(est.best_estimator_)\n return get_reg_fn\n\n\ndef neg_reisz_score_gen(moment_fn):\n def neg_reisz_score(model, X):\n return np.mean(2 * moment_fn(X, model.predict) - model.predict(X)**2)\n return neg_reisz_score\n\n\ndef first_stage_rr(moment_fn, numeric_columns, cv=None):\n return GridSearchCVList([\n SieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn),\n SieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn),\n SparseSieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn, tol=1e-4),\n SparseSieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn, tol=1e-4),\n RFrr(riesz_feature_fns=poly_feature_fns(10), moment_fn=moment_fn,\n min_var_leaf_on_val=True, min_impurity_decrease=0.001,\n max_samples=.65, inference=False, subforest_size=1, random_state=123)\n ],\n param_grid_list=[\n {'alpha': np.geomspace(1e-6, 1e3, 20)},\n {'alpha': np.geomspace(1e-6, 1e3, 20)},\n {'alpha': np.geomspace(1e-6, 1e3, 20), 'l1_ratio': [.5, 1.0]},\n {'alpha': np.geomspace(1e-6, 1e3, 20), 'l1_ratio': [.5, 1.0]},\n {'riesz_feature_fns': [poly_feature_fns(2), poly_feature_fns(3)],\n 'min_samples_leaf': [10, 50], 'min_var_fraction_leaf': [0.01, 0.1],\n 'l2': [1e-5, 1e-3], 'max_depth': [5, None]}\n ],\n scoring=neg_reisz_score_gen(moment_fn), cv=cv, verbose=0, n_jobs=-1)\n\n\ndef rr_fn_gen(moment_fn, numeric_columns, cv=None, verbose=0):\n def get_rr_fn(X):\n est = first_stage_rr(moment_fn, numeric_columns, cv=cv).fit(X)\n if verbose > 0:\n print('Chose: ', est.best_score_, est.best_estimator_)\n return lambda: clone(est.best_estimator_)\n return get_rr_fn\n\n\nclass RegRandomForestClassifier(BaseEstimator):\n def __init__(self, *, max_depth=None, min_samples_leaf=1):\n self.max_depth = max_depth\n self.min_samples_leaf = min_samples_leaf\n\n def fit(self, X, y):\n self.model_ = RandomForestClassifier(n_estimators=100, random_state=123,\n max_depth=self.max_depth,\n min_samples_leaf=self.min_samples_leaf)\n self.model_.fit(X, y)\n return self\n\n def predict(self, X):\n return self.model_.predict_proba(X)[:, 1]\n\n\nclass RegLogisticRegression(BaseEstimator):\n def __init__(self, *, C=1):\n self.C = C\n\n def fit(self, X, y):\n self.model_ = LogisticRegression(C=self.C, max_iter=1000, random_state=123)\n self.model_.fit(X, y)\n return self\n\n def predict(self, X):\n return self.model_.predict_proba(X)[:, 1]" + "source": [ + "def first_stage_reg(numeric_columns=[], cv=None):\n", + " return GridSearchCVList([\n", + " Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n", + " remainder='passthrough')),\n", + " ('lasso', Lasso())]),\n", + " Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n", + " remainder='passthrough')),\n", + " ('feat', Featurizer(degree=1, normalize=False)),\n", + " ('lasso', Lasso())]),\n", + " RandomForestRegressor(n_estimators=100, random_state=123),\n", + " RFreg(reg_feature_fns=poly_feature_fns(10),\n", + " min_var_leaf_on_val=True, min_impurity_decrease=1e-4,\n", + " max_samples=.85, inference=False, subforest_size=1, random_state=123)\n", + " ],\n", + " param_grid_list=[\n", + " {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n", + " {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n", + " {'max_depth': [3, 5], 'min_samples_leaf': [10, 50]},\n", + " {'reg_feature_fns': [poly_feature_fns(2)], 'min_samples_leaf': [10],\n", + " 'min_var_fraction_leaf': [0.01, 0.1], 'l2': [1e-3, 1e-2]}\n", + " ],\n", + " cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n", + "\n", + "\n", + "def reg_fn_gen(numeric_columns, cv=None, verbose=0):\n", + " def get_reg_fn(X, y):\n", + " est = first_stage_reg(numeric_columns, cv=cv).fit(X, y)\n", + " if verbose > 0:\n", + " print('Chose: ', est.best_score_, est.best_estimator_)\n", + " return lambda: clone(est.best_estimator_)\n", + " return get_reg_fn\n", + "\n", + "\n", + "def neg_reisz_score_gen(moment_fn):\n", + " def neg_reisz_score(model, X):\n", + " return np.mean(2 * moment_fn(X, model.predict) - model.predict(X)**2)\n", + " return neg_reisz_score\n", + "\n", + "\n", + "def first_stage_rr(moment_fn, numeric_columns, cv=None):\n", + " return GridSearchCVList([\n", + " SieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn),\n", + " SieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn),\n", + " SparseSieveRR(featurizer=Featurizer(degree=2), moment_fn=moment_fn, tol=1e-4),\n", + " SparseSieveRR(featurizer=Featurizer(degree=1), moment_fn=moment_fn, tol=1e-4),\n", + " RFrr(riesz_feature_fns=poly_feature_fns(10), moment_fn=moment_fn,\n", + " min_var_leaf_on_val=True, min_impurity_decrease=0.001,\n", + " max_samples=.65, inference=False, subforest_size=1, random_state=123)\n", + " ],\n", + " param_grid_list=[\n", + " {'alpha': np.geomspace(1e-6, 1e3, 20)},\n", + " {'alpha': np.geomspace(1e-6, 1e3, 20)},\n", + " {'alpha': np.geomspace(1e-6, 1e3, 20), 'l1_ratio': [.5, 1.0]},\n", + " {'alpha': np.geomspace(1e-6, 1e3, 20), 'l1_ratio': [.5, 1.0]},\n", + " {'riesz_feature_fns': [poly_feature_fns(2), poly_feature_fns(3)],\n", + " 'min_samples_leaf': [10, 50], 'min_var_fraction_leaf': [0.01, 0.1],\n", + " 'l2': [1e-5, 1e-3], 'max_depth': [5, None]}\n", + " ],\n", + " scoring=neg_reisz_score_gen(moment_fn), cv=cv, verbose=0, n_jobs=-1)\n", + "\n", + "\n", + "def rr_fn_gen(moment_fn, numeric_columns, cv=None, verbose=0):\n", + " def get_rr_fn(X):\n", + " est = first_stage_rr(moment_fn, numeric_columns, cv=cv).fit(X)\n", + " if verbose > 0:\n", + " print('Chose: ', est.best_score_, est.best_estimator_)\n", + " return lambda: clone(est.best_estimator_)\n", + " return get_rr_fn\n", + "\n", + "\n", + "class RegRandomForestClassifier(BaseEstimator):\n", + " def __init__(self, *, max_depth=None, min_samples_leaf=1):\n", + " self.max_depth = max_depth\n", + " self.min_samples_leaf = min_samples_leaf\n", + "\n", + " def fit(self, X, y):\n", + " self.model_ = RandomForestClassifier(n_estimators=100, random_state=123,\n", + " max_depth=self.max_depth,\n", + " min_samples_leaf=self.min_samples_leaf)\n", + " self.model_.fit(X, y)\n", + " return self\n", + "\n", + " def predict(self, X):\n", + " return self.model_.predict_proba(X)[:, 1]\n", + "\n", + "\n", + "class RegLogisticRegression(BaseEstimator):\n", + " def __init__(self, *, C=1):\n", + " self.C = C\n", + "\n", + " def fit(self, X, y):\n", + " self.model_ = LogisticRegression(C=self.C, max_iter=1000, random_state=123)\n", + " self.model_.fit(X, y)\n", + " return self\n", + "\n", + " def predict(self, X):\n", + " return self.model_.predict_proba(X)[:, 1]" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c011", "metadata": {}, "outputs": [], - "source": "def moment_estimate(est, inds=None):\n if inds is None:\n inds = np.arange(est.moment_reisz_.shape[0])\n theta = np.mean(est.moment_[inds])\n psi_theta = est.moment_[inds] - theta\n return theta, np.sqrt(np.mean(psi_theta**2) / psi_theta.shape[0])\n\n\ndef sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None):\n if inds is None:\n inds = np.arange(est.moment_reisz_.shape[0])\n nusq = np.mean(2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2)\n sigmasq = np.mean((est.y_[inds] - est.reg_pred_[inds])**2)\n S = np.sqrt(sigmasq * nusq)\n Casq = eta_asq / (1 - eta_asq)\n Cgsq = eta_ysq\n theta = np.mean(est.moment_[inds])\n error = S * np.sqrt(Casq * Cgsq)\n if alpha is None:\n return theta - error, theta + error\n psi_theta = est.moment_[inds] - theta\n psi_sigmasq = (est.y_[inds] - est.reg_pred_[inds])**2 - sigmasq\n psi_nusq = 2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2 - nusq\n phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0])\n phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0])\n return theta - error, stderr_minus, theta + error, stderr_plus\n\n\ndef sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n if alpha is None:\n lower, upper = zip(*[sensitivity_bounds_single(est, eta_ysq, eta_asq,\n alpha=alpha, inds=inds) for est in est_list])\n return np.mean(lower), np.mean(upper)\n else:\n lower, std_lower, upper, std_upper = zip(\n *[sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds)\n for est in est_list])\n std_lower = np.array(std_lower)\n std_upper = np.array(std_upper)\n lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower))\n upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper))\n return lower, upper\n\n\ndef tvalue(est_list, value=0, leq=None, alpha=None, inds=None):\n for t in np.linspace(0, 1, 1000):\n l, u = sensitivity_bounds(est_list, t, t, alpha=alpha, inds=inds)\n if (leq is None) & (value >= l) & (value <= u):\n break\n if (leq == True) & (value >= l):\n break\n if (leq == False) & (value <= u):\n break\n return t\n\n\ndef sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None):\n xlist = np.linspace(0, a_upper, 100)\n ylist = np.linspace(0, y_upper, 100)\n X, Y = np.meshgrid(xlist, ylist)\n Zlower = np.zeros(X.shape)\n Zupper = np.zeros(X.shape)\n for itx in np.arange(X.shape[1]):\n for ity in np.arange(X.shape[0]):\n l, u = sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds)\n Zlower[ity, itx] = l\n Zupper[ity, itx] = u\n return X, Y, Zlower, Zupper\n\n\ndef dml_estimate(est, inds=None):\n if inds is None:\n inds = np.arange(est.residuals_[0].shape[0])\n yres, Tres, _, _ = est.residuals_\n yres, Tres = yres[inds], Tres[inds]\n nusq = np.mean(Tres ** 2)\n theta = np.mean(yres * Tres) / nusq\n psi_theta = (yres - Tres * theta) * Tres / nusq\n return theta, np.sqrt(np.mean(psi_theta**2) / psi_theta.shape[0])\n\n\ndef dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None):\n if inds is None:\n inds = np.arange(est.residuals_[0].shape[0])\n yres, Tres, _, _ = est.residuals_\n yres, Tres = yres[inds], Tres[inds]\n nusq = np.mean(Tres ** 2)\n theta = np.mean(yres * Tres) / nusq\n sigmasq = np.mean((yres - Tres * theta)**2)\n S = np.sqrt(sigmasq / nusq)\n Casq = eta_asq / (1 - eta_asq)\n Cgsq = eta_ysq\n error = S * np.sqrt(Casq * Cgsq)\n if alpha is None:\n return theta - error, theta + error\n psi_theta = (yres - Tres * theta) * Tres / nusq\n psi_sigmasq = (yres - Tres * theta)**2 - sigmasq\n psi_nusq = Tres**2 - nusq\n phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (\n -(sigmasq / (nusq**2)) * psi_nusq + (1 / nusq) * psi_sigmasq)\n stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0])\n phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (\n -(sigmasq / (nusq**2)) * psi_nusq + (1 / nusq) * psi_sigmasq)\n stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0])\n return theta - error, stderr_minus, theta + error, stderr_plus\n\n\ndef dml_sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n if alpha is None:\n lower, upper = zip(*[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq,\n alpha=alpha, inds=inds)\n for est in est_list])\n return np.mean(lower), np.mean(upper)\n else:\n lower, std_lower, upper, std_upper = zip(\n *[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds)\n for est in est_list])\n std_lower = np.array(std_lower)\n std_upper = np.array(std_upper)\n lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower))\n upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper))\n return lower, upper\n\n\ndef dml_tvalue(est, value=0, leq=None, alpha=None, inds=None):\n for t in np.linspace(0, 1, 1000):\n l, u = dml_sensitivity_bounds(est, t, t, alpha=alpha, inds=inds)\n if (leq is None) & (value >= l) & (value <= u):\n break\n if (leq == True) & (value >= l):\n break\n if (leq == False) & (value <= u):\n break\n return t\n\n\ndef dml_sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None):\n xlist = np.linspace(0, a_upper, 100)\n ylist = np.linspace(0, y_upper, 100)\n X, Y = np.meshgrid(xlist, ylist)\n Zlower = np.zeros(X.shape)\n Zupper = np.zeros(X.shape)\n for itx in np.arange(X.shape[1]):\n for ity in np.arange(X.shape[0]):\n l, u = dml_sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds)\n Zlower[ity, itx] = l\n Zupper[ity, itx] = u\n return X, Y, Zlower, Zupper" + "source": [ + "def moment_estimate(est, inds=None):\n", + " if inds is None:\n", + " inds = np.arange(est.moment_reisz_.shape[0])\n", + " theta = np.mean(est.moment_[inds])\n", + " psi_theta = est.moment_[inds] - theta\n", + " return theta, np.sqrt(np.mean(psi_theta**2) / psi_theta.shape[0])\n", + "\n", + "\n", + "def sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None):\n", + " if inds is None:\n", + " inds = np.arange(est.moment_reisz_.shape[0])\n", + " nusq = np.mean(2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2)\n", + " sigmasq = np.mean((est.y_[inds] - est.reg_pred_[inds])**2)\n", + " S = np.sqrt(sigmasq * nusq)\n", + " Casq = eta_asq / (1 - eta_asq)\n", + " Cgsq = eta_ysq\n", + " theta = np.mean(est.moment_[inds])\n", + " error = S * np.sqrt(Casq * Cgsq)\n", + " if alpha is None:\n", + " return theta - error, theta + error\n", + " psi_theta = est.moment_[inds] - theta\n", + " psi_sigmasq = (est.y_[inds] - est.reg_pred_[inds])**2 - sigmasq\n", + " psi_nusq = 2 * est.moment_reisz_[inds] - est.reisz_pred_[inds]**2 - nusq\n", + " phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n", + " stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0])\n", + " phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n", + " stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0])\n", + " return theta - error, stderr_minus, theta + error, stderr_plus\n", + "\n", + "\n", + "def sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n", + " if alpha is None:\n", + " lower, upper = zip(*[sensitivity_bounds_single(est, eta_ysq, eta_asq,\n", + " alpha=alpha, inds=inds) for est in est_list])\n", + " return np.mean(lower), np.mean(upper)\n", + " else:\n", + " lower, std_lower, upper, std_upper = zip(\n", + " *[sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds)\n", + " for est in est_list])\n", + " std_lower = np.array(std_lower)\n", + " std_upper = np.array(std_upper)\n", + " lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower))\n", + " upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper))\n", + " return lower, upper\n", + "\n", + "\n", + "def tvalue(est_list, value=0, leq=None, alpha=None, inds=None):\n", + " for t in np.linspace(0, 1, 1000):\n", + " l, u = sensitivity_bounds(est_list, t, t, alpha=alpha, inds=inds)\n", + " if (leq is None) & (value >= l) & (value <= u):\n", + " break\n", + " if (leq == True) & (value >= l):\n", + " break\n", + " if (leq == False) & (value <= u):\n", + " break\n", + " return t\n", + "\n", + "\n", + "def sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None):\n", + " xlist = np.linspace(0, a_upper, 100)\n", + " ylist = np.linspace(0, y_upper, 100)\n", + " X, Y = np.meshgrid(xlist, ylist)\n", + " Zlower = np.zeros(X.shape)\n", + " Zupper = np.zeros(X.shape)\n", + " for itx in np.arange(X.shape[1]):\n", + " for ity in np.arange(X.shape[0]):\n", + " l, u = sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds)\n", + " Zlower[ity, itx] = l\n", + " Zupper[ity, itx] = u\n", + " return X, Y, Zlower, Zupper\n", + "\n", + "\n", + "def dml_estimate(est, inds=None):\n", + " if inds is None:\n", + " inds = np.arange(est.residuals_[0].shape[0])\n", + " yres, Tres, _, _ = est.residuals_\n", + " yres, Tres = yres[inds], Tres[inds]\n", + " nusq = np.mean(Tres ** 2)\n", + " theta = np.mean(yres * Tres) / nusq\n", + " psi_theta = (yres - Tres * theta) * Tres / nusq\n", + " return theta, np.sqrt(np.mean(psi_theta**2) / psi_theta.shape[0])\n", + "\n", + "\n", + "def dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=None, inds=None):\n", + " if inds is None:\n", + " inds = np.arange(est.residuals_[0].shape[0])\n", + " yres, Tres, _, _ = est.residuals_\n", + " yres, Tres = yres[inds], Tres[inds]\n", + " nusq = np.mean(Tres ** 2)\n", + " theta = np.mean(yres * Tres) / nusq\n", + " sigmasq = np.mean((yres - Tres * theta)**2)\n", + " S = np.sqrt(sigmasq / nusq)\n", + " Casq = eta_asq / (1 - eta_asq)\n", + " Cgsq = eta_ysq\n", + " error = S * np.sqrt(Casq * Cgsq)\n", + " if alpha is None:\n", + " return theta - error, theta + error\n", + " psi_theta = (yres - Tres * theta) * Tres / nusq\n", + " psi_sigmasq = (yres - Tres * theta)**2 - sigmasq\n", + " psi_nusq = Tres**2 - nusq\n", + " phi_plus = psi_theta + (np.sqrt(Casq * Cgsq) / (2 * S)) * (\n", + " -(sigmasq / (nusq**2)) * psi_nusq + (1 / nusq) * psi_sigmasq)\n", + " stderr_plus = np.sqrt(np.mean(phi_plus**2) / phi_plus.shape[0])\n", + " phi_minus = psi_theta - (np.sqrt(Casq * Cgsq) / (2 * S)) * (\n", + " -(sigmasq / (nusq**2)) * psi_nusq + (1 / nusq) * psi_sigmasq)\n", + " stderr_minus = np.sqrt(np.mean(phi_minus**2) / phi_minus.shape[0])\n", + " return theta - error, stderr_minus, theta + error, stderr_plus\n", + "\n", + "\n", + "def dml_sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n", + " if alpha is None:\n", + " lower, upper = zip(*[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq,\n", + " alpha=alpha, inds=inds)\n", + " for est in est_list])\n", + " return np.mean(lower), np.mean(upper)\n", + " else:\n", + " lower, std_lower, upper, std_upper = zip(\n", + " *[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq, alpha=alpha, inds=inds)\n", + " for est in est_list])\n", + " std_lower = np.array(std_lower)\n", + " std_upper = np.array(std_upper)\n", + " lower = np.mean(lower) - scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_lower**2) + np.var(lower))\n", + " upper = np.mean(upper) + scipy.stats.norm.ppf(1 - alpha) * np.sqrt(np.mean(std_upper**2) + np.var(upper))\n", + " return lower, upper\n", + "\n", + "\n", + "def dml_tvalue(est, value=0, leq=None, alpha=None, inds=None):\n", + " for t in np.linspace(0, 1, 1000):\n", + " l, u = dml_sensitivity_bounds(est, t, t, alpha=alpha, inds=inds)\n", + " if (leq is None) & (value >= l) & (value <= u):\n", + " break\n", + " if (leq == True) & (value >= l):\n", + " break\n", + " if (leq == False) & (value <= u):\n", + " break\n", + " return t\n", + "\n", + "\n", + "def dml_sensitivity_contours(est_list, a_upper, y_upper, alpha=None, inds=None):\n", + " xlist = np.linspace(0, a_upper, 100)\n", + " ylist = np.linspace(0, y_upper, 100)\n", + " X, Y = np.meshgrid(xlist, ylist)\n", + " Zlower = np.zeros(X.shape)\n", + " Zupper = np.zeros(X.shape)\n", + " for itx in np.arange(X.shape[1]):\n", + " for ity in np.arange(X.shape[0]):\n", + " l, u = dml_sensitivity_bounds(est_list, Y[ity, itx], X[ity, itx], alpha=alpha, inds=inds)\n", + " Zlower[ity, itx] = l\n", + " Zupper[ity, itx] = u\n", + " return X, Y, Zlower, Zupper" + ] }, { "cell_type": "markdown", "id": "c012", "metadata": {}, - "source": "## Setting\n\nThe data come from the 2001 National Household Travel Survey and contain household gasoline consumption, gasoline prices, income brackets, and 24 observed controls describing population density, urbanization, demographics, and Census regions. After the sample restrictions used in the earlier demand studies, the analysis contains 3,466 households.\n\nThe outcome $Y$ is log gasoline consumption, the treatment $D$ is log gasoline price, and $X^o$ collects the observed controls. Under conditional ignorability, the short regression is\n\n$$g_s(d, x^o) = \\mathrm{E}[Y \\mid D = d, X^o = x^o],$$\n\nand the target is the average causal derivative\n\n$$\\theta_s = \\mathrm{E}\\big\\{\\partial_d\\, g_s(D, X^o)\\big\\}.$$\n\nBecause both variables are in logarithms, $\\theta_s$ is the average price elasticity of gasoline demand. Its short Riesz representer is the negative conditional density score,\n\n$$\\alpha_s(D, X^o) = -\\partial_d \\log f_{D \\mid X^o}(D \\mid X^o).$$\n\nThe moment function below encodes $m(W_s, g) = \\partial_d\\, g(D, X^o)$. The derivative with respect to log price is approximated by a central finite difference, $\\{g(D + \\epsilon, X^o) - g(D - \\epsilon, X^o)\\}/(2\\epsilon)$ with $\\epsilon = 0.01$." + "source": [ + "## Setting\n", + "\n", + "The data come from the 2001 National Household Travel Survey and contain household gasoline consumption, gasoline prices, income brackets, and 24 observed controls describing population density, urbanization, demographics, and Census regions. After the sample restrictions used in the earlier demand studies, the analysis contains 3,466 households.\n", + "\n", + "The outcome $Y$ is log gasoline consumption, the treatment $D$ is log gasoline price, and $X^o$ collects the observed controls. Under conditional ignorability, the short regression is\n", + "\n", + "$$g_s(d, x^o) = \\mathrm{E}[Y \\mid D = d, X^o = x^o],$$\n", + "\n", + "and the target is the average causal derivative\n", + "\n", + "$$\\theta_s = \\mathrm{E}\\big\\{\\partial_d\\, g_s(D, X^o)\\big\\}.$$\n", + "\n", + "Because both variables are in logarithms, $\\theta_s$ is the average price elasticity of gasoline demand. Its short Riesz representer is the negative conditional density score,\n", + "\n", + "$$\\alpha_s(D, X^o) = -\\partial_d \\log f_{D \\mid X^o}(D \\mid X^o).$$\n", + "\n", + "The moment function below encodes $m(W_s, g) = \\partial_d\\, g(D, X^o)$. The derivative with respect to log price is approximated by a central finite difference, $\\{g(D + \\epsilon, X^o) - g(D - \\epsilon, X^o)\\}/(2\\epsilon)$ with $\\epsilon = 0.01$." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "c013", "metadata": {}, "outputs": [], - "source": "# The moment function, whose average we want to estimate: E[m(X; g)]\ndef moment_fn(x, test_fn):\n epsilon = 0.01\n t1 = np.hstack([x[:, [0]] + epsilon, x[:, 1:]])\n t0 = np.hstack([x[:, [0]] - epsilon, x[:, 1:]])\n return (test_fn(t1) - test_fn(t0)) / (2*epsilon)" + "source": [ + "# The moment function, whose average we want to estimate: E[m(X; g)]\n", + "def moment_fn(x, test_fn):\n", + " epsilon = 0.01\n", + " t1 = np.hstack([x[:, [0]] + epsilon, x[:, 1:]])\n", + " t0 = np.hstack([x[:, [0]] - epsilon, x[:, 1:]])\n", + " return (test_fn(t1) - test_fn(t0)) / (2*epsilon)" + ] }, { "cell_type": "markdown", "id": "c014", "metadata": {}, - "source": "## Data\n\nFollowing the earlier demand studies, households with very low prices or incomes are dropped. Besides the full sample (`q=5`), the analysis uses four income groups. The first three (`q=1,2,3`) are overlapping groups centered near annual incomes of \\$42,500, \\$57,500 and \\$72,500. The fourth (`q=4`) contains high-income households with income above roughly \\$110,000 ($\\log y \\geq 11.6$).\n\nCross-validation and cross-fitting are grouped by state, so households from the same state remain in the same fold. The folds are stratified by Census region, and region dummies are included as controls." + "source": [ + "## Data\n", + "\n", + "Following the earlier demand studies, households with very low prices or incomes are dropped. Besides the full sample (`q=5`), the analysis uses four income groups. The first three (`q=1,2,3`) are overlapping groups centered near annual incomes of \\$42,500, \\$57,500 and \\$72,500. The fourth (`q=4`) contains high-income households with income above roughly \\$110,000 ($\\log y \\geq 11.6$).\n", + "\n", + "Cross-validation and cross-fitting are grouped by state, so households from the same state remain in the same fold. The folds are stratified by Census region, and region dummies are included as controls." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "c015", "metadata": {}, "outputs": [], - "source": "# Data loading and manipulation\n\n# for semi-synthetic data generation\ndef true_f(X):\n y = (-.7 - .4 * scipy.special.expit(3 * (1 - 2*(X[:, 1] - 10.5)))) * X[:, 0]\n y += scipy.special.expit(3 * (1 - 2 * (X[:, 1] > 11.5)))\n return y\n\n\ndef get_data(q=None, synthetic=False, region=True, state=False, region_ids=None, random_seed=123):\n data_url = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/update-notebooks/data/data_BHP2_with_region.csv\"\n df = pd.read_csv(data_url)\n # Throw away low prices and low income due to sample quality as in Chetverikov\n df = df[df['log_p'] > math.log(1.2)]\n df = df[df['log_y'] > math.log(15000)]\n if not ((q == 4) | (q == 5)): # all data in first three brackets\n df = df[df['log_y'] >= math.log(42500) - .5]\n df = df[df['log_y'] <= math.log(72500) + .5]\n if q == 1:\n df = df[df['log_y'] >= math.log(42500) - .5]\n df = df[df['log_y'] <= math.log(42500) + .5]\n if q == 2:\n df = df[df['log_y'] >= math.log(57500) - .5]\n df = df[df['log_y'] <= math.log(57500) + .5]\n if q == 3:\n df = df[df['log_y'] >= math.log(72500) - .5]\n df = df[df['log_y'] <= math.log(72500) + .5]\n if q == 4:\n df = df[df['log_y'] >= 11.6]\n if region_ids is not None:\n df = df[np.any([df['region'] == r for r in region_ids], axis=0)]\n Xdf = df.iloc[:, 1:] # Covariates\n X_nostatedum = Xdf.drop(['distance_oil1000', 'share'], axis=1).values\n if state:\n state_dum = pd.get_dummies(Xdf['state_fips'], prefix='state', drop_first=True)\n Xdf = pd.concat([Xdf, state_dum], axis=1)\n if region:\n region_dum = pd.get_dummies(Xdf['region'], prefix='region', drop_first=True)\n Xdf = pd.concat([Xdf, region_dum], axis=1)\n Xdf = Xdf.drop(['distance_oil1000', 'state_fips', 'share', 'region'], axis=1)\n column_names = Xdf.columns\n X = Xdf.values\n y = df['log_q'].values\n if synthetic:\n y = true_f(X) + np.random.normal(0, .2, size=X[:, 0].shape)\n print(np.mean(moment_fn(X, true_f)))\n cv = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=random_seed)\n splits = list(cv.split(df, y=df['region'], groups=df['state_fips']))\n return X, y, [0, 1, 6, 7, 8], splits, column_names" + "source": [ + "# Data loading and manipulation\n", + "\n", + "# for semi-synthetic data generation\n", + "def true_f(X):\n", + " y = (-.7 - .4 * scipy.special.expit(3 * (1 - 2*(X[:, 1] - 10.5)))) * X[:, 0]\n", + " y += scipy.special.expit(3 * (1 - 2 * (X[:, 1] > 11.5)))\n", + " return y\n", + "\n", + "\n", + "def get_data(q=None, synthetic=False, region=True, state=False, region_ids=None, random_seed=123):\n", + " data_url = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/update-notebooks/data/data_BHP2_with_region.csv\"\n", + " # Need to update this url when we launch the new version of the repo with the data in it. For now, this is a temporary url.\n", + " df = pd.read_csv(data_url)\n", + " # Throw away low prices and low income due to sample quality as in Chetverikov\n", + " df = df[df['log_p'] > math.log(1.2)]\n", + " df = df[df['log_y'] > math.log(15000)]\n", + " if not ((q == 4) | (q == 5)): # all data in first three brackets\n", + " df = df[df['log_y'] >= math.log(42500) - .5]\n", + " df = df[df['log_y'] <= math.log(72500) + .5]\n", + " if q == 1:\n", + " df = df[df['log_y'] >= math.log(42500) - .5]\n", + " df = df[df['log_y'] <= math.log(42500) + .5]\n", + " if q == 2:\n", + " df = df[df['log_y'] >= math.log(57500) - .5]\n", + " df = df[df['log_y'] <= math.log(57500) + .5]\n", + " if q == 3:\n", + " df = df[df['log_y'] >= math.log(72500) - .5]\n", + " df = df[df['log_y'] <= math.log(72500) + .5]\n", + " if q == 4:\n", + " df = df[df['log_y'] >= 11.6]\n", + " if region_ids is not None:\n", + " df = df[np.any([df['region'] == r for r in region_ids], axis=0)]\n", + " Xdf = df.iloc[:, 1:] # Covariates\n", + " X_nostatedum = Xdf.drop(['distance_oil1000', 'share'], axis=1).values\n", + " if state:\n", + " state_dum = pd.get_dummies(Xdf['state_fips'], prefix='state', drop_first=True)\n", + " Xdf = pd.concat([Xdf, state_dum], axis=1)\n", + " if region:\n", + " region_dum = pd.get_dummies(Xdf['region'], prefix='region', drop_first=True)\n", + " Xdf = pd.concat([Xdf, region_dum], axis=1)\n", + " Xdf = Xdf.drop(['distance_oil1000', 'state_fips', 'share', 'region'], axis=1)\n", + " column_names = Xdf.columns\n", + " X = Xdf.values\n", + " y = df['log_q'].values\n", + " if synthetic:\n", + " y = true_f(X) + np.random.normal(0, .2, size=X[:, 0].shape)\n", + " print(np.mean(moment_fn(X, true_f)))\n", + " cv = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=random_seed)\n", + " splits = list(cv.split(df, y=df['region'], groups=df['state_fips']))\n", + " return X, y, [0, 1, 6, 7, 8], splits, column_names" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c016", "metadata": {}, "outputs": [], - "source": "# global result dictionaries and global parameters\nests = {}\nres = {}\nres_d = {}\nq_grid = np.arange(1, 6)\nsynthetic = False # use synthetic outcome\nverbose = 0\nregion = True # control for region" + "source": [ + "# global result dictionaries and global parameters\n", + "ests = {}\n", + "res = {}\n", + "res_d = {}\n", + "q_grid = np.arange(1, 6)\n", + "synthetic = False # use synthetic outcome\n", + "verbose = 0\n", + "region = True # control for region" + ] }, { "cell_type": "markdown", "id": "c017", "metadata": {}, - "source": "## Short Estimates Under Conditional Ignorability" + "source": [ + "## Short Estimates Under Conditional Ignorability" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "c018", "metadata": {}, "outputs": [], - "source": "# General pipeline for all types of estimators\ndef generic_pipeline(estimator, q_grid, name, n_seeds=5):\n ests = {}\n res = {}\n for q in q_grid:\n print(f'q={q}')\n ests[f'q={q}'] = []\n estimate_list = []\n for random_seed in np.arange(123, 123 + n_seeds):\n X, y, num_cols, splits, column_names = get_data(q, region=region, synthetic=synthetic,\n random_seed=random_seed)\n estimates, model = estimator(X, y, num_cols, splits)\n print(estimates)\n estimate_list.append(estimates)\n ests[f'q={q}'].append(model)\n estimate_list = np.array(estimate_list)\n res[f'q={q}'] = {}\n res[f'q={q}'][f'{name}'] = {}\n res[f'q={q}'][f'{name}']['point'] = np.mean(estimate_list[:, 0])\n if estimate_list.shape[1] > 2:\n res[f'q={q}'][f'{name}']['stderr'] = np.sqrt(np.mean(estimate_list[:, 1]**2)\n + np.var(estimate_list[:, 0]))\n res[f'q={q}'] = pd.DataFrame(res[f'q={q}']).transpose()\n return pd.concat(res), ests" + "source": [ + "# General pipeline for all types of estimators\n", + "def generic_pipeline(estimator, q_grid, name, n_seeds=5):\n", + " ests = {}\n", + " res = {}\n", + " for q in q_grid:\n", + " print(f'q={q}')\n", + " ests[f'q={q}'] = []\n", + " estimate_list = []\n", + " for random_seed in np.arange(123, 123 + n_seeds):\n", + " X, y, num_cols, splits, column_names = get_data(q, region=region, synthetic=synthetic,\n", + " random_seed=random_seed)\n", + " estimates, model = estimator(X, y, num_cols, splits)\n", + " print(estimates)\n", + " estimate_list.append(estimates)\n", + " ests[f'q={q}'].append(model)\n", + " estimate_list = np.array(estimate_list)\n", + " res[f'q={q}'] = {}\n", + " res[f'q={q}'][f'{name}'] = {}\n", + " res[f'q={q}'][f'{name}']['point'] = np.mean(estimate_list[:, 0])\n", + " if estimate_list.shape[1] > 2:\n", + " res[f'q={q}'][f'{name}']['stderr'] = np.sqrt(np.mean(estimate_list[:, 1]**2)\n", + " + np.var(estimate_list[:, 0]))\n", + " res[f'q={q}'] = pd.DataFrame(res[f'q={q}']).transpose()\n", + " return pd.concat(res), ests" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "c019", "metadata": {}, "outputs": [], - "source": "# Format results as a printable dataframe\ndef transform_res_to_df(res, q_grid, alpha=None):\n res_f = res.reset_index()\n res_d = {}\n for q in q_grid:\n key = 'all <= 11.6' if q == 0 else ('all' if q == 5 else f'q={q}')\n res_d[key] = {}\n for method in np.unique(res_f['level_1'].values):\n p = res.loc[(f'q={q}', method), 'point']\n if alpha is not None:\n s = res.loc[(f'q={q}', method), 'stderr']\n error = scipy.stats.norm.ppf(1 - alpha) * s\n l, u = p - error, p + error\n res_d[key][method] = f\"{p:.2f} ({s:.2f}) [{l:.2f}, {u:.2f}]\"\n else:\n res_d[key][method] = f\"{p:.2f}\"\n return pd.DataFrame(res_d)" + "source": [ + "# Format results as a printable dataframe\n", + "def transform_res_to_df(res, q_grid, alpha=None):\n", + " res_f = res.reset_index()\n", + " res_d = {}\n", + " for q in q_grid:\n", + " key = 'all <= 11.6' if q == 0 else ('all' if q == 5 else f'q={q}')\n", + " res_d[key] = {}\n", + " for method in np.unique(res_f['level_1'].values):\n", + " p = res.loc[(f'q={q}', method), 'point']\n", + " if alpha is not None:\n", + " s = res.loc[(f'q={q}', method), 'stderr']\n", + " error = scipy.stats.norm.ppf(1 - alpha) * s\n", + " l, u = p - error, p + error\n", + " res_d[key][method] = f\"{p:.2f} ({s:.2f}) [{l:.2f}, {u:.2f}]\"\n", + " else:\n", + " res_d[key][method] = f\"{p:.2f}\"\n", + " return pd.DataFrame(res_d)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "c020", "metadata": {}, "outputs": [], - "source": "if synthetic:\n def oracle_estimator(X, y, num_cols, splits):\n return (np.mean(moment_fn(X, true_f)),), None\n res['true'], _ = generic_pipeline(oracle_estimator, q_grid, 'true', n_seeds=1)\n res_d['true'] = transform_res_to_df(res['true'], q_grid)\n display(res_d['true'])" + "source": [ + "if synthetic:\n", + " def oracle_estimator(X, y, num_cols, splits):\n", + " return (np.mean(moment_fn(X, true_f)),), None\n", + " res['true'], _ = generic_pipeline(oracle_estimator, q_grid, 'true', n_seeds=1)\n", + " res_d['true'] = transform_res_to_df(res['true'], q_grid)\n", + " display(res_d['true'])" + ] }, { "cell_type": "markdown", "id": "c021", "metadata": {}, - "source": "### Nonparametric Model\n\nThe first specification leaves the short regression $g_s(D, X^o)$ unrestricted and targets $\\theta_s = \\mathrm{E}\\{\\partial_d g_s(D, X^o)\\}$ directly. Rather than estimating the conditional density derivative in $\\alpha_s$, which can be unstable, we learn the Riesz representer from the Riesz loss. Model selection is used for both the regression and the representer. Candidate procedures include expanded-feature lasso, forests and local polynomial forests for the regression, and penalized linear Riesz regression or ForestRiesz for the representer. The target and score stay fixed while the approximation class changes." + "source": [ + "### Nonparametric Model\n", + "\n", + "The first specification leaves the short regression $g_s(D, X^o)$ unrestricted and targets $\\theta_s = \\mathrm{E}\\{\\partial_d g_s(D, X^o)\\}$ directly. Rather than estimating the conditional density derivative in $\\alpha_s$, which can be unstable, we learn the Riesz representer from the Riesz loss. Model selection is used for both the regression and the representer. Candidate procedures include expanded-feature lasso, forests and local polynomial forests for the regression, and penalized linear Riesz regression or ForestRiesz for the representer. The target and score stay fixed while the approximation class changes." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "c022", "metadata": {}, "outputs": [], - "source": "# fully non-parametric average derivative estimation\ndef nonlinrr(X, y, num_cols, splits):\n get_reisz_fn = rr_fn_gen(moment_fn, num_cols, cv=splits, verbose=verbose)\n get_reg_fn = reg_fn_gen(num_cols, cv=splits, verbose=verbose)\n est = DebiasedMoment(moment_fn=moment_fn,\n get_reisz_fn=get_reisz_fn,\n get_reg_fn=get_reg_fn,\n n_splits=splits)\n est.fit(X, y)\n return est.avg_moment(alpha=.1), est" + "source": [ + "# fully non-parametric average derivative estimation\n", + "def nonlinrr(X, y, num_cols, splits):\n", + " get_reisz_fn = rr_fn_gen(moment_fn, num_cols, cv=splits, verbose=verbose)\n", + " get_reg_fn = reg_fn_gen(num_cols, cv=splits, verbose=verbose)\n", + " est = DebiasedMoment(moment_fn=moment_fn,\n", + " get_reisz_fn=get_reisz_fn,\n", + " get_reg_fn=get_reg_fn,\n", + " n_splits=splits)\n", + " est.fit(X, y)\n", + " return est.avg_moment(alpha=.1), est" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "c023", "metadata": {}, - "outputs": [], - "source": "res['rr'], ests['rr'] = generic_pipeline(nonlinrr, q_grid, 'dr')" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "q=1\n", + "(np.float64(-0.5929675270117792), np.float64(0.3590244758851252), np.float64(-1.1838475055676096), np.float64(-0.002087548455948829))\n", + "(np.float64(-0.5929675270117871), np.float64(0.35902447588512637), np.float64(-1.1838475055676194), np.float64(-0.002087548455954824))\n", + "(np.float64(-0.4279615680614352), np.float64(0.3692855377620416), np.float64(-1.0357291306931), np.float64(0.17980599457022955))\n", + "(np.float64(-0.5929675270117796), np.float64(0.3590244758851262), np.float64(-1.1838475055676116), np.float64(-0.0020875484559474966))\n", + "(np.float64(-0.5929675270117816), np.float64(0.3590244758851262), np.float64(-1.1838475055676136), np.float64(-0.002087548455949495))\n", + "q=2\n", + "(np.float64(-0.7706145225419935), np.float64(0.3079715728065326), np.float64(-1.2773939657170106), np.float64(-0.26383507936697637))\n", + "(np.float64(-0.7634759276458483), np.float64(0.30842861343725975), np.float64(-1.2710074493138168), np.float64(-0.2559444059778796))\n", + "(np.float64(-0.7746556601541333), np.float64(0.30878217156126997), np.float64(-1.282768975744129), np.float64(-0.2665423445641377))\n", + "(np.float64(-0.7694583558730154), np.float64(0.3086298439562792), np.float64(-1.2773210103447332), np.float64(-0.2615957014012974))\n", + "(np.float64(-0.7746556601541332), np.float64(0.3087821715612687), np.float64(-1.2827689757441267), np.float64(-0.2665423445641397))\n", + "q=3\n", + "(np.float64(-0.8003302929897983), np.float64(0.31790267633437735), np.float64(-1.323490635480487), np.float64(-0.27716995049910964))\n", + "(np.float64(-0.7081195309916972), np.float64(0.3358278989873699), np.float64(-1.2607787305869977), np.float64(-0.1554603313963968))\n", + "(np.float64(-0.800330292989798), np.float64(0.31790267633437846), np.float64(-1.3234906354804883), np.float64(-0.27716995049910753))\n", + "(np.float64(-0.8003302929898014), np.float64(0.3179026763343787), np.float64(-1.3234906354804923), np.float64(-0.27716995049911053))\n", + "(np.float64(-0.8003302929897973), np.float64(0.3179026763343783), np.float64(-1.3234906354804874), np.float64(-0.2771699504991071))\n", + "q=4\n", + "(np.float64(-1.8771853073446865), np.float64(0.4797536965392172), np.float64(-2.6671780560501315), np.float64(-1.0871925586392415))\n", + "(np.float64(-1.8259861668765283), np.float64(0.49690109549368927), np.float64(-2.6442149061140126), np.float64(-1.007757427639044))\n", + "(np.float64(-1.811838276250637), np.float64(0.4817598466715358), np.float64(-2.605134478517313), np.float64(-1.0185420739839612))\n", + "(np.float64(-1.8449310347465409), np.float64(0.478516130498998), np.float64(-2.6328859290177835), np.float64(-1.0569761404752982))\n", + "(np.float64(-1.8338625275288303), np.float64(0.488692919822914), np.float64(-2.63857516609468), np.float64(-1.0291498889629804))\n", + "q=5\n", + "(np.float64(-0.5776222999858567), np.float64(0.2385758243667754), np.float64(-0.9701495543809875), np.float64(-0.18509504559072582))\n", + "(np.float64(-0.5705826783871354), np.float64(0.24070747447487958), np.float64(-0.9666171228610853), np.float64(-0.17454823391318552))\n", + "(np.float64(-0.5705826783871422), np.float64(0.24070747447488072), np.float64(-0.9666171228610939), np.float64(-0.1745482339131904))\n", + "(np.float64(-0.5776222999858612), np.float64(0.23857582436677452), np.float64(-0.9701495543809906), np.float64(-0.1850950455907318))\n", + "(np.float64(-0.5705826783871346), np.float64(0.2407074744748801), np.float64(-0.9666171228610854), np.float64(-0.17454823391318386))\n" + ] + } + ], + "source": [ + "res['rr'], ests['rr'] = generic_pipeline(nonlinrr, q_grid, 'dr')" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "c024", "metadata": {}, - "outputs": [], - "source": "res_d['rr'] = transform_res_to_df(res['rr'], q_grid, alpha=0.05)\nres_d['rr']" + "outputs": [ + { + "data": { + "text/html": [ + "
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q=1q=2q=3q=4all
dr-0.56 (0.37) [-1.16, 0.04]-0.77 (0.31) [-1.28, -0.26]-0.78 (0.32) [-1.31, -0.25]-1.84 (0.49) [-2.64, -1.04]-0.57 (0.24) [-0.97, -0.18]
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" + ], + "text/plain": [ + " q=1 q=2 \\\n", + "dr -0.56 (0.37) [-1.16, 0.04] -0.77 (0.31) [-1.28, -0.26] \n", + "\n", + " q=3 q=4 \\\n", + "dr -0.78 (0.32) [-1.31, -0.25] -1.84 (0.49) [-2.64, -1.04] \n", + "\n", + " all \n", + "dr -0.57 (0.24) [-0.97, -0.18] " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_d['rr'] = transform_res_to_df(res['rr'], q_grid, alpha=0.05)\n", + "res_d['rr']" + ] }, { "cell_type": "markdown", "id": "c025", "metadata": {}, - "source": "### Partially Linear Model\n\nThe second specification is the partially linear model,\n\n$$Y = \\theta_s D + f_s(X^o) + \\varepsilon, \\qquad \\mathrm{E}[\\varepsilon \\mid D, X^o] = 0,$$\n\nin which the elasticity is constant." + "source": [ + "### Partially Linear Model\n", + "\n", + "The second specification is the partially linear model,\n", + "\n", + "$$Y = \\theta_s D + f_s(X^o) + \\varepsilon, \\qquad \\mathrm{E}[\\varepsilon \\mid D, X^o] = 0,$$\n", + "\n", + "in which the elasticity is constant." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "c026", "metadata": {}, "outputs": [], - "source": "# partially linear average derivative estimation, with doubleML\ndef dml_estimator(X, y, num_cols, splits):\n num_cols = [t - 1 for t in num_cols[1:]]\n model_y = reg_fn_gen(num_cols, cv=splits, verbose=verbose)(X[:, 1:], y)\n model_t = reg_fn_gen(num_cols, cv=splits, verbose=verbose)(X[:, 1:], X[:, 0])\n est = LinearDML(model_y=model_y(),\n model_t=model_t(),\n linear_first_stages=False,\n cv=splits, random_state=123)\n est.fit(y, X[:, 0], W=X[:, 1:], cache_values=True)\n p = est.intercept__inference().point_estimate\n s = est.intercept__inference().stderr\n l, u = est.intercept__inference().conf_int(alpha=.1)\n return (p, s, l, u), est" + "source": [ + "# partially linear average derivative estimation, with doubleML\n", + "def dml_estimator(X, y, num_cols, splits):\n", + " num_cols = [t - 1 for t in num_cols[1:]]\n", + " model_y = reg_fn_gen(num_cols, cv=splits, verbose=verbose)(X[:, 1:], y)\n", + " model_t = reg_fn_gen(num_cols, cv=splits, verbose=verbose)(X[:, 1:], X[:, 0])\n", + " est = LinearDML(model_y=model_y(),\n", + " model_t=model_t(),\n", + " linear_first_stages=False,\n", + " cv=splits, random_state=123)\n", + " est.fit(y, X[:, 0], W=X[:, 1:], cache_values=True)\n", + " p = est.intercept__inference().point_estimate\n", + " s = est.intercept__inference().stderr\n", + " l, u = est.intercept__inference().conf_int(alpha=.1)\n", + " return (p, s, l, u), est" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "c027", "metadata": {}, - "outputs": [], - "source": "res['dml'], ests['dml'] = generic_pipeline(dml_estimator, q_grid, 'dml')" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "q=1\n", + "(-0.4536878928585129, 0.37362069717731183, np.float64(-1.068239251714752), np.float64(0.1608634659977261))\n", + "(-0.4536878928585129, 0.37362069717731183, np.float64(-1.068239251714752), np.float64(0.1608634659977261))\n", + "(-0.429515660443789, 0.3729126041018483, np.float64(-1.0429023098366328), np.float64(0.18387098894905457))\n", + "(-0.4536878928585129, 0.37362069717731183, np.float64(-1.068239251714752), np.float64(0.1608634659977261))\n", + "(-0.4536878928585129, 0.37362069717731183, np.float64(-1.068239251714752), np.float64(0.1608634659977261))\n", + "q=2\n", + "(-0.8494168313481653, 0.3137014117762554, np.float64(-1.3654097362881363), np.float64(-0.3334239264081943))\n", + "(-0.8446550780035984, 0.3136164203479587, np.float64(-1.360508184484476), np.float64(-0.32880197152272106))\n", + "(-0.8449890320646015, 0.3136283098351859, np.float64(-1.3608616950116672), np.float64(-0.32911636911753595))\n", + "(-0.849754321614156, 0.3137135083038694, np.float64(-1.3657671235714466), np.float64(-0.3337415196568656))\n", + "(-0.8449890320646015, 0.3136283098351859, np.float64(-1.3608616950116672), np.float64(-0.32911636911753595))\n", + "q=3\n", + "(-0.7828372463451757, 0.33937247365239837, np.float64(-1.3410552905198163), np.float64(-0.22461920217053533))\n", + "(-0.7851971307470414, 0.3409418069717759, np.float64(-1.3459964985239559), np.float64(-0.22439776297012715))\n", + "(-0.7828372463451757, 0.33937247365239837, np.float64(-1.3410552905198163), np.float64(-0.22461920217053533))\n", + "(-0.7828372463451757, 0.33937247365239837, np.float64(-1.3410552905198163), np.float64(-0.22461920217053533))\n", + "(-0.7828372463451757, 0.33937247365239837, np.float64(-1.3410552905198163), np.float64(-0.22461920217053533))\n", + "q=4\n", + "(-1.8609830906964384, 0.5260984657599104, np.float64(-2.726338060235232), np.float64(-0.995628121157645))\n", + "(-2.0977343203341525, 0.5018784402735386, np.float64(-2.9232508931068306), np.float64(-1.2722177475614749))\n", + "(-1.7858036333632403, 0.5226547120869649, np.float64(-2.6454941321827623), np.float64(-0.9261131345437187))\n", + "(-1.820712719964298, 0.525419059879485, np.float64(-2.684950166276502), np.float64(-0.9564752736520944))\n", + "(-2.1564071980583783, 0.49950099455212066, np.float64(-2.9780132206133016), np.float64(-1.334801175503455))\n", + "q=5\n", + "(-0.8478402980113542, 0.25050019798628687, np.float64(-1.2598764572211603), np.float64(-0.4358041388015484))\n", + "(-0.8419240753558733, 0.25055408044216465, np.float64(-1.2540488633186588), np.float64(-0.42979928739308787))\n", + "(-0.8419240753558733, 0.25055408044216465, np.float64(-1.2540488633186588), np.float64(-0.42979928739308787))\n", + "(-0.8478402980113542, 0.25050019798628687, np.float64(-1.2598764572211603), np.float64(-0.4358041388015484))\n", + "(-0.8419240753558733, 0.25055408044216465, np.float64(-1.2540488633186588), np.float64(-0.42979928739308787))\n" + ] + } + ], + "source": [ + "res['dml'], ests['dml'] = generic_pipeline(dml_estimator, q_grid, 'dml')" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "c028", "metadata": {}, - "outputs": [], - "source": "res_d['dml'] = transform_res_to_df(res['dml'], q_grid, alpha=.05)\nres_d['dml']" + "outputs": [ + { + "data": { + "text/html": [ + "
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q=1q=2q=3q=4all
dml-0.45 (0.37) [-1.06, 0.17]-0.85 (0.31) [-1.36, -0.33]-0.78 (0.34) [-1.34, -0.22]-1.94 (0.54) [-2.83, -1.06]-0.84 (0.25) [-1.26, -0.43]
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" + ], + "text/plain": [ + " q=1 q=2 \\\n", + "dml -0.45 (0.37) [-1.06, 0.17] -0.85 (0.31) [-1.36, -0.33] \n", + "\n", + " q=3 q=4 \\\n", + "dml -0.78 (0.34) [-1.34, -0.22] -1.94 (0.54) [-2.83, -1.06] \n", + "\n", + " all \n", + "dml -0.84 (0.25) [-1.26, -0.43] " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_d['dml'] = transform_res_to_df(res['dml'], q_grid, alpha=.05)\n", + "res_d['dml']" + ] }, { "cell_type": "markdown", "id": "c029", "metadata": {}, - "source": "## Sensitivity Results for the Full Sample\n\nFor the full sample we report the short elasticity, the sensitivity bounds and their confidence bounds under the scenario $C_Y^2 = 0.03$, $C_D^2 \\approx 0.03$ and $|\\rho| = 1$, together with robustness values." + "source": [ + "## Sensitivity Results for the Full Sample\n", + "\n", + "For the full sample we report the short elasticity, the sensitivity bounds and their confidence bounds under the scenario $C_Y^2 = 0.03$, $C_D^2 \\approx 0.03$ and $|\\rho| = 1$, together with robustness values." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "c030", "metadata": {}, "outputs": [], - "source": "table = {}\ntable['point'] = '{:.3f} ({:.3f})'.format(res['rr'].loc[(f'q={5}', 'dr'), 'point'],\n res['rr'].loc[(f'q={5}', 'dr'), 'stderr'])\n\nest_list = ests['rr'][f'q={5}']\ntable['Bounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=None))\ntable['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=.05))\ntable['RV(v=-1.5)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=None))\ntable['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=.05))\ntable['RV(v=0)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=None))\ntable['RV(v=0,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=.05))" + "source": [ + "table = {}\n", + "table['point'] = '{:.3f} ({:.3f})'.format(res['rr'].loc[(f'q={5}', 'dr'), 'point'],\n", + " res['rr'].loc[(f'q={5}', 'dr'), 'stderr'])\n", + "\n", + "est_list = ests['rr'][f'q={5}']\n", + "table['Bounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=None))\n", + "table['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=.05))\n", + "table['RV(v=-1.5)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=None))\n", + "table['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=-1.5, leq=True, alpha=.05))\n", + "table['RV(v=0)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=None))\n", + "table['RV(v=0,a=.05)'] = '{:.3f}'.format(tvalue(est_list, value=0, leq=False, alpha=.05))" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "c031", "metadata": {}, "outputs": [], - "source": "table2 = {}\ntable2['point'] = '{:.3f} ({:.3f})'.format(res['dml'].loc[(f'q={5}', 'dml'), 'point'],\n res['dml'].loc[(f'q={5}', 'dml'), 'stderr'])\n\nest_list = ests['dml'][f'q={5}']\ntable2['Bounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=None))\ntable2['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=.05))\ntable2['RV(v=-1.5)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=None))\ntable2['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=.05))\ntable2['RV(v=0)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=None))\ntable2['RV(v=0,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=.05))" + "source": [ + "table2 = {}\n", + "table2['point'] = '{:.3f} ({:.3f})'.format(res['dml'].loc[(f'q={5}', 'dml'), 'point'],\n", + " res['dml'].loc[(f'q={5}', 'dml'), 'stderr'])\n", + "\n", + "est_list = ests['dml'][f'q={5}']\n", + "table2['Bounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=None))\n", + "table2['CIBounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=.05))\n", + "table2['RV(v=-1.5)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=None))\n", + "table2['RV(v=-1.5,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=-1.5, leq=True, alpha=.05))\n", + "table2['RV(v=0)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=None))\n", + "table2['RV(v=0,a=.05)'] = '{:.3f}'.format(dml_tvalue(est_list, value=0, leq=False, alpha=.05))" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "c032", "metadata": {}, - "outputs": [], - "source": "tabledf = pd.DataFrame({'Non-parametric': table, 'Partially linear': table2}).T[\n ['point', 'Bounds', 'CIBounds', 'RV(v=-1.5)', 'RV(v=-1.5,a=.05)', 'RV(v=0)', 'RV(v=0,a=.05)']]\ntabledf" + "outputs": [ + { + "data": { + "text/html": [ + "
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Non-parametric-0.573 (0.240)[-1.104, -0.043][-1.500, 0.350]0.0520.0300.0330.011
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" + ], + "text/plain": [ + " point Bounds CIBounds \\\n", + "Non-parametric -0.573 (0.240) [-1.104, -0.043] [-1.500, 0.350] \n", + "Partially linear -0.844 (0.251) [-1.299, -0.390] [-1.712, 0.022] \n", + "\n", + " RV(v=-1.5) RV(v=-1.5,a=.05) RV(v=0) RV(v=0,a=.05) \n", + "Non-parametric 0.052 0.030 0.033 0.011 \n", + "Partially linear 0.043 0.017 0.055 0.029 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tabledf = pd.DataFrame({'Non-parametric': table, 'Partially linear': table2}).T[\n", + " ['point', 'Bounds', 'CIBounds', 'RV(v=-1.5)', 'RV(v=-1.5,a=.05)', 'RV(v=0)', 'RV(v=0,a=.05)']]\n", + "tabledf" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "c033", "metadata": {}, - "outputs": [], - "source": "qgr = np.arange(1, 5)\n\npoint = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] for q in qgr]\nlower = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] - 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n for q in qgr]\nupper = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] + 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n for q in qgr]\nsens_band = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\nsens_band_unc = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n\nif synthetic:\n true_moment = [res['true'].loc[(f'q={q}', 'true'), 'point'] for q in qgr]\n\nplt.figure(figsize=(5, 5))\nplt.plot(qgr, point)\nplt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\nplt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\nplt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\nif synthetic:\n plt.plot(qgr, true_moment, label='true')\nplt.xticks(qgr)\nplt.title('Non-parametric')\nplt.legend()\nplt.grid(color='0.85', linewidth=0.6)" + "outputs": [ + { + "data": { + "image/png": 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3kWGYRMFiyCQMd8ANt8stchvJpUqRqSFxpNJxDMMw8YLFkEkal6rT5xSDoNzGUBAOl4tjGCbWsBgySZvb2OnpFIPLxTEME2tYDBlJwOXiGIaJJSyGjOTgcnEMw0QbFkNG8nC5OGkQDNLMcHDoc3rseyqeD7tOsH+F0Dr9r0Prh7/PMBOBxZBJ6XJxlNtIUaoUjGPWmqFVaSe9/4gb8aCbcv+LiBv9cOuHXof2N9z6Qz5rmPXD1xvp/f3uY7TjHW6d0Y53JLGLMeqAGu2udmToM2L+WUxqwmLIpHxuY6hcXGNPY18TYz+w1753RJEIMaIwMElJg7MB3Z5uFBgLuKADM25YDBnZlYtTQy0CcpjUg9qO7e3cizxDHqx6a6IPh5EQ3M6cYZiU8wbUOetQ01UDX8CX6MNhJAKLIcMwKQmV/SN3eJenr/wfw4wGiyHDMCkdaVzTXYM6Rx38AX+iD4dJYlgMGYZJeexuu5hLdHgciT4UJklhMWQYRjbBU1XdVWhwNCAQDCT6cJgkg8WQYRhZ0e5uF3OJPd6eRB8Kk0SwGDIMIzs8AQ8quyrR5Gzi6jWMgMWQYRhZQkUUqEpReWc5XD7OO5U7LIYMw8gaKsBAgtjS08JWooxhMWQYRvaQldjc24yKrgq4/W7Znw85wmLIMAzTT6+vF+X2crT1tvE5kRkshgzDMIP6ZVJR98rOSnj9Xj43MoHFkGEYZhicPqdI1Le77Hx+ZACLIcMwzH6Kfld3VYukfSZ1iXkLJ+oH98knn6CqqgrTp0/HwoULY7INwzBMrKB+mL32XuQZ85CuS+cTnYLE1DJ0uVw4+eSTcfbZZ+P555/H0qVLce6558Lv90d1m1jh8QWwo6ELTZ0ueH1cvolh5F70u9ZRi9ruWi76nYLE1DK8++678eWXX2Lbtm3Izc3F7t27MX/+fPzzn//ExRdfHLVtYoU/EERlW48YhEWvRoZRC5tBKx41avYyM4zc6PR0wul1otBUCJPWlOjDYaJETO/mzz77rLDqSNQIcnmuWLFCLI/mNvGiy+UTwri1xo73dzRjw55W7GjoZsuRYWRoJVLR73pHPRf9ThFiJoZerxc7d+7E7NmzI5bPmTNHWH7R2oZwu93o6uqKGLEmOCCOzmHF0eejNRiGSWU63B1c9DtFiJmb1OFwIBAIwGq1RizPyMhAZ2dn1LYhbrvtNtx8881DllMAjtlsnvB30CoUWJyVN/4NewFfLxBQAiqlEipF36NCMeFDYaKJH1DbYx47xsjkmlJeYk17DVQKFdRK/ruKJh6PB5WVlRPevru7e8zrxuzKpaWliUen0znk4ELvRWMb4oYbbsDVV1898Josw+LiYpSWlsJisUz4O2zfsQNrWxsRDRT9c442ow4ZRg0yDDqo1ayOiYBumj6rLyGfzaTuNfXBJ3xtheZCpKlHvl8xY4eEsKysDBNlPF7CmImhXq9HQUHBEFWn1wcccEDUtiF0Op0YyQw5TTtdPjEq28LE0aRDhoHFkWFSAXfAjYrOCmSnZSMrLQsKdgdJhpgG0Jx66ql4+eWX4fP5Biy+lStXiuUhKGo0PDhmLNukAiFxrGx14vNqO97b0YRPaM6xsRvNXTznyDCSL/rdyUW/pYQiSBnuMaK2thaHHXYY5s6di2XLluHFF19Ea2srNm/ePDAveNNNN+Hee++F3W4f8zZjMY3T09PFPONk3KRf7PgabzfWIxGTfeGWYyalc6Rp2a2aQi41Rh7XVAklsg19ViKTGDfpWLUgppZhUVERtm7dimOPPRY7duzAWWedhS1btkSIGuUQnn/++ePaJl6ovD04qPxJTK19DQXNH8LW+Q3SXC1QBPxxtRw/q+qIsBxbut0crcowEoCCa5p6moSV6PF7En04TKIsw0QRLcvw6y8+RfXmu4d5RwG3zoZebRZ6dVlw0dBmIqBSx9ly1MBm0rLlmCJWBJPa15SsRCrnZtPbEn0okqEyjpYhxwFPiCB07nYxrN27+pcp4Namw0UCqd8nkH6VNoaWo1cMsh5D4phh0g5UyVFTTgfDMEljJdY769Ht6Ua+KR8apSbRh8SEwWIYNYLQeexipDv29C9TwKMxhVmPfY8+tT6m4ljB4sgwSQsX/U5OWAxjShBab7cY6Y6KgaVetVGIohBJbaawJH1qY0zFkSaHLWka2Ix9blUrW44Mk/Ci312eLuQb8zlZPwlgMUwAGp9TDLOzamCZT53W52LttyLp0auJXhFg6rlh7/WKweLIMMkBiWGPtwcFpgKYtROvlsVMHhbDJEHt64XJVwNTT83AMr9KJyxHIY76bPRqM+HRpPdFz8RIHDP6K+Sw5cgw8bMSq7urYdPZkGvIhUqp4lOfAFgMkxiV3w1jb70YIQJKjYhidekzB+YgXVrrpHMhw8WxvBWR4mjSiDxHFQfkMExMi347vA7RGsqoie60CbN/WAwlhjLghdHVIEaIgFINlzYjbB4yS6R+BBUTTyNlcWSY+OMNeFHZVYlMfSZyDDlQTuL/MDM+WAxTAGXAB4OrWYwQJIRubUZEkA4JZnCCVfVZHBkmfrS52uDwOMRcokFj4FMfB1gMUxRFMAC9u1WMsKVwa60DFmTIipxIsQAWR4aJfdFvshKplBsV/uai37GFxVB2uZAdYqR37+5fpoBbY+mbexQjU4gkBe9EQxwzjTrYeM6RYSb4PzaIlt6WAStRH4McZaYPFkPZE4TO2ylGumPvwNnwaMwRc5DjLRYQLo5gcWSYSdHr70V5Z7mYR+Si37GBxZAZllCxAEtYsQCf2jAgjsLNqs+ET22asDimG7TIoGHSwpqm4WhVhtmPlUhFv6mcG0WcamNU6lGusBgyY/9j8fXA7KuG2Vk9sMyvTutL9eh3r5IFSVblWMSxo8cjxt4wcRTtqqhCDosjwwxLj68He+17kWvMRYY+g89SlGAxZCaFathiAdoB12qfQGbCraFcyLGJI1pYHBlmf0W/G5wNwkosMBZAo+Ki35OFxZCJOiq/Z9hiAftSPPqq6ri0thGLBYwmjtSVgyrkKDkFi5E5lKS/t3Mv8gx5sOrj3/M1lWAxZOJWLMDgahQjRFChgkuXsW8OUpcFt9aG4DDlqAaLIxXDsaSxODKMP+hHnbNuoDWUeoK5xHKHzxqTMBRBP9JcLWLYIooF2PpcrKI3JJWdyxxSLMAfHCqO6Wl9ViNbjowc6fJ2ocfeIwTRop14U3O5wmLIJGGxgDYxrNg5bLGAPqHMRCBsnoTEsb3HI4asxDEIBGnQ0yDFG/YtC/Q963+vb4XQOoG+t8Le37ctPUYsH7StWN6/fd/7kZ+7bz/924Y9j9g2bP992/a9QcfWt17/tmHHEDruId8XwCGmTOE+SMlrPM6i3zXdNbDqrMJ1ykW/xw6LISPZYgEUtTrQOLlfIP39uZCjieNUlQXVrU4E+m/u4hMG3WiFmPTf4ffdfCNvwOE3/vBt962zTwAihaZ/u5G2DX3uoOWhDw6Ebc/04dEHsGVPK+YUWGAzccqB3W2H0+sUwTUmbfRawaUyLIaMhBsnd4mR7igfWOpVmweCdEJCSb0iw8WxKMuEHa3dCT16Jvo4PT5sqmxHsc2AGblmqNVR6HUm8aLfVd1VyNBliDQMLvo9OiyGTEqh8XVD4+iGBZHFAkQeZH+gjjIzC8Ze6vqh6LeuFAiK+6aif1nf4wD9nQPEckX/Y//y0PPw7feNfutNEbnfvnVDn9vv15tkCy5mHzUdPWjpduOgAjNyLFy+rN3dPtAaiot+jwyLISOLYgEmGs6+XEhN4QyU1a1CchISV0WYYCqGXd4nxvu2GxBpKCMFV6w7WODDxTkk3GGfKZJZQpuEf/6+5QPbD+w/5Lod9KNArBs65vDjHO67hR9D+Pajnw9F5okRZ9Hl8+Pzajvy0/WYlW+BVi3vyURPwIOKrgpk6bNESTcu+j0UFkOGSSr6ZSM0mSieJ/BwJIK27AiYnNVwGEsiljd0utDq8GBmnhmFtjTInVZX64CVyEW/I5H3zyWGYVKEIEob3oa1KxSBvA+vP4Av6jrxaWUHXB4/5I7L7xJFv1t6WgaCuhgWQ4ZhUoYgCpvXI7t967Dvtjrc+GhPK6pae2QfikvxyM29zcJ16va7436lkhG2DBmGSSly2j9FQfNHwwqeLxDEN41d2FjeBofLB7nT6+tFub0cbb1tkDsshgzDpBy2rm9Q3PgOFIHhBY9aiW3Y04o9TQ4EqdafzIt+N/Y0orKzEl6/F3KFxZBhmJTE4qxEWf2bUI3gBiQN3NPiwMd7W2Hvka8IhHD6nKLot91lhxxhMWQYJmUxuJowpW4l1D7HiOs43D5sKm/DjoZu+Kk6g4zx9xf9ru6qFkn7coLFkGGYlIbK+B1Q+1/o3e0jrkMSWNnmFAE2FGgjd7q93WIusdPdCbnAYsgwjCwKL5TVreyvPDQyvV6/SMH4orYTXp+8JxN9QR9qHbWo7a6FP5D6KSkshgzDyAJVwIPS+jdhcewr1TcSdfZeYSU22l2QO52eTuyx74HDM7KrORVgMWQYRlYtwoob30OG/av9ruv2BbCt1o6tVXa4vWwlVnVXod5Rj0CKht+yGDIMIzOCyG/dgNzWLWNau6nbhQ93t6C2vQdyp8Pdgb32vejxpt65YDFkGEaWZNm3oahp7b6mkqNAyfpf1ndhS0U7etzyTtb39Bf9bnQ2plQ5NxZDhmFkCzWLLm1YA6V/bALX5vRgw542VLQ4ZV/Src3VJqxEqmKTCsS8a4XP58Pbb7+NqqoqTJ8+HUuWLBm1fcjmzZuxYcOGiGU6nQ6XXXZZrA+VYRgZYuqpQVnDKlTnLYNPvf/+h75gEDubutHY2Ys5hVaY0+Tb/McdcKOiswLZadnISsuSdGuomFqGDocDRx99NK666ips3LgRF110EVasWAGvd+RkThLOO+64A5WVlQOjuro6lofJMIzMSXO1YErda9B6usa8TafLh0/2tmJXowOB1IwpGV/R705pF/2O6U+a22+/HXV1ddi+fTtsNhtqamowe/ZsPProo7jiiitG3K60tBT33ntvLA+NYRgmAq23G1PrX0dl3jK49FljOjukgeWtDjR1uTCn0AKbUSvbs9rr7yv6nW3osxKlRkwtwxdeeAHnnnuuEEKiuLgYp556qlg+Gna7HU888QT+/e9/Y+fOof3JGIZhYoHK14sp9atgctaOazunx4dNFe34ur4LPl/qBJVMpOh3U0+TsBI9fg+kRMzE0OPxYM+ePZg5c2bEcnr99ddfj7ptT08P1q9fL0Rz3rx5uPrqq0dd3+12o6urK2IwDMNMBGXAi5LGNbB27R73ttXtPSJZv7lL3sn6Pb4eEVzT4epASrpJP/nkE2zatGnUdc455xwUFRXB6XSKsFur1RrxPlmJ3d3dI25/2mmn4brrroNW2+duWLt2rQi6OeaYY3DmmWcOu81tt92Gm2++echyCtoxm82YKCqNEUWHXjnh7ZnkRKPP4OuaYsTimhZTsIwqDX7l/oNqhtAD+NwK6NRKSQeVTJZmezNa0Qq1Uj2h80BGFcWNTJTRtGZSYtjR0bHfA3O5+n4RGQyGYQ+GrLbQe8NBlmA4ixcvxoIFC/DOO++MKIY33HBDhPVIn0EuWZp7tFgsmChff/Epaj97YMLbM8kJ3TT5uqYWsbym7emz0ZB1JDABTdOqlJiZb0GBdQKCmkIEFAHkGfOQrksf13akN2VlZRP+3PF4CcclhhQJSmMsUDpESUkJ9u7dG7GcXlOKxXggK7Gzs3PUz6LBMAwTbTI6v4LK34O6nOMRVKrGta3HH8D2Wjvq7TrMKbBArx3f9qlW9LvL04V8Y76wFGUVQHP66afjxRdfFHN6ocCYlStX4owzzhhYh1IuHnrooYHX33zzTcQ+vvrqK3z22WciRYNhGCYRpDsqUNrwFlQTDAqhtlA0l1jV2iPrZP0uT5eYS+z2jN19GS9iKs+///3vsWrVKhx//PE46aST8Nprr6GwsFDkHYZYvXq1SKMIJdXTe+TaJNdoa2sr/vnPf2Lp0qW4+OKLEU/sPR5RgolhGIag9k9T6l5HZcFy+NTGcZ8Uup9809iFBpGsnw6TPvmso3hZidXd1bDpbMg15EI1Tms7VsT0auTk5GDr1q3417/+JRLnSeguuOACpKWlDaxzxBFHRFSXoblBEkgK1CHh/O9//yvmDePNLSu/xuv/a0aO+lQUaO0o0NnFY6HWjkyNA0r5zokzjKwbBU+tfR1V+cvh1kUGB44Ve68XG/a04oAcE6ZmmaBQyrfot8PrQKGpEEbN+H9cRBtFMJUqrYZNmqanp4t5xokG0Jzz0AZ8WjV8WLBW4UM+CWSYSNKjVdULGQeOSQYOoEk94n1N/SodqvOWoictd1L7MevVmF2QDqtBAzmTqc9EjiEHykG/DKIRQDNWLZCnnT4GXvzZt7Bu02Zs2fwf1LutqPf0jQaPFZ6gGlXuLDEQ5vo2KN2RAtn/3KSSVvIpwzCjo/K7UVb/Bmpyl6DbVDrh09Xt8mFTeRtKM42YnmOCSqWQbdFvh8eBAlMBDJqRsw1iCYvhCFBOTI5RhbnGejFCBIIKtHhNfeLotqKuXySbPRb0BHTY48oVIxyLqgcF2k4hjORmLdB1IF/bCb1S3q1gGEbKKIJ+lDS+g/rso9CRPmvC+yHXXGWbU5R0m11oQZZJJ9ui35VdlaKUGxX+jnd+JovhOFEqgsjVdouxwFQzsNwbUKLJa4mwIul5q8+MLr8BXb0G7OjNj9hXlrp7iBWZq+mCRinjqr8MIymCKGj5CBqfE82ZCye1p16vH59WdqDQmoaZeWZo1EpZFv1u6W0ZsBLjCYthlCABK9LZxQjHFVCjwZM+RCQ7/QYhlDS2O6nWRR9KBJCj7RoI1gmJZbYI2km56V2GSQmyO7ZC7e9FffbR5Faa1L7q7L0iFWNWngV5Mk3W7/X3ot3VHtfPZDGMMeQKnaJvEyMch18r5h/rQiLZ/0iu1kaPVYzPw9bXKHzII1drmBVJYmlT93DQDsMkAbauHVD7e1CbuwSBSSaVu30BbKu1I7dTj4MKLNBp5GclxhsWwwRBQTXT05rFCEFxvZ3+tAgrksQyFLRT484UIxy90tMvkH1zkiGxtKjlXSiYYRKB2VmN0vo3RKSpfwyNgvdHU7cLbbvdmJlnQVHGvpQ0JvqwGCYR5F2xqnvFOMjYMLCccv/bfKZ9Itn/2OixwBXQotyVI0Y4ZpUrwooMPU9TjdxYmWGYyWNwNYvk/KqCFfBqTJPeHyXrf1nfKZL1ZxdYYNDxbTsW8FmVAJTgT3OGNA7Gvj5r/qACTR5LhBVJj61eM7r9euzszRMjHJvaGeFmpefkftUq/Qn4ZgyTmui8nZha919UiUbBkd6cidLm9GDDnjaRrD8lyzihwuHMyLAYShiVIogCHblHqYh51cByT0DVF7QTZkXS6PAZB8ZXPYUD6ysQEEI72IrM1XaJz2AYZvyofT2YUr8SNXknwWHY9/9tMviCQexs6kajKOlmhTmNb+HRgs9kCkJWXqm+XYxwevyaiIjWkDXpDOjR7LWIsc1ZMrC+Cn7k9Ue2hlfayVRzOTqGGXOj4IbVqMtZjE7zAVE7aZ0uHz7Z24qyLBOm5Zig5PiaScNiKCMMKi+mpbWIER60Qy7VcDdrSCzdQQ3qPDYx4Bhajq5wUI5kOpejY5ghKIIBFDV9ICJN26xzo3aGKBu5vNUhkvXnFFpgM/Y1RGcmBouhzKGgHYo8tagbMdPQGCGS7T7jEFcruV8jytFh5HJ0IbE0cjk6RvYEkde6USTnN2YeEdX5PqfHh00V7SjJMODAXDPUMi3pNllYDJkRRTJT4xRjrrEuIminxWseUkSgyWsesRxdOpWjCwvYoedUjk7H5egYmZFp/wJqXy/qco9DMMrtKqrbe9Dc5RYl3bLN8izpNhlYDJlxQQE1NI9I4xBUDypHlz7E1UopIVRtp7PHgG96IssrZWm6B9ysISuSgnbUCi5Hx6Qu6Y49olpNdd5JCKii263C5fPjs6oO5KfrMSvfAq0MS7pNFBZDJorl6DrEGFqOLqyouRDLdFGvlVJAaAwuR0eCODhHksvRMamEsbdORJpSX0SfOvrJ9A2dLrQ5PJiZb0GBTEu6jRcWQyYO5ehaxQjH4deh3t2f/iGCd2zisTfQV6aOxmeDytGRa3Vw+geXo2Okit7dhimUi5i/HB5tetT37/EHsL3Wjnq7DnMKLNBrk6OjfLLCYsgkBJPKjQMNzWKEB+3YfYY+cQwL3KGgHW9QjWp3phjhPSRD5ejCi5rTMKvdifliDDMOtN5uTKVqNfnL0KvPjsm5o6LfH+1pxfQcM0ozDZysPwIshkxSBe3YND1izB7UQ7I11ENyzOXoeiMjW7V9QTtcjo5JNlR+F8rqV6Em90Q4jPumDKIJlXT7prFLlHSbU5gOk55v/YPhM8IkPdS6KkfbLcZ87Osh6Qsq95WjGwjcSe8vR5eGnb008oeUo7O2dMPXuwJKBEWEu0IR7H8eDHve97m0TLwe9JzCEsTr/uX7nkcu3+/7EfsLiB8EoWOJXKd/u4hj7FtO86xj+h6D9jfW7SK/By3v227wsYR/vzj3ZZU8yoAPpQ1rUJdzLOyWA2P2OfZeLzbsaRUl3aZmmRDlgFZJw2LISBaKOqUoVBow71vuDqiF1RjuaqVhD5Wjs1O0anTqRTLDM5qIDvdjIFxch/uRQPV5B+9zn6gHUervwcnQCfe7dAmisHmdyEVsyVgQs0+hv/7dzQ40drkwuyAdVkN0I1qlCoshk3JQ/uJw5eicfi0aPemwTDsLzbtWiptCMEj2Ed2GFMIdG7p19z2PXE638mD/8gDdgoOIWC4exXN6P3w/oXX6t+t3/QZH2S58+b7jCttu2H0PXp+ej3SMoecYcXkg2Hesg/c5FsI/P2xhzNhZ7sVWzTJcWfA+crVhk8oSJKf9U6j9TjRkUaPg2H1Ot8uHTeVtKM00YnqOCSqZJ+uzGDKygSrhHJDWgqI8DWrr9s1JMmMn9AMgXJgDYxDRyB8SoefD/8AY8kNi0HaDhZvc5W85jkGT04I7a5bjsoIPIkoOSpGMzm+g9rtQm7MYwUk2Ch4NOq+Vbc7+km7pyDTJt6QbiyHDMGMmNHdJJFNHkyXfWo4/vFWBSncW7q07CT/M3YBF5kpIGYujAmW+XlTnnwy/KrYVZXq9fmypbEeRNQ0z8szQyDBZX37fmGGYlMOmV+Lqorcx31gNX1CFJxqPwZvtc4SlKWUMrkZMqVsJtS+sUn4MqbX34uM9rWi0uyA3WAwZhkmZ1mU/zV+PE61fidevty3AM83fEm5UKaPzdGBq7evQuyOrO8UKly+AbbV2bK2yw+2VT2lEaf+VMAzDhEGRpudkf47zsjeJGckNXdPw97oTRC9PKUMRpmX1r8PYu6+zTKxp6nbhw90tqG3vhRxgMWQYJuU4zroLlxd8AJ3CK3JN76pdhlavEVJG5fegtP4NMZcYL3yBIL6s78SWinb0uH1IZVgMGYZJSeYa6/Hr4jWihRjVuqVI00pXpuQbBRc3vocM+9dx/dw2pwcb9rShosUZ0xSZRMJiyDBMylKs68Bvit9CkbYdXf403FN7MrY5YlPyLH4Ekd/6MXLbtsT1U33BIHY2deOTva3o7k09K5HFkGGYlIZq3ZKFONtQJwq+P9JwHN7tmCX5SNOsjm0obFrXl/wZRzpdPiGIuxsdCKRQfA2LIcMwsmglRnOIx6bvFAn7L7UuxPMti+APSrvqirV7l6hpqvTH11ILANjb6sCGva3ocHqQCrAYMgwjC6hIwHnZm3F21qeicMC6zpl4uH6xaEAtZUw9NShrWAW1L/65gQ63D5sq2vF1fRd8fmmb2iyGDMPIqoLOSbZv8JO89aJh9Bc9RWIe0e6Lfrf5eJLmahGNgqk/YiKobu8Ryfot3dItlM5iyDCM7DjEXI2ri96BWeVCjTsTd9QsR63bBimj9XYJQdS7WhPy+b1ePz6r6sD2Gjs8PulNJrIYMgwjS6boW3F98VvI03SK1l531SzFV84CSBm1rxdT6lfB5KxN2DHUd7rw0e5W1EuspBuLIcMwsiVL48C1xasxI60R7qAGD9Yfj/Wd0yFllAEvShrXwNq9J2HH4PEHsL3Wjk8rO+Dy+CEFYi6Gfr8fr7/+Om666SZs3759TNv09vbi2WefxW233YaXXnoJPl/q5bQwDJM8rb2uKnwPR5j3ijZSzzYfgZdbD4nsxSjB5PzCprXI6vhfQo+j1eHGR3taUd3Wk/TJ+jEVwzVr1mDatGl49NFHcfPNN49JDDs6OnDYYYfh9ttvR2NjI2644QYsXrwYLpe0TG6GYaSDWhEQbZ++nbFNvH6nYzYeazwWnoAK0iWI3LbNyGv9JKFC5AsE8XVDFzaWt8Hh8slTDHNzc7Fu3TqsWrVqzNv85S9/gdPpxIYNG3Dffffh448/xldffYWHHnoolofKMIzMoUjTUzK/wI9yP4Ja4cdWRyn+VncSunx6SJlM+5coanoPikBi3ZX2Xq9I1t/b7EAwIDMxnD9/PkpKSsa1zcsvv4zvfve7MJlM4nVOTg6+/e1vC3cpwzBMrDncUoGfF7wLg9KNClc27qxZhkaPRdInPt1RjtKGt0Sx70TiDwK7mx3YUN6Kzh4vkomkCqDxeDyoqKjA9OmRE9j0eufOnSNu53a70dXVFTEYhmEmyoGGZlxXvBpZmm60+sxCEHf25Er6hBp7GzClnhoFOxN9KOh2+YTbdEdjN/xJkqw/rtIL77//PtavXz/qOhdddBHKysomdDDkHiXS09MjllutVjgcI3d6pkAbmpMcTFVVFcxmMyaKSmNE0aFXTnh7JjnR6DP4uqYYsbimRQDucwXwpw97sKNNh783nIyfL0rDCVO0kDJToYRXbUJQkRzzof5WQK1RQaWMLI3nVDgR9AVRWVk54X13d4+9CEFS1SEyGvv6jXV2dkYst9vtA27T4aAgm6uvvnrgNVmGxcXFKC0thcUycffG1198itrPHpjw9kxyQjdNvq6pRSyv6eVWFf7pPhKfO8rw10292Ll7I07N2C7mGKWKX6VDVf5S9OqTx9otsqZhZp4FanXfibXpbPC0eiZsXBHj8RKOSwxPOOEEMWKFVqvFlClTsHv37ojl9HrGjBkjbqfT6cRgGIaJ+n1J6ccleR/iv20OrOmYgzfaD0ar14QLczZCo0zCSJAxoPK7MaXuDdTkLkG3qRTJQK29V6RizMq3IDddL785w7Vr14o0ihBnn302XnjhhQG3aHNzM1auXCmWMwzDJALy4J2ZtRUX5HwCJQLY1H0A7q9fAqdfui5TRdCPksZ3YOvcgWTB5Qtga40dW6vscHn9qSOG5eXlItmeBvHKK6+I55SEP5IY/va3v4XBYMCRRx6JX/ziFzjqqKMwe/ZsXH755bE8VIZhmP1yTPoeXFnwPvRKD3b35onAmhbvyFM4yU8QBS0fIqftMyQTTd0u7GiIbyBk3OYMb7zxxmGXU0K9Xr/PJLbZbNiyZYsQzurqahEcc8YZZ0CtTqrpTYZhZMpBxgZcW7QGD9SfgCZvOu6sWY7L8j/A1LTEFMiOBtkdn0Pt70F99tF9CZcyJKYKM3Xq1AGrcCRIDGmEk5aWhgsuuCCWh8YwDDNhCnV2UeSbaplS1wtKzr8o92Mcaq6W7Fm1de0QgkjziEGl/IyPhM8ZJjN+tR5NmYejwzITzrR8+NSGRB8SwzBJglXdi2uK3sY8Yw28QTUeazwOa9oPQjA50uYmhNlZjbL6N6BKQKPgRCM/+R8HQYUarbZ5EcuUfh+0Xjt03k5ovZ3Qebqg9dmh9XRCFUhsdQeGYeKLXunDz/LX4cWWhfigcyZebTsUrV4zvpezGSqFNFXR4GoWyflV+cvh1Uh5PnR8sBiOk4BKDZcqCy591tCT6XMJoewTyU5ofV3Qeeh1l4jcYhgm9VAqgjg3Z4uoVvNS60J82HUg2nxG/CTvQ6Spkqvk2FjReeyYWvdfVOUtg0ufCTnAYhhFfGo9fOo89KTlRb4RBDQ+R78wdvZblV39wkkpJNL8BckwzD6W2HYgU+PAk43H4OueQtxduxRXFLyPDE2PJE+T2tcjLMTqvJPhNEi76fFYYDGMBwoId0Ofy6Eo8q2AH1pvd5hIdgqXq85nF12rGYaRDvNNtbimaA0erD8BdR4b7qhZLgSxRN8BqTYKLm14C3W5x6PTNBWpDIthggkqVXDrrGIMrqKn9HsjRLLP9UqPdvFHyjBM8lGqb++PND0B9R4r7qldiovzPsQ8Ux2k2ii4qPF9qLN60Gadg1SFxTCJCag0o8xP9gqR1IREMkw06Y+XYZjEkalx4tqi1Xik4Tjs6M3HQw2LcW72Fiy27pLoZQmKJsEanxONmYcLb1eqwWIoUXzqNDEwzPyk1tctXK0hgRQi6ekU85Y8P8kw8YGCZ64qfA/PNh+Oj7um4/mWw9HiNePsrM9F0I0UybRvFz/E63KPRVCRWpl5LIajIMlCDArAozGLMdz8pE4E7oRZkmJ+shMqnp9kmKhD6RVU0Dtb48BrbQvwnv0gUeT7x3kfQ6f0SfKMpzt2i+R8Cqyh6PpUIXW+SQzQa1Q4+aA8eHx+UUDW7fXD6w/A5Q3A46fXAbh9frh9AXh9AfiSPNuW5iddOpsYg6EO2OECGe525flJhpncj+plGV+KSNOnmo7E/5wl+GutAZcXfIB0tTST2429df25iMv6PFQpAIvhflAqAb1WJQagGXVdny8It98PjxDJfYPENPIxgGSb1fOrtOhVZaNXnz1siPWAFRmKeO1PD+H5SYYZG4vMlbCpnXiofjGq3Fki0pSKfhfoIvu3SgW9uxVTKBcxfzk82siG7FKExTCaJ1OtEAXFjWNorUiCSMPl88NDIur1w+0PwOP1C6tTWJ+0jj/xskll6GhQSboIgsEhaSGhYB6aaOf5SYaJZFpaC64rXi0iTZu9FtxVuwyX5q/DTEOjJE+V1tuNqWQh5i0d9oe0lGAxTBBatVIM034uAQWGkrXpHuSaJaEcbH36AnF20yoU8GgtYjhQHPlWwBc5PxnmelX5pekaYphokKvtxnXFb+Hh+sXY48rF/XVLcEHORhyVvleSJ1jl6xX1TKnAt8MYeR+QEiyGSQ4FbOmVKjF/uT/8/uDAHGbI8qTn1CTTQ+/1W530GGt7k6reu3QZYgzXZTtcIMOr8vD8JCMHTCoPflH4Lp5uOhJbHFPwTPORoqbpaZnbJBm4pxTJ+WtQl3Ms7JYDIUVYDFMIlUoBg0oNwxjctBTwExLNPuvSP+R1SFCjbW/6VTr0qnLQq88Z8p7a5xySNykCenzdPD/JpBQaZQA/zvsI2e3deLN9Ht7qmCsiTX+Qu0G8Jz2CKGxeJ6ZIWjIWQGqwGMoUjVopxn4JIkwk+wQzwk3b774l6zMablqf2iiGEwXDzE927cudDLMqNT6q/ZjckbwMMxxkBZ6W+T9kqR34V/MRwkrs8Bnws4J1MKnckjxpOe2fitSLhqyjJJWcz2LIjI4C0GmUYpj38+cSCCB2aShifjJdDCodEI4y4Ou3HvtEcqAyj5iflOYNhZEXR6bvRYbGKSrW0DziHTXLRKQpzS9KkYzOr6H296I2Z7FkGgVL4ygZWaWhkJh6x5GGEqD5SX0mXBjaaoaalEaKZBcKFBo4DUV90UlkUQZJ8wNiEJQuohDLSbCD/e5ZehSfJt7rez+0fN96fc8JtlSZ8UERpVTC7YH6E9DiteDOmmW4rGCtiECVIhZHBUp9LtTknySmRpIdFkNG0mko+0R0+DQUv1qPXhr63IFl09QmVBYsR0wRWhiMEFYhtuKRoGMl8cSAsAqRJTEdqC3btw0tE+uEhDlCpPu3D19viIj3v+7f3773hz72/SAIXx6533377/9eAz8eaL99z4dsH75e6LuFzsugzxXnpv87Kob5DqkO5RxSke//qz9e5CLeW3cSfpi7QeQoShGjqwFT6laisoCS85O7UTCLISOrNBR6TvM0GpUSwUAQ/mAwNrdYcVdXIKhQyeAWHkf6rXghpqEfHAggT2UU5zoVmmhTVZprit7GE41Hi2o1TzQeIwJrltm+lGSkqc7Tgam1r6M6f/mw1a+SBRZDRnZpKGq7Gktm5USIKIliIDQC9Ii+IZ73DX8gKIwiIaDiNT0P9C0LkA3Uv+3ANrScxLZvXbFcrINB+yRRjsMJSgUUJH9KcbHDT5lfqUVF4bdR0vi2qJgkdbRKPy7NX4+XWw8R9Uz/27ZACOL5OZtEvVOpofE5UVb/OmrylsI5uLlAksBiyMgeElG1MOUS+7NbiPKAYPZbrf2CGhLXcLEW7/ev17e8X8AjRF0+okwVUPYWnYGSxneQ5pLmPFs41NniO9mfIVvTjf+0LBKdL9q8Jvw0fx0MKun1M1X5PSitfxO1uSegy1SGZIPFkGGSSZSViRdlIZJRFOUBoQ0T3z6rOyDEd7AF3vfZE4PScioKTkVh83qkO6RZ0WUw1AOR+iM+3nCM6I1IJdwo0pSWSQ1F0I/ixnfRkHUk2q0HIZlgMWQYZkhUsDKJRDnklg7lsfoGrNiQWxpQe/cdK4Xy1+adAHe7DTntn6VE4M1cYx2uKVojapo2eKyiyPcVBe+jVN8O6RFEfuvH0PidaMpchGQhtbozMgyTUqJMUceU40rpOia9WgyrQQObUYsskw45Fj3yrHro1SrxOhyqglKTt0Sk3qQCJfoOEWlaqO1Alz8Nd9cuxTZHZM9SKZHVsQ2FTev6o5MTD4shwzDSRwEsKLbCmhaZ39plmoKKgtPgVRuRCmRoevDrojWYbaiDN6jGIw2L8V7HzGTRk3Fj7d4lappS4YxEw2LIMEzK1OY9tNQGky7SEqSCDOVFZ6JnmFq4UiRN5RWNgY+x7BKJJS+2LhIBNoGgQppFy3tqUFa/CmpfYrvZsBgyDJMyUL3dRWUZSBuUXkPd2CsLTkWnaTpSAUqvoDSLs7JoThRY2zkTDzUshisgTZdwmqtFNAqm/oiJgsWQYZiUguYYF5XZoBtUiD6oVKE2b3F/0IY0rahwKAH/ZNvX+EneOmgUPnzhLMI9tSfD7kuDFNF6u4Qg6l2tCfl8FkOGYVIOg06NhaUZ/akqkbTa5qM67yQElKPXz5UKh5qr8avCd2BWuVDjzhSRpnVuK6SI2teLKfWrYOqpi/tnsxgyDJOSmNPUYg5RPUwNs25TKSoKKbDGjFRgalorrit+C7maTnT4jLirdim+cuZDiigDXpQ0rIa+Y1d8Pzeun8YwDBNHKAXj4BLrsDc6ly4D5UVnoEefnOXBxku2xoHrildjelojXAGtyEn8sFOac6RUoD2tqzyun8liyDBMSpNt1mFu0fBuQ59aj8qCFbBbZiAVMKo8+EXhezjcvBcBKPHv5iPwSusCUeWHGR0WQ4ZhUp58qx4H5VuGfY8Ca+pyjkVj1hEpEVijVgRwUe4GnJrxP/H67Y45eLzxGHgCoxewlzsshgzDyIKSTAOmZY/cU6/NOhdV+UtTIrCGpklPzdyOi3I/ggp+fO4ow711J6Lbl/xNdhMFiyHDMLJhWq4JZZmGEd93GItRXngGPJrUCKw5wlKBnxe+B4PSjXJXDu6oXY5Gz/AWstyJeYZmT08Pnn/+eXz++ef44Q9/iEWLRi/MumbNGqxcuTJimcFgwJ133hnjI2UYRg7MzLeIJs/1ncNXPHHrrEIQi5vehbG3AVJnhqFJBNY8UHcCWr1m3FmzDD/LX4sDDc2JPjT5WIavvfYapk2bhvXr1+PBBx/Ezp0797vNli1b8MYbb2DmzJkDY/p0aUZEMQyTnMwttCJ7UGHvcPxqPaoKVqDDMgupQJ62SwjiFH0LegI63Fd3IjZ1TUn0YcnHMpw1axa++uor2Gw2PPXUU2PeLjc3F1deeWUsD41hGJn3jpxfbMWnVR3o6PEMu05QoUR9ztFwaW3Ib/1E8q2gLGqXSM7/Z9NR+NxRin80HY0WrxmnZGwXc4xyJ6aW4YwZM4QQjpempibccMMNuPnmm/HWW2/F5NgYhpE3orB3iQ0W/eg2Qbt1NqoKlsOv0kLqaJV+XJK3HifbvhSvV7UfjKeajoQvyOEjSXkGCgsLxTxhV1cXzj//fJx++umiK/ZIuN1usW74YBiG2R/UL3FhWQYM2tEF0WEoREXh6XBr0iV/UqlC3VlZW3F+zkYoEcDG7gNwf90SOP3SF/vJoAhSy+gxsmrVKqxevXrUdX7xi18MO8enUCjwzDPP4MILLxx1+5qaGhQXFw+8/uabbzB//nw8/PDD+NGPfjTsNjfddJOwIgezfft2mM0TjwrzeDwIqqTtGmGGwU9mAZ+ZlGKS1zQQDKLX4x9DX8AgND4nlEEvUoHPGry4/eMe9PqAIosSNx1rRJ4pOWwkpToNaq0ZWu3ERbq7uxvz5s1DZ2cnLBZL9MRw06ZNIsBlNM4880xh2U1UDIfjyCOPFPOPTzzxxIiWIY0QZBmSoI7lBIzG7vLd8KQPP5/ASBe1XQ2fNfHNRJnkuqYOlw+bKtrh9Y/shRIEg2IOMaPzK6QCtW6rKN1GNU2p2Df1SpyiT0zniHDSCxYhf9bZKCsrm/A+SAvS09PHpAXjCqA5/PDDxYg3JHQ+38h/6DqdTgyGYZiJYtL3Ffb+tLIdvtHqlykUaMg+sj+wZoOooyllinR2XF/8Fh6sP150vfhr7Un4Ue7HOMRcDTmRcHv4zTffxHXXXReRZxjOe++9h61bt2Lp0qUJODqGYeSE1aDBghLbmG6MHemzRPqFXyX9H+JWdS+uKXobcw218AbVeKzxWLzdcdAY3MapQ0zFkPIKKUUilCbx9NNPi+fPPvvswDqbN2/Go48+OvCaEvQPPvhgkaB/yimniPHLX/4S5513XiwPlWEYRpBp0mJekXVMVUqdafkiQd+tHX/UfLKhV/rws4K1OC59B4JQ4JXWQ/Fs8+HwB+WRdxHTPEOj0SiS5om///3vA8vz8/f12VqxYkXEHOM//vEP7Nq1S4gkRZRSsv5kfMYMwzDjJc+qhy+Qji/rO/e7rkdrQXnh6Shueg+mnhpJn2yVIojvZW9BjqYbL7UuxIddB6LdZ8RP8tcLsUxlxhVAIxXGM2k6GhxAk5pwAE3qEatrWt7iwK4mx9hWDgJ5bRuRaf8CqcA2RzGeaDxauE0Lte24suAD2DQ9KRtAk/A5Q4ZhmGRlarYJU7KMY1tZAdEGitpBUfUaqTPfVCPmES2qXtR5MnB7zXJUu6TvDh4J6V8xhmGYGDIjz4wia9qY16dGwZUFp8CvHvs2yUqZvg3XFb+FfK0dnX4D7qldii+cBUhFWAwZhmH2w5zCdORZ9GM+Tz1pedibIoE1WRonri1ajZlpDXAHNfi/+uOx1n4gUg0WQ4ZhmP2hgIgwzTKNvRqKV2MSkabdxlLJn1+DyosrC9/HkZY9CEKJ51sOx0sthyKQQpGmLIYMwzBjuVmKThc2pOs1Yz5fAZUa1Xkno9U6X/LnWK0I4Ps5n+C0zK3i9bv2g/Bow7HwBFKjtiGLIcMwzBhRU6eLMhtMunFkpSmApqxFqMs9XvKBNQoFsCLjS1yc9yHUCj+2OUvw19qT0eUbuws5WZH2lWEYhokzWrUSC8tsSNOMzyKym6ehovBU+FIgsGaRuRK/LHwHRqUble4sEWla75Z2Rw8WQ4ZhmHGi16iEIOrU47uF9upzsbfoDLh0mZI/59PSWkSkabamC+0+E+6qXYYdPXmQKiyGDMMwE8Coo8LeGVBTg8Bx4FObUFF4GrpMUyR/3nO13bi+eDUO0DejN6AVfRE3dE2FFGExZBiGmSCWNDUOLbFBNc6gyoBSjZrcE9FiWyD5c29SuYXLdKGpAgEo8XTTUXi97WDJFfmOaW1SqaNWqJFnzoM/4Icv6EMgGBDP/cH+Ef48SN1FGYaRGzaTFgcXW7Gt2o5xNXNSAM2ZC+HWZaCwaS0UEr6HaJQB/DjvI2S1ObC6Yy7ebJ+HFq8ZP8jZIN6TAiyGo0ANic1a85hOJJV4HSySJJ6+gG/I89A6YlkwtYvfMowcyLHoMafIiu219nFv22maCrfagtLGNVD74lf7M9ooFcAZWduQrenGv5uPwJbuKejwGkQnDJMq+ZuksxhGUTjJkqR/GGfaTUg8SSyF9TnoeUg8hXUa2LcsSJWBGYZJCgqsenh9FnzT2DXubV36LBFYU9L4DtJcLZAyR6XvRYbGiUcajsMeVy7urFmOKwveR462G8kMi2ESoFKqQP+0qrFXtyBGdNkO9zxsGYsow8SG0iwDvP4A9rSMsdNFGD61ERUFp6KweT3SHXshZWYZGnFd0Wo8UH8Cmr0W3FGzDJcVrBURqMkKi2EKiOh4CXfRjuTKJQt08Pssogyzf6blmoQgVrWP3+UZVKpRm3cC3O025LR/1tcXSqIU6DpxffFbopZplTsL99adhItyP8ZCcxWSERZDGYuoBmMvK0UMDiCKcOUOY4GG3qd/DCMnZhVYhCDWd7omtH1LxgJR5LuweS2UAS+kSrrahauL3sGTjUfjf85iPN54LFq9W7HU9qWoZpNMsBgyY0apUEKpUk5MRAcFDQ0nnoPfZxFlpMzcQis8/g60OtwT2r7LVAaP+tsoaVwDjc8JqaJT+nBp/jq81Hoo3rfPwmttC9DiNeH8nE1QKZLH8mUxZOIjogolNMrxiShF6IantIQCiIYsG/xcwiHqTOpAZUgXFFvxaVUHOnomFk3p0meivOhMFDe+DYOrGVJFqQjiu9mfikjTF1oW4uOu6Wj3GfHTvPVIUyWH5ctiyCR1hK5G0S+g45gaHS7NJdwC7ersgkVnEXOgJJ60fui5sEaDfdYs/Qt/j+dMmfGiosLeJTZsqmxDt2tiaVRUy7SSAmtaPkR6925JX4TjrTuRqXbi8cZj8E1PAe6qXSoiTTM0iU8pYTFkZJfm0qPsQb4pf0L7FiLZL4wklMM9Hyyww65L/wavFy7Kwb55Vp5zlT5qtQILSzOwqaIdPZ6JCWJQqUJt7mK4tDbktm2RdGDNPFMtfl20Bg/WH496j00U+b6i4AOU6tsTelwshgwzAZdvvBlOTEdaFi62EQI+kvCyNRxzdBolFpXZsKm8DS7fxAPKWm0Hw62xoqj5A0kH1pTo20Wk6YP1J6DOY8M9tUvx47wPMd9Um7BjYjFkGIlYuypFYpqohsRziNt4JEt2P9awcENj/xZ2qrml07TU6aLPQqRI04nSbSpFheY0lDS8DY0vuRPZR4Nco2QhPtZ4LL7uKcAjDYtxTtanWGLbgUTAYsgwzKgIS1hBHuf4ivFYXcj0XrNdGsElJn1fYe9PK9vhm0Qla5cuA+VFZ6Co8V0YXQ2QKmkqL64oeB/PNR+Oj7qm48XWRWj1mvGd7E/jfizctYJhmKS1hkmI1Uo1NCqNqNCkV+th0BjEMGlNsGgtSNeli/fNmrHVEU40VqMG80utk775+tR6VBUsh90yA1JGpQjigpyNODOTigwAH3TOxMMNx8Hlj28iIoshwzApQZG5CEa1EVIgy6TDvCIrGdyTggJr6nKORWPWEX1tMCSKQgEszfgaP8lbB7XCj+3OYtyyrRRtzvjNi7IYMgyTEpAVWWwuRpo6DVIgz6rH7AJLVPbVZp2LqvylCIwzlzfZONRcjasL34ZJ5UKFQ4/r3qxCIBCfuWMWQ4ZhUqrUYKm5FHqVHlKgKMOA6TmmqOzLYSxGeeEZ8EjEXTwSU9NacX3RWyg2unDlkflQUm+oOMBiyDBM6gmipRQ6pQ5S4IAcE8qyouPedeusQhCdaRPLo00WsrUO3H5oJRYUxs/tzWLIMEzKQUE3JIjjLQGYKGbmmVFojY571y8Ca1agwzILUkYZ5ylQFkOGYVISijAts5SJakRSYG5hOnLN0XHvBhVK1OccjYasIyUdWBNPWAwZhklZKB2jLF0igqgADi62ItM4vibfo9FunS3SL/zjbBwuR1gMGYZJaXQqHUosJQmr4DMelNTposSGdH303LsOQyEqCk+HW5MetX2mIiyGDMOkPJRuUWIugVICtzw1dboos8Gki54169ZaUVF0OpxphVHbZ6qR/H8ZDMMwUYCq1pCFqJDAHJpWrcTCUhvSNNGzZv0qHSoLlqM9fXbU9plKsBgyDCMbjBqjSMyXgiDqtSocWmqDVhXF27RCgYbsI1GffYwIsmH2wWeDYRhZYdaaUWgqlExhb+p0oY5ynkFH+kyRfkHWItNHTEOsqJr8qlWrsG7dOni9XixatAjnnXceVKrRTf+GhgY89thjqKqqwvTp03HppZfCZrPF8lAZhpERVNybul7UO+uR7FjS1DikxIbPqtrhj2JlMkrMpwT9ksa3ofN0QO7E1DI85phj8PjjjyMvLw9lZWW48cYbsWTJEiGMI1FTU4NDDjkEGzduxPz58/HGG2/gsMMOQ0cHXyyGYaKHTW9DniFPEqc0w6SNSmHvwXi0FpQXng6HoRhyJ6aW4TPPPIMpU6YMvD7jjDMwdepUvPXWWzjttNOG3eZPf/oTcnJysHLlSmFBXnzxxTjggANw77334uabb47l4TIMIzMy0zKFhdjcm/z9EHPT9ZgTSMcXdZ1R3W9ApUFV/jLktW1Epv0LyJWYWobhQkgUFRVBo9GgtbV1xG3IrXrWWWcNuFINBoMQTlrOMAwTbbIN2cjSZ0nixBba0kTptqijgGgDVZdznGwDa+L6rcllSvOIixcvHvb93t5eMV9YWloasZxel5eXj7hft9uNrq6uiMEwDDNWco25yNRnSuKEUVHvA7Ki0+liMHbLgagsOAV+ibTBSpib9KWXXsJrr7026jq/+93vMGvW0AKxGzZswNVXX41bb71VuEqHw+VyiUeTKfJCm81mIZQjcdtttw3rQqUAHNp2ong8HlRWVk54eyY54euaekTrmmoDWuE2TXZm6a2YmmuGN5oRNQPkQVE8DxqfA4qgH4lCqU6b9HXt7u6OjRhSZOeyZctGXScjI2PIsi1btmDFihW48sorcf3114+4LYmgUqkcEizT3t6O9PSRSwndcMMNQmhDkGVYXFwsLEqLZeLNM+kiUOAPk1rwdU09onVNyXNV66hFlyf5vUuqIPBlrR0NnX1GRLRR+n0oan4fZmcVEkF6wSLkzzp7Utd1PF7CcYnhwQcfLMZ4+Oyzz3DyySeLQJi77rpr1HVpPnHmzJn48ssvI5Z/8cUXmDt37ojb6XQ6MRiGYSaDQqFAkakINd016PaO3apICApgXqEVXn8HWh3uqO8+oFKjOu9k5LZtQZZ9G1KdmM4Zfv755zjppJOEEN5zzz3DrvPKK6+IPMIQ559/Pv7zn/+gsbFRvN65c6eIPqXlDMMwcRFEcxGM6vg1lp0oFOuyoNgKa1qM+jYqgKasRajLPT7lA2ti+u3INUo5hSRsF1544cAgAQyxfft2IX4hrrnmGpFfSBboKaecgm9961siJeOiiy6K5aEyDMMMoFQoRR1TKvCd7KiosHepDWZ97DLl7OZpqCg8FT4JnI+kzDP829/+Br9/6ATstGnTBp5TGsVBBx008Fqv1wtLcPPmzaiursaf//xnIY4MwzDxFsRScymquqrQ6x85gC8Z0IjC3hnYVNGOHo8vJp/Rq8/F3qIzUNrwNvTuNqQaMRVDKr22P+bNmyfGYDfF4YcfLgbDMEyiUClVwkKs7KyEOxD9eblootOQIFqxuaIdLl9sImJ9ahMqCk9DYfNaWBwVSCVS2wnMMAwzSdRKNUrTS6FVJn+3eIMuNoW9wwko1ajJPREttgVIJVgMGYZh9oNGqUGppRRqRUydadHrdFGaAbUihm2qFEBz5kLU5i1BUBG9nouJhMWQYRhmDGhVWpSll0lCEK1GDeaXWGN+g+80TUV54WnwqQ2QOiyGDMMwY0Sn0ok5RJUErKEssy4mnS4G49JnicCaXn02pAyLIcMwzDigdIsScwmUErh95ln1OCh/4lW4xopPbURFwanoNB0AqZL8V5NhGCbJMGgMwkKUgiAWZxowPSc2hb3DCSrVqM07Ac0ZC/smFSVG8l9JhmGYJMSoMYpKNQoJ3PgPyDGhLDM+FXVaMhagJu9EBJQxqooTI1gMGYZhJohZa0ahqVASgjgz34xCa3wqyHSZylBR8G14JVDSLgSLIcMwzCRI16WjwFggiXM4pyAdOWZ9XD7Lpc9EedGZ6NHnQAqwGDIMw0wSq96KfGN+0p9HqrU9v9iKDEN8Cgj41GmopMAa83QkOyyGDMMwUSBDn4FcQ27Sn0ulEjikxIb0GBb2DieoVKE2dzGaMg9L6sAaFkOGYZgokZWWhey05M+3U6sVOLQsA0Zt/AoItNoORnXeSUkbWMNiyDAME0VyDDnI1Gcm/TnVqpVYVGaDXh2/AgLdplJR6NurNiPZYDFkGIaJMnnGPNh0tqQ/r3qtCgvLbNCq4icFLl0GyovOgFOfXHOsLIYMwzAxoMBUgHRtuiQKex9aaotpp4vB+NR6VBUsh90yA8kCiyHDMEyMoBxEsyb5XIKDSTdoRFCNKo7xLRRYU5dzLBqzjkiKwBoWQ4ZhmBhBjcqLzcUwSiD5PMOkjUth78G0WeeiKn9ZwgNrWAwZhmFiLIhUx9QggTZHuel6zC6Mv2vXYSxCeeEZ8GhiX1R8JFgMGYZhYn2jVShFp4s0VXzKoU2GIlsaZubF37Xr1llRXng6nGmJCaxhMWQYhokDKqVKWIg6pS7pz3dZlhFTs2Lf6WIwfrUelQWnoMMyK+6fzWLIMAwTJ9RKNUrTS6FVxqcc2mQ4MM+EYlsCXLsKBepzjkZ3LlWsiR8shgzDMHFEo9SgzFImHpOd2QUW5FniU9h7MD59Rlw/j8WQYRgmzmhUGpRaSqFWxK8c2oRQAAcXWZFlSn7X7mRhMWQYhkkAOpVOCKJKEb9yaBPtdLGg2AprWvJbspOBxZBhGCZB6NV6EWWa7IKoUilElRqTLskt2UnAYsgwDJNADBqDEERlkt+ONaKwdwbSNMkt3BMluc8+wzCMTASRKtUokqAs2WjoNH2dLnTq1JOO1PtGDMMwEsSkNaHIXJT0gmjQqbGwNCOuhb3jAYshwzBMkmDRWlBgLECyY07r73ShSB1BZDFkGIZJIqx6K/KNydXrbzhsRi0OLrGmjIikyvdgGIZJGTL0Gcg15CLZyTbrMLfIilSAxZBhGCYJyUrLQnZaNpKdfKseB+UnrttEtGAxZBiGSVJyDDnI1Gci2SnJNGBadvwLe0cTFkOGYZgkJs+YB5vOhmRnWq4JZZnJ37NxJFgMGYZhkpwCUwHStfFvujteZuZbUGBNTGHvycJiyDAMIwEKTYWwJLAT/FiZW2BFtgQLe8e00JzX68Xzzz+PdevWieeLFi3CJZdcAr1+5F8O//nPf/DMM89ELDOZTGI/DMMwckWhUIik/Oruaji8DiRzYe/5xVZ8WtWBjh4PpEJMxfDII4/EQQcdhGOOOQYqlQp//etf8c9//hMfffTRiIK4e/du7NmzB3fffffAMo0mtaulMwzDjFUQqWxbVVcVenw9yV3Yu8SGzZVt6HL5ALmL4apVq5Cbuy9X5uSTT0ZRURHefPNNnHXWWSNuZ7Vaceqpp8by0BiGYSSJUqEUhb1JEHv9vUhW1GoFFpZlYFN5O5wen7zFMFwIiaysLGHldXV1jbpddXU1zjvvPGE9HnbYYcK1Gm3rMBAIwOMZ3YT3+/1wuVxR/Vwm8STTdaW/a/KaMMx4UClVKLH0CaLLnxx/y8OhFZ0ubNhY3g6Xz49kJq7Nqf7+979DqVRiyZIlI65D7x9//PFYtmwZ7Ha7cJc+8cQT+Pjjj6HTDT8p63a7xQixP7ElEayoqBCCOBo+n0+sx6QWyXZdyROSl5cnXGAMM1bUSrUQxMrOSngCyTs3p9eqsLDMhs0V7fD4R7/nJhJFMBgMjnXlf/3rX/sNZLntttswd+7cIcvfeecdnHLKKbj33ntx+eWXj7g9CZnFsi9iqq6uDjNmzMBf/vIX/PznPx92m5tuugk333zzkOXbt2+H2WyOWEZf1+l0il/kZLmS+I4Ercs3qNQjWa4rHUdvby9aW1vF36HBIN0crURDP3C1Wi3kCP0deQNeBDHmW3lC8AeCcHn9GKvi6NRqaBWKSV3X7u5uzJs3D52dnRG6Mmkx3LlzpwhwGY1vfetbyMyMrJhA0aQrVqzAb3/7W/zud7/DeDn22GMxZcoUPPXUU2O2DIuLi4c9ARTVSgE6BQUFSE8fPW+H9jmSNcpIl2S7rm1tbWhubsaBBx7ILtMJUllZibKyMsgVj9+Dis4K+ILJPTfX5vDgs8p2jMU+nJWdh4MMxkldV9ICus+PRQzH5SYlC43GeFi/fr2wCH/zm99MSAiJjo4OcaMYCbqxjfXmRvNFhFx/RTLJR8gipB9qPH/ITAStSotSS6mYQ0xmQcw0aTGvyIr/1dqTzo6NadI9pVCQRXj99dfjD3/4w7DrPPvss/je97438Pqxxx4TN4UQlIrx5Zdf4swzz4zqsSWDm4xhCP5bZKKBXq0Xc4gqRXIHZOVZ9ZhdkC6vAJrTTz9d/EfftGlTRKrE+eefLwaxa9curF69euC92tpa4RI94IADxFwKvb7vvvuEdckwDMOMTJo6bSDtIjAmZ2RiKMpIg8fvx64mhzzEkAJuQm7JcMJdniSKNM8YggJhrrnmGhH8Qu4jcssajUbIHZraHe5cqtVjv4QUPUs/TqJhiSTb8dC+6JjYzcjIHYPGIBLzqVJNMgfVTM02wesPoqLViZQXw+XLl+93HRLGwfOBNNF59NFHI548u6l6yDKfzwu1OjbVb84/vGRc61Pw0I9+9KMhN3uaGA79WNi8eTN++MMfor6+HhdffLGo+BOCImgPPvhgvPTSS5g/f/6on0U5eLfccov4MdPQ0CCuD0XzkqWfiOOhSfDbb79drEuBEvT3MWfOHBFdTO5zEtM//vGPItKZgqMYRu6YtCZRuq22uzapBXFGnhleXwC19sQXD+BC3RKisLBQ5MiFj3CrmcTpl7/8pXA9v/baa6ICUAgKXvrOd76zX+Ehfv3rX4v6sC+//DIcDgf+9Kc/iXndDRs2xP14KNeUyvpRas7jjz8ujocE79Zbb8Vzzz2HmpqaMZ8/hpETFq1FFPdOduYUpiPPopdX0j0TO0g0vvnmGyFAFClLFhMVKqC5WrLQaF5227ZtY9oXWVjXXXedKKxOUOk8svLvueceIUzxPJ7f//73Yt547969Ayk7tD/yHMTbe8AwUiNdl45AMIB6Zz2SFgVEhKmvuh2tjsQVD2DLUEI0NTWJIgLkJqTi55S2EiI07xZKGw3Nx1Fk7k9+8hM89NBDo3YLGczg9FN6TdHB8Twe2paE+Qc/+MGQ3FWGYcaGTW9DriGyNGayoRSdLmywpiWuKQOLoUSgkl0PP/ywqNtKhQ+oZuuJJ56Izz//XLxPiaWzZ8/GI488IlyHr776qhCoO++8EwsXLhR1YakSA4nXZZddNmopOnJf3n///cKSo/k6aqtFxdUpMTy0XTyOp729XSSkj5ZjyjDM/slKy0JOWk5Snyq1SoFDSm0w6RLjsGQxlAhnnHGGCEKx2WyijBy5LKnsHdV7Dc/JpDquhxxyCM4991xMnTpV5G1SfVeyxi666CIRgEIW3Isvvii2CZ/vC1lxtG+K8v3+978vKvU8+eSTwm1KJcNCFl+sjiecwdYlwzATJ9uQjUx9cntYtGqlqGOapol/riTPGUoYsrzCoycPPfRQ/O9//xsQECp4ftddd4mgli1btoh5OrLozjnnHOHynDZtGg4//PCB7SmClErmUUoLbUcjxFVXXSXyP0dLg5js8ZBghkNCSxbkjh07onTGGEbe5BnzxP/Fdnc7khW9pq+wd7cjvoLIlqFEoT9oysWkiM7hIAuM5vLI5Rm+Tfj8HYlVuGVIQjhSqgW5OUfrQRmN4xkMLSMLlVI8WlpaRvxshmHGTr4pH1adNalPmVGnFmkX8YTFUCKQS5FSE6gqD83TXXnllfjqq6+G7eRBuYGUevB///d/A9GXNKdHc3yU80cuSZq/GwkSvgceeEB8Vnl5uegtSfu44YYb4n48lNZRWlqKk046SaRXUBX6xsZGvPvuuzjttNNQVVU1ofPJMHKmwFggUi+SGaUyviUzWQwlwrXXXounn35auCKPOOIIIVLkWhwu1eFXv/qVKIxeVFQUYZlR3iAFrSxduhRnn332iJ9F71O/P0psp+pAFAxDwTTktoz38ZA1SfmNNEd59dVXIz8/X1i0NO946aWXCqEkKPl/PNVvGEbOkNelyFQEsya+1lcyM64WTlJhtLYd5PKjGz3Nf+0vtD/ZWv0w0SHZrut4/iaZ4ZF7C6eJQjmI1V3VcPqSoyRaODadDZ5WT9xaOLFlyDAMI1OUCqWoY5qmToPcYTFkGIaRMSqlCqXmUuhV8vZKsBgyDMPIHBUJoqUUOmXyTB/EGxZDhmEYBmqlWgiiRpm4kmiJhMWQYRiGEWhUGpRZyqBWyC8ym8WQYRiGGUCr0qIsXX6CyGLIMAzDRKBT6VBiKYFKEf8aoYmCxZBhGIYZAqVblJhLoJSJTMjjWzIMwzDjxqAxCAtRQR14UxwWQyaqUMum8BqmDMNIG6PGKBLzU10Q5TVDKmGoNyB1bwhBJYaodig1xs3Ly0OyQI1+t23blujDYBgmipi1ZhSaClHrqE3Z88piGGLlL4aeHL+fKkDH5sx/+75xrU59Ar/55hs89dRTA13gqZEuFcv+4osvYDKZYnOcDMMwANJ16aKWab2zPiXPB4uhhEhLS8OJJ5448Jo6PsyaNQuff/45jj322IHl1OXh8ccfR1NTE6ZPn45f/vKXEcVuf//734u+g2RVhnj00UdFsejbbrtNvKYmvH6/HzNmzMDrr78Oh8OBb3/72/jBD34QcUxr1qzBk08+KbpGUPPewXXf29rahGhTk9/MzEzRDmrJkiUxOT8Mw8QWm94mBLGxpzHlTjXPGUoYsgiVSiWKi4sHllGfv8WLF4sO8T/+8Y9RW1srWh5R38AQ5MbcuXNnxL527dqFrVu3Drym3oR//vOfhaiecsopot8giSdZoiHefPNNIZAHHnigaPz7yiuvDPQsDHHqqafik08+wfe//30h2CSy77//fozOCMMwsSYzLRM5aTkpd6LZMpQQJGghy7Cjo0NYcs8++6xo/RPiuuuuwyWXXII777xTvCaRmj9/vhC2Bx98cFyfl52djddeew0ajWZAIF966aUB65AsTGrqSw14idNPPx1Tp06NaE20ceNGYbkuWLBALKNtycpkGEa6ZBuyhYXY6mpFqsCWoYSg5rrUJJcGCRF1f6fHurq6gT595I4kayy8iSdZb5s2bRr35y1cuHBACAlytVKX+dBnkYW5fPnygfdpXTqmENSb75BDDsEvfvELvPrqq8JlSvD8JsNIn1xjLjL1mUgVWAwlOGdI48wzz8Rzzz0Hr9eLe+65Z6CRZSAQGNLEkiJP7Xb7uD9Pq9VGvCZhpf2HPovmB83myE7Zg1+/9957WLZsGe69917hzqXnIfFmGEba5BnzYNVZkQqwGEoYmi+ktIqampoBtyZZXTT/Fw7ND4a7UklUyYUZTnNz87g+mz7LaDSKVIpwBr+2Wq347W9/i3Xr1gkRJOvwpptuGtdnMQyTvBQYC2DRjt5FXgqwGEqYr7/+WrhFDzvssIFlP/zhD/G3v/1NzCmG5vmef/55XHTRRQPrzJ49Gx988AF6e3sHxJLmBsfLhRdeKD6LrMRQFCtFl4YLLAXgUFRqSBhpDI44ZRhGuigUChSZimDWRHqFpAYH0Eg0gKa7uxvbt2/HOeecI1InQtx6660iaOaAAw7AtGnT8OWXXwqBPP/88wfWueqqq/Dyyy+Ldch1SS7URYsWjft4KHCG3J4UNEOWJ4kfRbKGu0xprpIq0lDEKVmGZLk+9thjkz4XDMMkmSCai1DdVQ2nzwkpogim4M90slRonqyzs3PI/Bm5BykKk27eFOAxGhQkotMlR+fnvXv3iuMOQaJCYkbuyuEgVykFu5AgFhQUDHnf5/MJq5FcrTNnzkRlZSWcTqeIPA1ZnfSnQVZkiKqqKpG7GG6J0hwipWRQ8Aztp7q6WkSLhvZDkNhSwQAKAKK8RfqPk0iS6bqO92+SGR76+w3PpWUSQyAYQGVXJXp9fV6nyWDT2eBp9Uzquo6mBYNhMZTQTZOJDsl2XVkMJw+LYfLgD/hR1VWFXn+vpMSQ5wwZhmGYqKFSqkSnC50yeX5wjgUWQ4ZhGCaqqJVqlKaXQquMTM9KZlgMGYZhmKijUWpQaimFWiGNOE0WQ4ZhGCYmaFValKWXSUIQYyqGFI344osvinw0CsH/+c9/LqIKx1KAmopMU3eDn/3sZygvL4/lYTIMwzAxQqfSiTlElSJG7fCkIIbXXHPNQGcDqk9JET1U75LEbiQopP/II4+EWq0W+XPUt++II47gEl4MwzASJU2dhhJzCZRJ7IyMqe1KSdlUsisEFXWmFkP//e9/MXfu3BG3oT591F+PWLFihejJR/U3//rXv8bycBmGYZgYYdAYhIVIifkB9NU4TiZiKtPhQkjs2LFD1KYcSQgJEkuyJENQ01jqwvDOO+/E8lAZhmGYGGPUGEWlGgUSW3hjOGI+q0mVNaixK1U3oSoq1PWc+t4NB63T2toqurCHQ6+pssloSdQ0QoRqZTJMvPjwww9FObqzzz6bTzrDjIJZa0ahqRB1jjoEEZSmGFJNyaeeemrUdUjsQo1cidzcXNx+++2iJNcLL7wgOhjQHOBw1iG1IyIGl6SiLgsej2fEz7zttttw8803D1lO5cMGtxSiotFUioz2t7+yYBQAFC6yiYTmUsOLYIeg5rrhPQfJgqYaoOSSpnMfDtUjpfJqVDZtf1DtU2q/1NDQINzUFMwUfr7idTxU/Du8FyNVkaBt6G9oosTiur7++utDekmOFfpbpL9JOk/kCWEwoXNIVWgY6aAL6OAL+kZ836lwIugLTuq60n0sJmK4dOlSzJo1a9R1wjudEwaDAUcffbR4TjcKek5d16mTwmBIuOhGGmoCG4JeZ2RkjPiZVAj66quvjrAMqQB1aWnpiLVJqVdfeEmur9q+GrJfr8cLjXbfjT2azM7cV/NzLNCNls7bpZdeGrGcvkeo7+Dvfvc7vPLKK6IuKP04oEClzMy+5pubN2/GLbfcIhry7q8U2aeffiquFdU1pR8tDz30kDiXK1euHNg2XsdDgvzggw/i4osvHqi5+oc//AHHHHOM2DfVVk2GcmwkYnQsE9kviTMFjJEHhGuTTgwuxyZN2l3taHA2xLQcW0zEsKSkRIzJQP/hR+qdRzcUuvl+9tlnEcvpxhlubQ6GbkDJVGsyVlCNvbvvvnvY98jipXZKJFJkyZEr+plnnhERuWRx/+QnPxGiNpab7eWXX46jjjpKWG4EtXqaM2eOsPp//etfx/14SEDDP2f9+vU47rjj8Pnnn4vo5HA3+xtvvCGKidNnnnzyyRFi+eqrr4oWUhStHIL6LJJrPuTeXL16tSg+fsghh4j3qOg4WcWD/0PSDzSyBulvNnx/4WzcuFF8fzp+2gcVKmcYZh8Z+gxR3LuppwkpHUBDEaDhTWRJ1N566y2RcxjiySefFC60EJRfSLmJFGxDfPLJJ8I6oOVyh84l9Qd84oknhvxgaGlpERYPiQBBrkRyExN33nmnaNF0/PHHj+lzyIKjm3e4m5rEcbA1H6/jGQx5G4hwty1Z++S1uOOOO0RrK7JYjz322Ah3KB0nRTKHQ9ZueEsp+o6/+tWvRCsqcvGGXLlbtmwZWIesU/qsRx55RAgztdUiER78g+KMM84Q54UsWPKIUDsthmEiyUrLQnba8N13UiaAhn5hU1uanJwcYV3Q/NMVV1wR4dKkwBgSvBCXXXaZuJmRa41aFFHQDVkj1KNP7pALmQI1aH6EChiQK/PZZ58V1klWVpZ4pK735CKmQgVk3dCNm2721Gbp/fffF6JBP0YGBykNdnWH3/zJyiMrjK5FIo6H5ptDliE1LSZxob+TQw89NCKnldy6b7/9tnA5krV30EEH4f7778e11147rvNMliUVh8jPzxevyaq999578e9//1u8vv7664VFumrVKmF50t8wzXuGvgMJMH3HtWvXih8RoR8HHNjFMMOTY8gRFmKbK3KKLGXEkG5C5BajTup046R5p8FuMbL4wi1FurnQL26aY6IbKYkp3VjlDrni9uzZM5CuQjdruiE/8MADoqABCcCPfvQjfPe73xXrkiiQgFxwwQW46667xM2ZcjfpPQpiIjEars9hyHIjtyG5CMltTfsiK4wsQQr0oM+K5/GQGFNvxtCEOB1DKAiK5ifpRxcVd6DgLvpcgv5mqKExWX7jFUOyKENCSND3Io8GEfossiBDLliaOjjllFMGJuvpmMgdS25UOn80b019J0fqPckwDJBnzBOC2OHuSM3UChJBmm8a7zxkXl6eGEwf1Ck+HHLT0ZwY5WWS+BD/93//h6effhr19fVivopu2jSv953vfEeIA7n9aO6VUl3IPU3zdjSPF4JceRSlSTd2ihYly4fmdykQhqx1KogQEptYHM/vf//7Mc0ZknVI1isdA3kNQi7ZoqKiiO3IIiVBGi+Dg67obzgUzUyfRc8HCzd9dqjUIP1wIHfsb37zGxFBe/DBB+N73/ue8IqER9oyDBNJgalACGKnpxPxJvmrpzIjQsJE5epCkFuSrDGCXNK33nqrSE2gwBIK+KAu8yHhIvckBbKELC6C1gtBYkOuzxAU7ELzfLE8nrFCgSi0DQksQcJKIjM4MIu+W7iFR8dHVmY44d95LJAw035IFAe7VsMhi5fmE8laJNGnlBNy9950003j+jyGkRuFpkIEu+Off5i8heKYCAYHX1BOGt1kyaU3HHTzJcuELBZyZZpMpoEAFpq/I5Gg98jiCo1Q0Ay5p8PzOinVgqy6cHGMxfGMFRIwctGS2z0kuhSME54D29PTI/JaKbglBHkgwuvikjVJlux4ICGk4Jp//etfA8tI2On8hCABDBWXp3Qhmu+m4wsFhTEMMzLkWaEqNVStJp6wZSgRKDKXRIrcmHSjp5sxud+Gmw977bXXhFVEQSYhfvrTn4ruISR4FCEZHiAzXM4WRUJSlC8Fffzzn/8U7sgzzzwzIccTHkBDQkMuSJp7pqCZ8OOh3EM6ZrJgaR0S3PDjueSSS3D44YcLtyxZluQGHmwpjgWKWKXPOu2008R3pujn8DxYsrgpmIimB2jOsLa2VgT9DI5kZRhmZEFM16WjA/GbP2QxlAj/+Mc/hIuRct9oTovm+qgIwnCQy5Hm4MJTD0hMKBqShO6jjz4SkZcjQTd6ChAhkSIrjj5zcJ5nvI6H5jEpcCfkzg0JHFlblPIRgoSHLK///Oc/Yl1Kjzj33HMj1qEC8BSoQ9+LglwoLYQs2nBXMYkpBcmEQwIavh+KiqU5VDpH5J4li5SsQ5obJUgYKb+QxI8sURJemoOlYDCGYZITRZDKX6QYZM1QoAa1jBqpAg3dmPaX8B2LSiVM4km26zqev0lmeLgCTWpSWVk56Qo0I2nBYHjOkGEYhpE9LIYMwzCM7GExZBiGYWQPiyHDMAwje1gMGYZhGNkjWzFMwSBaRqIMTuVgGCb+yC7PkPLCKN+NymlR4eTRut1TFRYWzdQjWa4rHQMdC/0tUtHvUFNkhmHij+zEkEp3UUkwqgpCOSyjEerQwKQWyXZdqT8jlYoLb0TMMEx8SZ47QhyhKibUdJbKZo0GVScZrc8eI02S6brSjzMS5tE8FAzDxB5ZimHoJkRjf+twRZDUg68rwzCDYb8MwzAMI3tYDBmGYRjZw2LIMAzDyJ6UnDMMhc1TxfLJQL3zJrsPJvng65p68DVNTboneQ8ObTuWVCp1qp5Aori4ONGHwjAMwySBJlArJ9n1M6SKHtRo1Ww2TzhknX5RkJhSN/f99cFipANf19SDr2lq0hWFezDJGwlhQUHBfvN4U9IypC9NifXRgC4Ci2Hqwdc19eBrmppYJnkP3p9FGIIDaBiGYRjZw2LIMAzDyB4WwxHQ6XS48cYbxSOTOvB1TT34mqYmujjfg1MygIZhGIZhxgNbhgzDMIzsYTFkGIZhZA+LIcMwDCN7UjLPMBps3rwZ5eXlWLZsGaxWa6IPh4lCQ99t27ahsbERBx54oBiM9Glvb8enn34q2nIdfPDByMrKSvQhMVHC7XbjtddeQ2ZmJk488UTEGg6gGcTKlSvxxz/+EU6nE7t378bWrVsxf/78mF8IJna8+uqruO6660TybV5eHj766COccMIJeO655zhaWMJce+21+M9//oM5c+aIKiOff/45brnlFlxzzTWJPjQmClx11VV49NFHsWDBAmzcuBGxht2kg+jt7cWTTz6JV155JeYnn4kPFDD93nvvCQti1apV+PLLL7Fu3Trcc889fAkkDP1IJe/Nm2++iQ8//BAPPPAAfv3rX4tSjIy0ef3117F27Vqcd955cftMFsNBfPe73xW/RJjU4ayzzkJJScnAayrVd/jhhwu3KSNdLrjgAqjV+2Z6ZsyYIR4dDkcCj4qZLLW1tfjZz36Gf//739Dr9YgXPGfIyLIA8JYtW4QbhpE2X3/9NbZv3y7mgsmldv311/N8sITx+/04//zzhYU/b968uH42iyEjO5fpJZdcIn5xXnnllYk+HGaS7Ny5U8wJU2cDmuKI9w2UiS4050sVZ371q18h3rAYMrLiiiuuwPvvv48PPvgAGRkZiT4cZpKceeaZYhA0z3/OOedg5syZOOSQQ/jcSoy9e/fiL3/5C+644w4RGBVa1tbWhueff15ElMYyWpjFkJEN5BZ94YUXRDDN3LlzE304TAzmhg0Gg4gWZjGUHgqFAmeffbZIawtRUVEBu90uUiwoloPFkGEmyS9+8QuRSvHuu++KfDRG2nR0dAjhCy/ivGPHDpESRQ1hGekxdepUYQGGQ4E0FOg2eHksYMtwELt27RL5ShTRRKxZs0b8J6NfmpyoLd15iPvvvx+/+c1vxLWkQWRnZ2PJkiWJPjxmAtAc4YUXXihcpFOmTEF1dTUeeeQRnHTSSTjttNP4nDLjhsVwEOSjJpOcOPfcc0XSPQ1K2GYxlCYULEPXklwuNELQ3BKLoTShQJnVq1fj6aefxvr164X7jKJJV6xYIdxtTGqwaNEi8aM1HnAFGoZhGEb2cNI9wzAMI3tYDBmGYRjZw2LIMAzDyB4WQ4ZhGEb2sBgyDMMwsofFkGEYhpE9LIYMwzCM7GExZBiGYWQPiyHDMAwje1gMGYZhGNnDYsgwDMPIHhZDhmEYBnLn/wFEzinJpGrJFwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "qgr = np.arange(1, 5)\n", + "\n", + "point = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] for q in qgr]\n", + "lower = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] - 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", + " for q in qgr]\n", + "upper = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] + 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", + " for q in qgr]\n", + "sens_band = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\n", + "sens_band_unc = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n", + "\n", + "if synthetic:\n", + " true_moment = [res['true'].loc[(f'q={q}', 'true'), 'point'] for q in qgr]\n", + "\n", + "plt.figure(figsize=(5, 5))\n", + "plt.plot(qgr, point)\n", + "plt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\n", + "plt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\n", + "plt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\n", + "if synthetic:\n", + " plt.plot(qgr, true_moment, label='true')\n", + "plt.xticks(qgr)\n", + "plt.title('Non-parametric')\n", + "plt.legend()\n", + "plt.grid(color='0.85', linewidth=0.6)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "c034", "metadata": {}, - "outputs": [], - "source": "qgr = np.arange(1, 5)\n\npoint = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] for q in qgr]\nlower = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] - 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n for q in qgr]\nupper = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] + 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n for q in qgr]\nsens_band = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\nsens_band_unc = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n\nplt.figure(figsize=(5, 5))\nplt.plot(qgr, point)\nplt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\nplt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\nplt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\nif synthetic:\n plt.plot(qgr, true_moment, label='true')\nplt.xticks(qgr)\nplt.title('Partially linear')\nplt.legend()\nplt.grid(color='0.85', linewidth=0.6)" + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "qgr = np.arange(1, 5)\n", + "\n", + "point = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] for q in qgr]\n", + "lower = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] - 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", + " for q in qgr]\n", + "upper = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] + 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", + " for q in qgr]\n", + "sens_band = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\n", + "sens_band_unc = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n", + "\n", + "plt.figure(figsize=(5, 5))\n", + "plt.plot(qgr, point)\n", + "plt.fill_between(qgr, lower, upper, alpha=.4, label='5%-95% CI')\n", + "plt.fill_between(qgr, *list(zip(*sens_band)), alpha=.6, label='Bounds')\n", + "plt.fill_between(qgr, *list(zip(*sens_band_unc)), alpha=.2, label='5%-95% Bounds')\n", + "if synthetic:\n", + " plt.plot(qgr, true_moment, label='true')\n", + "plt.xticks(qgr)\n", + "plt.title('Partially linear')\n", + "plt.legend()\n", + "plt.grid(color='0.85', linewidth=0.6)" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python (causalmlbook)", "language": "python", - "name": "python3" + "name": "causalmlbook" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.8.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.15" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/T-ADML/r-ADML-401k.irnb b/T-ADML/r-ADML-401k.irnb index f75e6aec..3303bfbf 100644 --- a/T-ADML/r-ADML-401k.irnb +++ b/T-ADML/r-ADML-401k.irnb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "170f2037", "metadata": { "execution": { @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "267a6538", "metadata": { "execution": { @@ -57,7 +57,16 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dml.sensemakr 0.2.2 \n", + "sensemakr 0.1.6 \n" + ] + } + ], "source": [ "suppressMessages({\n", " library(sensemakr) # attached first: we only need add_bound_to_contour()\n", @@ -91,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "641048b9", "metadata": { "execution": { @@ -104,7 +113,15 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "households: 9915 covariates: 8 eligible: 37.1% \n" + ] + } + ], "source": [ "data(\"pension\")\n", "\n", @@ -137,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "f4dad1cf", "metadata": { "execution": { @@ -150,7 +167,35 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Partially Linear \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 26.474%), treatment (ranger, R2 = 11.599%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 9114 1323 6.888 0.00000000000567 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "set.seed(1)\n", "dml.401k.plm <- dml(y, d, x, model = \"plm\", cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", @@ -175,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "c886bdf1", "metadata": { "execution": { @@ -198,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "e281ccde", "metadata": { "execution": { @@ -211,7 +256,46 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Debiased Machine Learning\n", + "\n", + " Model: Nonparametric \n", + " Cross-Fitting: 5 folds, 5 reps \n", + " ML Method: outcome (yreg0:ranger, yreg1:ranger, R2 = 25.572%), treatment (ranger, R2 = 11.48%)\n", + " Tuning: dirty \n", + "\n", + "Average Treatment Effect : \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "ate 8198 1196 6.852 0.00000000000726 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Group Average Treatment Effect: \n", + "\n", + " Estimate Std. Error t value P(>|t|) \n", + "g.ate.q1 4646.2 794.2 5.850 0.00000000492 ***\n", + "g.ate.q2 2729.4 1296.7 2.105 0.035305 * \n", + "g.ate.q3 6854.3 1838.0 3.729 0.000192 ***\n", + "g.ate.q4 18406.6 4061.6 4.532 0.00000584674 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Note: DML estimates combined using the median method.\n", + "\n", + "Verbal interpretation of DML procedure:\n", + "\n", + "-- Average treatment effects were estimated using DML with 5-fold cross-fitting. In order to reduce the variance that stems from sample splitting, we repeated the procedure 5 times. Estimates are combined using the median as the final estimate, incorporating variation across experiments into the standard error as described in Chernozhukov et al. (2018). The outcome regression uses Random Forest from the R package ranger; the treatment regression uses Random Forest from the R package ranger." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "set.seed(1)\n", "dml.401k.npm <- dml(y, d, x, groups = g1, model = \"npm\",\n", @@ -233,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "5c41f860", "metadata": { "execution": { @@ -257,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "522bd397", "metadata": { "execution": { @@ -270,7 +354,23 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], "source": [ "p1 <- ggplot(df, aes(x = groups, y = estimate)) +\n", " geom_line() +\n", @@ -286,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "90b555b3", "metadata": { "execution": { @@ -299,7 +399,17 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RV_alpha (alpha = 0.05, threshold theta = 0)\n", + " partially linear : 5.6%\n", + " nonparametric : 4.6%\n" + ] + } + ], "source": [ "rv.plm <- robustness_value(dml.401k.plm)\n", "rv.npm <- robustness_value(dml.401k.npm)\n", @@ -328,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "8a03f2af", "metadata": { "execution": { @@ -352,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "eb62473e", "metadata": { "execution": { @@ -372,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "b6c06ec7", "metadata": { "execution": { @@ -393,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "6a24c23a", "metadata": { "execution": { @@ -406,7 +516,52 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 2 × 5
ModelShort ATEBias boundATE bounds95% conf. bounds
<chr><chr><chr><chr><chr>
Partially linear9,1144,201[4,918, 13,311][2,725, 15,573]
Nonparametric 8,1984,529[3,644, 12,752][1,618, 14,805]
\n" + ], + "text/latex": [ + "A data.frame: 2 × 5\n", + "\\begin{tabular}{lllll}\n", + " Model & Short ATE & Bias bound & ATE bounds & 95\\% conf. bounds\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t Partially linear & 9,114 & 4,201 & {[}4,918, 13,311{]} & {[}2,725, 15,573{]}\\\\\n", + "\t Nonparametric & 8,198 & 4,529 & {[}3,644, 12,752{]} & {[}1,618, 14,805{]}\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 2 × 5\n", + "\n", + "| Model <chr> | Short ATE <chr> | Bias bound <chr> | ATE bounds <chr> | 95% conf. bounds <chr> |\n", + "|---|---|---|---|---|\n", + "| Partially linear | 9,114 | 4,201 | [4,918, 13,311] | [2,725, 15,573] |\n", + "| Nonparametric | 8,198 | 4,529 | [3,644, 12,752] | [1,618, 14,805] |\n", + "\n" + ], + "text/plain": [ + " Model Short ATE Bias bound ATE bounds 95% conf. bounds\n", + "1 Partially linear 9,114 4,201 [4,918, 13,311] [2,725, 15,573] \n", + "2 Nonparametric 8,198 4,529 [3,644, 12,752] [1,618, 14,805] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "dollars <- function(v) formatC(round(v), format = \"d\", big.mark = \",\")\n", "\n", @@ -451,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "9b0a3776", "metadata": { "execution": { @@ -464,7 +619,23 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], "source": [ "bds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0)),\n", " c(\"lwr.bound\", \"upr.bound\"))\n", @@ -496,14 +667,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "752bb83c", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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uBqkBAlcYqjSqjQegGhabQWqAwBWGKo1q4wGXZrkZpAYIXGGo0qg2HmBpVphBaoDAFYYqjWrjAZVmjRmkBghcYajSqDYepIZgpAYDuEqj2ngApVllBqkBAlcYqjSqjQdMmnVmkBogcIWhSqPaeICkWWkGqQECVxiqNKqNB0SaFZudpAYcXGGo0qg2HgBpVptBaoDAFYYqjWrjwT7NejNIDRC4wlClUW08ANSw2gxSAwSuMFRpVBsP5mkiBg1SAwSuMFRpVBsP1mlizCA1QOAKQ5VGtfFgroYIM0gNELjCUKVRbTxYp4kxg9QAgSsMVRrVxoNxmigzSA0QuMJQpVFtPNimiTOD1ACBKwxVGtXGg2maSDNIDRC4wlClUW08SA3BSA0GcJVGtfFgmSbWDFIDBK4wVGlUGw+GaaLNIDVA4ApDlUa18WCXJmqzk9SAgysMVRrVxoNZGgMzSA0QuMJQpVFtPFilsTCD1ACBKwxVGtXGg5kaDMwgNUDgCkOVRrXxYJTGZNAgNUDgCkOVRrXxYJPGxgxSAwSuMFRpVBsPRmowMYPUAIErDFUa1caDTRobM0gNELjCUKVRbTyYpDEyg9QAgSsMVRrVxoNFGiszSA0QuMJQpVFtPBikMTOD1ACBKwxVGtXGg9QQjNRgAFdpVBsP8WnszCA1QOAKQ5VGtfEQncbQDFIDBK4wVGlUGw+xaYw2O0kNOLjCUKVRbTxEpjE1g9QAgSsMVRrVxkNcGlszSA0QuMJQpVFtPESqwdQMUgMErjBUaVQbD1FpjAcNUgMErjBUaVQbDzFprM0gNUDgCkOVRrXxEKUGYzNIDRC4wlClUW08RKSxFoPUgIErDFUa1caD1BCM1GAAV2lUGw/r09ibQWqAwBWGKo1q42F1GoAZpAYIXGGo0qg2HqSGYKQGA7hKo9p4WJsGYYb3Q3AnlxrC4QpDlUa18bAyDcgMGjUA4ApDlUa18bAujflmp9oMmlAg4ApDlUa18bAqDcQM7wetNUDgCkOVRrXxsCYNxgzvWobEwBWGKo1q42GVGkBmkBogcIWhSqPaeFiRBjZokBogcIWhSqPaeFieBmcGqQECVxiqNKqNhxVqgJlBaoDAFYYqjWrjYXEanBikBgxcYajSqDYeGNRwkBqQcIWhSqPaeFiaBmGGw9kNUgMCrjBUaVQbDwvTQM0gNUDgCkOVRrXxsCwNwAzvrRmkBghcYajSqDYesquhYwapAQJXGKo0qo2HRWkgZpAawHCFoUqj2nhYkAax16lnBqkBAlcYqjSqjYfwNKA3Yne/khoQcIWhSqPaeAhOA9wfLTVA4QpDlUa18RCuBrwZpAYIXGGo0qg2HkLTpBg0SA0QuMJQpVFtPASmSWIGqQECVxiqNKqNh1A1pDCD1ACBKwxVGtXGQ2CaJGZ4v5EaAHCFoUqj2ngIS2NvgYPjezc3N6GxpYZwuMJQpVFtPGRSQ3+r08UMGjUA4ApDlUa18RCUJpUZvnOr4R8hRJdf5hwOo2+VZvgeHEmjhnC4wlClUW08hKQBDBrG37u5eX4mHzUE35PrHHOFoUqj2ngISAMwg2s68Sw1YOAKQ5VGtfEwm8Z+s9PEQoPUAIIrDFUa1cbDXBrANshJM0gNELjCUKVRbTzMpEmzQboxg9QAgSsMVRrVxsOcGlKaQWqAwBWGKo1q48GfJsmg4WIGqQECVxiqNKqNB2+axGaQGiBwhaFKo9p48KshrRmkBghcYajSqDYevGkSm0FqgMAVhiqNauPBl8ZaA9NXLaUGIFxhqNKoNh4SqmFqe7TUgIUrDFUa1caDJ01yM0gNELjCUKVRbTxMpzE2g+vDW26khgRwhaFKo9p4mExjboa5hQapAQRXGKo0qo2HVGoIMYPUAIErDFUa1cbDRBrzzU4hZpAaIHCFoUqj2nhwp0mwDdJhBqkBAlcYqjSqjQdnmkxmkBogcIWhSqPaeHCrIY8ZpAYIXGGo0qg2Hlxp8IMGtxmkBghcYajSqDYeHGmSTCdcZpAaIHCFoUqj2nhwqSGXGaQGCFxhqNKoNh4caWw1ELAJUmqAwhWGKo1q42GcxtgM471Ok2aQGiBwhaFKo9p4GKXJaAapAQJXGKo0qo0HuBoWmEFqgMAVhiqNauNhmCanGaQGCFxhqNKoNh4GaazNMFSD1wxSAwSuMFRpVBsPSDUsNYPUAIErDFUa1cZDL43xZqelZpAaIHCFoUqj2njopkFvg5wzg9QAgSsMVRrVxkMnDXyD9JwZpAYIXGGo0qg2HrpqyG0GqQECVxiqNKqNhzZN9umE1ICBKwxVGtXGwyUNgRmkBghcYajSqDYeWjXkN4PUAIErDFUa1cbDJY2lB4I+I1ZqSARXGKo0qo2HcxpTMyze0CA1AOEKQ5VGtfHQpKEwg9QAgSsMVRrVxgNEDSvNIDVA4ApDlUa18VCn4TCD1ACBKwxVGtXGQ5XG1gwDNYSbQWqAwBWGKo1q46FMAzVDwCZIqQEKVxiqNKqNh3+MNzvFmEFqgMAVhiqNauPhH/A2yAXTCakBA1cYqjSqjYd/iMwgNUDgCkOVRrXx8A+RGaQGCFxhqNKoNh6gg4aFZpAaIHCFoUqj2kxDZQapAQJXGKo0qs00N7+IzCA1QOAKQ5VGtZnm3UwNy/7ihNSQDq4wVGlUm0nezdQQswlSaoDCFYYqjWozxbuZGkzMIDVA4ApDlUa1mcJSDYNvrDGD1ACBKwxVGtVmgnczNdiYQWqAwBWGKo1q4+bdTA020wmpAQNXGKo0qo2TdzM1WJlBaoDAFYYqjWrjotnsZKMGGzNIDRC4wlClUW0cnLdBArY8rTaD1ACBKwxVGtVmzGWDtL0a1ptBaoDAFYYqjWoz5vLWCXM1RJhBaoDAFYYqjWoz4gamhhgzSA0QuMJQpVFthnTeb2mshigzSA0QuMJQpVFthnTeiW2rhjgzSA0QuMJQpVFthnQ6c5QaHO+2jDGD1ACBKwxVGtVmQLczx6jBsdUpygxSAwSuMFRpVJs+vd4coQazTZBSAxSuMFRpVJse/e4cpQZjM0gNELjCUKVRbXpYqcHeDFIDBK4wVGlUmy6DDr5aDcPphIEZpAYIXGGo0qg2HYY9fK0aEGaQGiBwhaFKo9q0jD5cfr0a7M0gNUDgCkOVRrW5MP6zEzZbnmzMIDVA4ApDlUa1OeP4gzQmajAyg9QAgSsMVRrV5ozjT1UZqcHEDFIDBK4wVGlUmwbXX7GzUIOVGaQGCFxhqNKoNjXOv29poAar6YTUgIErDFUa1abG+Zdv49VgZwapAQJXGKo0qk2Fu2MvVsP4L06YmUFqgMAVhiqNalNhowbIViepAQlXGKo0qk3JRFdfqAaoGaQGCFxhqNKoNsdJMyxXA9AMUgMErjBUaVSbo5UasGaQGiBwhaFKo9pMm2GZGgbTCWszSA0QuMJQpVFtps2wSA1oM0gNELjCUKVRbZybndaoofeluRmkBghcYajSXH1tPGaI2PJkbwapAQJXGKo0114bnxnWq8F+OiE1YOAKQ5Xm2mvjM8NqNSDMIDVA4ApDlebKa+MdNKxVA8QMUgMErjBUaa67Nn4zrFQDxgxSAwSuMFRprrs2fjOsUwPIDFIDBK4wVGmuujZzvTxIDfZ/cUJqSAdXGKo0V10bCzXAtzpJDUi4wlCluebazHb7ADUkM4PUAIErDFWaK67NfL8PUkMiM0gNELjCUKW53trMd/sANcC3R0sNWLjCUKW53tpYqGE0ncCZIYka/v1cFL/9LG/9/K0ofg+4dTlm8DG42j9XGKo011qbmcuWYWpIt9CQRg3/FhWnPv9ffevvuVvtMYMPwtX+ucJQpbnS2tzMbHYKVUM6M6RQw+fi6/H4tfh0PP5R3vpc/G/uVnvM4INwtX+uMFRprrM2YWZYtuUJbIYUavhQlP8vTv//vRwS/F38PnerPWbwQbjaP1cYqjTXWZswMyxSA9oM6ZYhy1FDKwn/repoFf8IsX1ubn5ZczLDdzDBTy9ODX8Vf5y7ffl//y2pQeyJG3s1JDBDIjX8++G34yI11GhCYQBXaa6wNoELDUsmFPDZRLIJRW0GqSELXKW5wtoEmyFYDSnMkEYNf9dmOHf+D3O32mMGH4Kr/XOFoUpzhbUJFYNHDdi/OJFPDX+erzp8ulyD8N9qjxl8DK72zxWGKs0V1iZeDSk3QaZUw8/it+bW1+JzuXPhj7lb7TGDD8LV/rnCUKW5vtqEm2FKDVnMkEINv9W7HItySFDx39yt9pjBB+Fq/1xhqNJcXW0WmGFaDX0z7EYNxUUN1bsk2ndTTN+6HDP4IFztnysMVZprq80SM0yoIY8Z9M5LCFxhqNJcW23i1dCfTiQzg9QAgSsMVZorq80iMzjVkMsMUgMErjBUaa6qNuE7GnxqyGMGqQECVxiqNNdUm6VmmN3ylNIMUgMErjBUaa6oNovNMKeGpGaQGiBwhaFKc0W1WWyGGTWkNYPUAIErDFWa66nN8kGDXw2JzSA1QOAKQ5XmamqzwgxzakhqBqkBAlcYqjRXU5sVZvCqIbUZpAYIXGGo0lxNbVaYoa+GHO+2lBrQcIWhSnMttVljhp4asm11khqQcIWhSnMttYlVQ3YzSA0QuMJQpbmS2qwyQ18Nmc0gNUDgCkOV5jpqs84MHTXkN4PUAIErDFWaq6jNSjO0auhNJ/KYQWqAwBWGKs1V1CZWDQxmkBogcIWhSnMFtVmz2WmohvxmkBogcIWhSrP/2qw3g3PLUy4zSA0QuMJQpdl9bSLM4FJDNjNIDRC4wlCl2X1tIszgUEO26YTUgIErDFWavdcmZtAwVkNGM0gNELjCUKXZeW2izDBSQ04zSA0QuMJQpdl5baLMMFRDVjNIDRC4wlCl2Xltoszw/ivPX5yQGtLBFYYqzb5rE2cGjq1OUgMSrjBUafZdmx2Z4fkgNQDgCkOVZte1iTMDxybIixkOwZ1cagiHKwxVmj3XJtoM7TJkbjOcxKBRAwKuMFRpdlybaDP01JDdDFprQMAVhirNjmsTbYb24mV+M2gZEgNXGKo0u61N3GaneqHhrIbM04nKDFIDBK4wVGn2WptYM1T8IjKD1ACBKwxVmp3WxsQMjRo4zCA1QOAKQ5Vmp7UxMUOthrxmOJzNIDVA4ApDlWaftbEZNFRqYDGD1ACBKwxVml3WxsgMpRqym+FyW2pAwBWGKs0ua2NkhpMaeMwgNUDgCkOVZpe1ifJBZ3f0LyIzSA0QuMJQpdljbeLM0HnnBJMZpAYIXGGo0uywNmZmyLoJ8jAwg9QAgSsMVZod1iZSDaRmkBogcIWhSrO/2hiaIZ8aRmKQGjBwhaFKs7vaRJqht9DwPYcVShxmkBogcIWhSrO72hia4TmXGlxmkBogcIWhSrOz2sRtdhpenMikBqcZpAYIXGGo0uyrNlbbIJt1hixqGC9A1kgNCLjCUKXZVW2MzZBFDVNmkBogcIWhSrOr2hibIYcapsQgNWDgCkOVZk+1sRo0XK5aplfDtBmkBghcYajS7Kg25mZIrwaPGaQGCFxhqNLsqDZ204lLb0zigwuTywwVUgMCrjBUaXZUmwgbdP+2ZWcPZFo1+M0gNUDgCkOVZj+1iTHDxLstk6rBLwapAQNXGKo0u6kNwAxJ1TBnBqkBAlcYqjS7qU2UGtxmSKmGWTNIDRC4wlCl2UttEGZIqIZ5M0gNELjCUKXZSW2izHBRw/Bt2KnUMLMAWSM1IOAKQ5VmH7XBmCGVGoLMIDVA4ApDlWYXtYnZ7OQxQyI1BIlBasDAFYYqzR5qY7MN0vGhTknUEGgGqQECVxiqNDuoDcwMSdQQagapAQJXGKo0O6iNzQZp1wdBJlBDsBmkBghcYajSbL82VoMGV2+0lICLsAXIGqkBAVcYqjSbrw1uOoFXwxIzSA0QuMJQpdl8bYBmQKthiRikBgxcYajSbL42q3UQ8BcnsGpYZgapAQJXGKo0W6/NejP4NjSce6OFAaZYaAapAQJXGKo0G68N1AxQNSw1g9QAgSsMVZpt12a1GbybINveGNv/J1m0AFkjNSDgCkOVZtu1wZoBp4YVZpAaIHCFoUqz6dpEmOGsBu/fvEWpYYUYpAYMXGGo0my5NgZmcG91uvTG1Z3fyyozSA0QuMJQpdlwbdZvdgo0A0gN68wgNUDgCkOVZru1sbJ548UAACAASURBVNgG6Z1OYNSwZpmhwkQNB++XEUgNBnCVZrO1SWAGhBpWm8FUDWclSA1cYajSbLY2CcwAUMNqMUgNGLjCUKXZam0MBg2zZrBXQ4QZpAYIXGGo0my0NknMYK6GGDNIDRC4wlCl2WhtkpjBWg1RZpAaIHCFoUqz0dqs9MHkX5yY6I0RPXnE+gXIGqkBAVcYqjTbrM1aM4Rtgmx7Y0xXHhBrBqkBAlcYqjSbrE0iM1iqIVYMUgMGrjBUabZYm5VmCN4E2fbGyN7cEm8GqQECVxiqNFusTSoz2KnBwAxWahgQ+pizxwy+J1f75wpDlWaDtVlthoXTCTs1WJhBaoDAFYYqzfZqk84MRmqIXoCs0durEHCFoUqzudqs3ey0wgw2ajAyg9QAgSsMVZqt1SZ6G+QCM5iowUgMUgMGrjBUaTZWm6RmsFCDmRkgb8q2Q2owgKs0G6tNUjMYqMHODFIDBK4wVGm2VZvYQcMyM8SrwdAMUgMErjBUaTZVm8RmiFWD1QJkjdSAgCsMVZpN1SaxGSLVYGsGqQECVxiqNFuqzTohdN9tubQ3xnRlWzFoyxMGrjBUabZUm3Vm6GxoWNwbI3qytRmkBghcYajSbKg20WZIqQZzM7BPKP4RIhO/VnE4NDdOZviejpMZzB8zuFJaawiHKwxVmu3UZuWgYf2YYf2owXgBsoZ81BB8T672zxWGKs1mahM3nVhlhrVqgJhBaoDAFYYqzVZqk8MMK9UAEYPUgIErDFWajdRm3WanSDOsUwPIDHp7FQSuMFRptlGbuG2Qa82wSg0oMyDU8ONBFy9zB+jCVZpN1CaTGVaoAbPMUGGthrfH28OhCD83M8cMvidX++cKQ5VmE7XJZIblagCawVgNT3enqHdPS06O/5jB9+Rq/1xhqNJsoTaxg4b1vXHh/YFiMFXDaSJx4uFl6enxHTP4nlztnysMVZoN1CabGZaqAWoGMzVUE4nDR+NrFVKDAVyl2UBtck0nlqoBawYjNVQTidtvb9aXMaUGA7hKw1+bNULo/MWJCDMsUwPYDGZvryoeXutb607R1DGD78nV/rnCUKXhr80aM8RuaDj3xvC7Ihcga4zU8HC5teL8eI4ZfE+u9s8VhioNfW0ymmGBGvBm0KgBAlcYqjTstVlhhuhNkG1vDL0jXgxaa8DAFYYqDXltspohWA0pzKArFBC4wlClIa/NKjMcjMwQqoYkZtC+BghcYajScNcmrxkC1ZDGDNoNCYErDFUa5tqs2utkaIYgNSRYgKzReygQcIWhSkNcm6hdkBZmCFFDMjPonZcQuMJQpeGtTX4zBKghmRj0eQ0YuMJQpeGtTZwZ0qghoRmkBghcYajS0NYmZtBgZIZZNaQ0g9QAgSsMVRrW2jCYYU4NSc0gNUDgCkOVhrU2DGbwqyHdAmSN1ICAKwxVGtbaLDdC1F+cmOiNnp+lNoPUAIErDFUa0tqsMIPlhoZzb5z+UWoxSA0YuMJQpSGtDYUZPGpIbwapAQJXGKo0nLVZM2iwN8O0GjKYQWqAwBWGKg1lbVabwWir06U3ur+dfJmhQmpAwBWGKg1lbVaY4TxosO2Nzu/mMYPUAIErDFUaxtpEmCGBGvKIQWrAwBWGKg1fbVZsdgKZwamGXGaQGiBwhaFKQ1eb9dsgzc3gUkM2M0gNELjCUKWhqw2RGRxqyGcGqQECVxiqNGy1WT1oAJhhpIZMC5A1UgMCrjBUabhqc0NlhqEasppBaoDAFYYqDVdtuMwwUENWMUgNGLjCUKWhqs37r19MZuirIbMZpAYIXGGo0hDV5tTFl6rB7C9OTPTGzu3cZpAaIHCFoUpDU5uqjy9UA2gTZNsb25vZzSA1QOAKQ5WGoTY3N+cNkMvUgDZDq4a8C5A1UgMCrjBUafLX5uamvS6xVA2NGdBqYDCD1ACBKwxVmty16YphoRrgZjirgUEMUgMGrjBUafLWpi+GZWpophNAMzRq4DCD1ACBKwxVmuxq6HX3BWpIYIZaDSRmkBogcIWhSpO1NqP+vkgNcDNUamAxg9QAgSsMVZqMtXH096X7GrBmOKmBYgGyRmpAwBWGKk222ji7+kI1gM3w/J3IDFIDBK4wVGly1Gb6TVTL1IA2A89kokRqQMAVhipN+trcDC9LrFXDdZlBaoDAFYYqTeLa+LywXA3YvshlBqkBAlcYqjQZ1ODp7UvUADZDucww95eykyI1IOAKQ5UmbW3munuIGsDvtmyoFiClhmCkBgO4SpOyNvPdPkANKbY6nScTUkMwUoMBXKVJV5t5MYSoIaUZpIZwpAYDuEqTpDbtu64N1JDODFJDOFKDAVylSVAb/0WJZWpIagapIRypwQCu0sBrs0AM82qopxNYM3R2QEoNwUgNBnCVBlybRWKYVUNiM0gN4UgNBnCVBq+GcDEEqCGFGTq9EXigxUgNCLjCUKXB1maJFgLU8J7WDFJDOFKDAVylQdZmqRgC1QDsfoO90VJDMFKDAVylwdVmuRhC1JDSDFJDOFKDAVylgdRm9Z+unFUDcjox/nAGqSEYqcEArtIAajPz7soYNaQ1g9QQjtRgAFdprGsT4YV5NYDNMO6NsKOtQGpAwBWGKg1CDWvFMK0G+F+ccH84g9QQjNRgAFdpjGuz3go+NeC3Ork/tkVqCEZqMICrNKa1iRTDlBpymUFqCEdqMICrNIa1iRbDtBqwZpj83GipIRipwQCu0tjUJvhd12vUkM0MUkM4UoMBXKWxqE3URYlZNVTTCawZJnsj6pgruLmRGgBwhaFKY1AbMzE41XBeaEB1Od/nRhOpoSxy6AmRGsLhCkOVJro2hmKYUEM2M/CooSzyL40aAHCFoUpjoQYzM0xdvIRNJ2b+cB2JGuoJm9SAgCsMVZrI2thZwaOGXGYgUUMzLpMaEHCFoUoTVRtjMUyoAWkG/x0Y1HCZsEkNCLjCUKVZWZtyTcxcDG415DMDgRo6F3+kBgRcYajSrKqN3dXKeTVkNEN+NXTrLDUg4ApDlWZFbWBicKkhpxlyq6FfZ6kBAVcYqjSLawMUQ18N0L84MbcAWZNVDcNCSw0IuMJQpVmjBpQYemqAboIMM0NWNYwMLDUg4ApDlWZpbXBa6KsBugkyTAw51eAYmkkNCLjCUKVZVhusGPpqyG+GbGpwTtqkBgRcYajSLKkNWgwdNSDfbRlshkxqmFjNkRoQcIWhShNYG6t3XQeqAbnQEG6GPGqYWuaVGhBwhaFKE1Qb6FUJhxqAZghcgKzJoIbpSksNCLjCUKUJqU0qMXTUQGGG9GrwOVhqQMAVhirNfG3SiaG7DAkzw5K7p1aDt9RSAwKuMFRpgtSQRAtV8z+rgcIMidUw42CpAQFXGKo0c7VJIYRO82/UwGGGpGqYXc+RGhBwhaFK461NChv0m3+tBhIzpFTD/LRNakDAFYYqzVRtEl2tHDb/Sg0QMyxbgKxJpoaQ9RypAQFXGKo07toku1o5bP6NGuw73xozpFJDWLWlBgRcYajSuGqTSwzvh0OpBpAZVvxWGjUEVltqQMAVhirNuDa5xHCowEwn1pkhiRpCq304SA0AuMJQpXGqAW0BZ8svOU0oiMyQQA2hHq6qE3pKpYZwuMJQpRnVBi6Bybb/Xq41AMywapmhAq6G4AFaqU2NGgBwhaFKM6gNtv/PwmUGtBqWzdwSqeFrUf3z87ei+P3n/K3LMYMPwNX+ucJQpenVBtfng6hG19b9b70YwGpYuqaTRg1fi0oN/xUVf8/dao8ZfASu9s8VhirNuTZZ9jHUs4iGGzozQNWweLE3iRo+F7Ua/ii+ll/8b+5We8zgQ3C1f64wVGnq2mS5KnG+JFHSeOG7dWeMMgNQDWH17poziRo+fPhZq+H3ckjwd/H73K32mMHH4Gr/XGGo0lS1ySCGrhfaAYO1GuLMAFNDmIm7FXpPo4bTYKBWw4fq/+Vt/63qaBX/iP1RttNfqakvVZbUXvgOoDwG4nFjCSt4W6GG4BMatQxZFP3/+29JDbsmhxku4MTAaoaVYqBVQ40mFAZwlebd/Wfr0zBaebScUEROJkoAE4oFe5wG30l08VJqyAdTacoWB+jyYe3dcUnCUA0GZgCoIWJVJ4caPszdao8ZfACm9i81uDhfrUynBvfKY78vmnVGCzOYqyFquTepGj5drkH4b7XHDD4ASftv4ArDkKYd1yZSQ88Lk3sYzNRgYgZjNUReIU6qhq/F53Lnwh9zt9pjBh+Aov1f4AqTP023kSZUQ3PTs7fJSA0Re6N7WKohSAyjBYYOSdXwd73f8b+5W+0xgw9A0P47cIXJnabfSFMvQ3o3PdqowcoMlmoIE8N48bElqRqqd0n89nP+1uWYwQfI3v57cIXJnabfSJOqYW4ztIkarMRgqIZ4MeijXDBwhcmcZtji4jr7XGvvfjn/LgkLNdiZwUoNIXOJOTFIDRi4wmRNM25xizt8KGErj/2+GN8ZDc1gpIbQXdEz95AaEHCFyZjG1eIWdvhA+l4IfVtlvBoszWCiBrP3p0gNCLjC5Ekz1UKBajjfDn+7dawazBYga+LVYPiWVqkBAVeYHGluJtsofBlyyecwRKrB2AzxarB8S6vUgIArTPI00154R6thiReeY9VgLIZoNQStPgaXUmpAwBUmixqmW1xw25xnuJa2UAyRajA3Q5waAuYS85clOkgNCLjCpE4z0+JCm+Ys61Ye+30xojPamyFKDcZikBowcIVJm2a2xYU3Th8DL6z8pMcINQDMEKEGczFIDRi4wqRME9DiljTPaaIHDHVfXNsZrRcga9amCdzjtKzCUgMCrjCJ0gR+RrT9MuRqMaxXA8YMa9VgeV2iRWpAwBUmSZrgC+rGaojwwvNqNWDEsFINGDFIDRi4wiRIs2CnTYwaRoPiODGsVQPKDGvUYLjHaYDUgIArDDzNoua5Xg02K4/9vrhGDTAzrFDDbOWXrjC0SA0IuMKkUMOCFreuoQ69YPPHp1aoAbTMULE0TYAYFq8+XpAaEHCFQadZ2OLWNVTzAUPdFxerAWmGhWqYHazFiEFqwMAVBptmcYtb3VYvWIlhhRqQYlioBqwYpAYMXGGQaVa0uIjWWmLnheflasCaYYkagvY4RRVaakDAFQaUZuXK+CI1jFq3qRgWqwFshnA14K5LtEgNCLjCQNJ4313pbXHhd4WsPPb74iI1oM0QqoYUYpAaMHCFsU+z2gvvC9SAF8MyNUAXIGvC0iQRg9SAgSsMRg2rW1zY3ez3MDj7YrgaEpghSA3zq49rz8wAqQEBVxjzNHEtLuxuKcSwRA0JxBCihrnhWuxliQ5SAwKuMMZpYlvc0l+AeeF5gRqSmGFeDenEIDVg4Apjmia+xS269w1SDOFqSGOGOTWkFIPUgIErjFWawHddz7U4z8+iP9BtcV8MU0MiM/jVELL50eD8XJAaEHCFsUljdcVsWg0GH+i2uC+GqCHFAmSNL02i6xItUgMCrjAWaewupU+oAfC2ypC+GKCGdGbwqCG5GKQGDFxh4tNY7rHxqOHyRRovPAepIZ0YptWQZo/TAKkBAVcYEzXYtbjZeyQTQ4gaUpphSg1eMdguMHSQGhBwhYlOY9vi/D9O6IXnADUkNYNbDTNiMF58bJEaEHCFiUxj3eI6t80/0G1xX5xRQ1ozuNTgn0vgxCA1YOAKE5XGvsVdbuVZeez3Ra8aEi5A1ozTZBOD1ICBK8zaNJilr0YNmA90W9wXfWpIboaRGub3OAFO0OVESQ0AuMKsSxPz7kpvi6v/yT5gqPuiRw3JxTBUQ5brEi1SAwKuMCvSoLzw7lyGzCUGrxoymKGvhrxikBowcIVZqQZUixt8nc8Lzz415DBDVw25xSA1YOAKszwNqrWVk4i+GrKKYVoN6ZcZKi5pfIM26AJDB6kBAVeYpWlQba1eeeyoIbMXnifVkMkMFzV4xYBdfGyRGhBwhVmWBtTQLlckLmrIL4YpNWQSw1kNvrlEMjFIDRi4wgSnsXnXtZtLm67VwOCF5wk1ZDNDpQb/XCKZGKQGDFxhAtMkulZWqoFEDG415DNDqYa5PU74E3Q5UVIDAK4wYWlSLYn/ovHCs1MNGc3w/D3/dYkWqQEBV5iQNJg26XiVYxKDQw25FiArMu9xGiA1IOAKE6gG87Y12gtN5oXnsRpymoFLDDtSw00DME04W1MDoGGNvdCIYfmfrQcyUEPuIYP7DespFxg67FYNWVWxLTVAGtbkgIFYDZmHDO7Pski3kWHAbtTg3n+bSRVbUUNZjiTNrDOT4FVDPjOc5xIONeQSw47VMCSpKrahhlST2/4KA60a8pqhqtSoEecTwxWpYchZDe/NA5iqYgtqQIjB1ZCHS4+kasi3ANk5Dy41GJ+icK5WDUMuqjhRPmCUKvjVABJDwN+Y4VRDNjP0TkRsIzZFapigq4oTI1V41bEJNdjWy+EF97VKSjUwDBnepYYFZFTDkIEq/GsX9Gqwr0/IgKGCUQ05hwzdmkkNwRCpYY6hGqDLnIsYqyFZOVzdgVANmcwwntRdPhwvwRmaRWpA8CvzxdN+aQanGP/0p73wzKiGjGYYt5uMGxkGSA0IJj//sGwMdeB0quiowfpd18427BUDnxoyLUA6l4F/8YhBasAQEKa3dnEcq8JOHRc1jAewcQSvPPYgU0MeM0ycil88YpAaMKwIs2iZcxFnNdiKweWFoLdPcamBaMjwXqvB7hRFIjUgAIRZr4paDcYjBsc+vRAvPFOp4ZBvyOAuKlcjlhoAJAgTroqLGhLkCegYLGqohz3p0/imdVyNWGoAkCHMZULSFKRVRamGJAcP6xoUajg0A4bZv5Rtjnf0xtWIpQYABGHatYtfhmmcU+ElYiBQw+HQTiRSq2FCDOeyErSbFqkBAVcYszRrVx575FVD1wvPqdUwMZdoy8rVbqQGAFxhjNJMXqpc9oluGdVwGK07JlXDnBjY2o3UAIArjEmaVXsYXGRSw2HsheekapgXA1u7kRoAcIWxUkPv63VeeM6jBrcXnhOqYeq6RL+sXO1GagDAFQa2y2JVJ0muhiktlKRSQ+CuEq52IzUA4AqD+SiLtb0kqRomhwsNadQQvN2Mq91IDQC4wqxPE/KBbktJp4Y5LzynUYNvj9PwVK0+UwCkBgRcYdamCftAt6UkUsO8FkrwanCLYeKtElztRmoAwBVmVZp1b6sMIIUawrzwnEANE2KYeBsVV7vZixqa0SPHG9e4TvFqNfS+YeKFZ7waAqYRLWA1LBMDW7vZqxqyqoLrFFuksRIDWA2LvPAMVoNzLuFtlFztZi9qGFY1qyq4TnF0GjsvPCPVsFALJUg1TO5x8pyq2DNlyW7VMCSpKrhOcXgaZz1MxYBSw9LhQgNODeGXJbqnavFvANmNGm46hDxxqCq4TnFoGtjKYw9AZ1zphWecGhZcsOydquW/gmOXalhoiQpTVXCd4qA0zmdrLwZ7NazWQglIDevEwNZu9qKG74MzE2GJiihVcJ3iYDX0v4PwwrOtGtYPFxoganCvPgadqpA7pWKXahicpmhNlCxSBdcpXpUGJAZDNUR74RmiBlf7Cn1R4Wo3O1fD8JxZWKJi12qAeeHZSg0GWiixV0OEGNjazfWoYXgCzSxR0VMF1ymeTuNuskgxmKjByAvP9mqIEgNbu7lONQzOpoEmhmqAXhxdxKQakq089ojsjBbTiBZbNTgbzpI2IDUEk0INw1NrMZhwqCGfKtxqcKeBiyFODbZeeDZWQ+zAU2pYQGI1DM/zaks4TnE+VUyrYfCtBF54jlCDtRZKDNUQLwapYQEZ1TA46cs0EXCK06kicBkyjRhWqsF8uNBgpobotar6VEU/giFSw/IWMG+JFacYp4oQNaTywvMaNaC88GynhlEjWHfipIZg6NQwbA4TljA4xXaq6Khh4vcTimGxGnBaKLFRg0MM66QuNQRDrYZB21i/MhGEgRqyrTz2WHCigMOFBgs1jE/16tGe1BDMVtTQIYElKsJVUath4t6pxRCuBrwXnk3UYCcGqWEBG1RDRRMGaYnwCUerhuFP0nvhOVANKbRQEq0GxwmNWCCSGoLZuBo6wAcT06qYWobMIoYQNaTywnO0GqwHhFJDMPtRQ4c0U44ZNWTywvOcGpJMI1qi1GB/2qSGYHaphi4pLOFQQz4xeGuT2AvPcWoA+FxqCGb3augAG0wM1ZDTC8/TtUmuhZL1ahieG5PNalJDMNekhg62luirIbMY3LVJP1xoWKuG4fmw2p0W/xB2SA0IDMPEW6KjhuxeeHbUJpsXnlerYTxksNnhLjUEIzV0WTmYuKiBQQzD2mTUQskqNaDEIDUs4P/+Ceb7VTFhiV/T1HfInbtHM1zIHWMh40KfnoOn8NsluO9p1BBO0jBzY4ly1MAxYKhoapNzGtGyfNQQsegT8EINe+QVkI8agu95xWroMDGY4PHCc10bCi2ULFUDVAxMaiifqNQAgCAMqRhYhgsNy9QQtLQTg9QQjNRgAPwPxS+AygvPC2vTFwPkU3fsH3IdVZeSGgBwhWFJ06w65o7RZUFthmIwuyzRgUMN5y4lNQDgCkOR5jJcoEhzJrg2N2MzADplZjWUT7HTpfaihgPTWHWbzR9G79RkT9MltDZJxJBZDfWiVKdL7U0NFIbYZPMHMTwhG6yNY48TpnfmU8PQC8cdqeF8jikMscHmj8FxFjZXG/h1iZa8ahh2qb2poSGvITbX/BFM1H5rtUknhmxqmOhSO1VDQyZDbK352zNd8W3VJqUY8qhhukvtWw0NqQ2xreZvjrfMW6pNby6R4C+NpVWDYxLR61JXoYaGZIbYUvM3Zra4G6pNXwywxceWdGo475L1dalrUkMD3hAbav6mhJR0M7VJLobUapjrUleohgagITbT/C0JrONGatOdSyQSQ0I1BHWp61VDA8IQG2n+hoRXbxu16S0/JhJDKjWEdqmrV0ODqSG20fytWFazLdQm7XWJFrAa+huhZ7uU1NDDxhBbaP5GLK4Uf20S7nEaAFXD7LrjsEtJDS4iDcHf/G1YUx722uQTA1INS71wlBr8HFYqgr35W7BTbfZWH2E9dQKsGhZ2PqkhgMWGIG/+8URMuKhrk+OyRAeQGtZ1PqlhAcGGoG7+0ex2HaYzl8ghBpAa1nY+qWE584Ygbv6RxF/B4a1NbjFYq2HFJKLXpaSGtXgMwdv8o4j3wjNvbQabH007aSh2agjYCD3bpaSGSFyGYG3+MVhooYSzNjmvS7TYqiGy80kNRkRe7gRioQbDZ5X7RPU414ZCDIZqsOh8UoMthIaIVYPxcyE5UTV1bUjEYKUGo84nNSD4zmSIKDXYPwOuE/W9O5fItMLQEqmGZRuhZ7uU1ACA64MqI995ZpnlmU8NHTHkWn28EKWG6HXHYZeSGgBwfVDlqtLAwnKdKCYxxKjB2gtHqQED1wdVrv4oC0AYJjU01/fKnkUhhlg1GHc+qQEB1wdVLiwNOBfFiTpf9r9pOiOFGNarAdL5pAYEyz+oEqiIJWpI4KvcJ+pihXK48Ivjr0yeWZUG1fmkBgSrP6gS0SmD1ZBmHJPzRHWtUPfFTavBfhLR61JSA4AVYXCGCFNDsgWQTCdqZIW6L25YDYCVx36X2osa6kLlaXYj1jd/gCEC1JByYTS9Gm7cWqhWGLaqBrQXjrtTA4kfopu/pSFm1JD8gkmyIz17rPDePO2NquEIF8OO1FBXlcQPVs3fxBA+NaT2wnNCNUxaoaK+WrnJCQWww/W61L7UUJHfD8bNP84Qk2pIr4WSFGqYsMJhdI1yW2qw3Qg926X2oobhac/qB+inMS41hFMNGYYLDWA1TE0hzmUb9MUNqSHB8kK/S+1JDaNTn8sPKT7ePbhzj9WQzwvPyNpMLyy8u0YMVV/cihpSe+G4IzWUVXWf/gwLlFwfVNlPk1MLJZja+BcWpvviltQA7GjOLrUnNXQZaCKtH7g+qPL78M7Jwrkwr820FdwvFb2+uA01ALuYp0vtWQ3ZJhhcH1T5vXeX5NEGWNbGM4Vwry2M+uIG1ADsX/4utVc1lMxNMAwbaR+uD6r8/px/GtFiVBvvwsLkuR/1RWo1pJ9E9LrUntXgAeuH3Lu2Dy4yZzpjUJuVCwuuvkishgwrj/0udS1qcA0wYX7IrYYGPi88x9Zm0gphE4hRX2RVQ24vHK9cDZ2TYOsHEjXUYP9S9mJWp/FPIfalhhQboWe71LWowY/xBQyqzrgHNdhNIfpQqgHYoZZwtWpwvMLY+YGqM25cDR4rLB8lDOFSw03SjdBzXLUaYBMMqs64XTX4phDrJhBDmNRAsLzQ42rVUDLdtCL9QNUZN6mGuWuTBiOGEho11M81uDOm4KrVEHS61viBqjNuTg2ohQUHVGo4tWJgX1qM1NAwNUBd5QeqzrglNfitYDNS6EKihksrhvSilUgNDd6561I/UHXGjahhZgphs7YwhEEN3VZs34XWIzUEs+ACBlVn5FfD7MLCO2LEUJJXDaNVR6khmIxqmGiKYX6g6ozcavBZAaODLjnV4LgiITUEk1kNqycYVJ2RVg3esQJmAjEkmxrcVyqlhmAyTyj8TdPjB6rOyKiG+SkEXgsludQwsRFaagiGaK1hdoIxav4skKkhZGEhGVnU4GnFBn3GDKkhEN8Ad+QHqs5IpIYZK6QZKXRJq4bqiXtbcXSPMURqWIC36Xb9wNMZn1nUcKnOxIlKs7YwJKUa3MsL/VYc22EskRpMWXCBMxnZ1dCfQnjUkPJM1SRTQ4AXjlLDAhb+YbtEp7nC8ypH5oesahhPITgc3pBUDQGtOKKvmLMrNaTUg3cA/Oud4W9oNeRSw8TCwq+A+iUjkRqCW/GqXgJiN2oonwfJsnfnlZHBDxnU4LsKcXVqWNDepYZgFqqhAX+2HfSaeb/BZfZDWjX4rOCoTWawagiaRPRa8bK7Y9mjGo457NB7FXQ0wAXkzQAAED5JREFUuHx+SKeGOStU9bkaNYStPPZb8aJ7g9mpGvJMLnxqqMjihyRqmLNCq87rUMMKLxylhgVEqiHb2kPAXzxOJwi0GmanEBWz2swCSg0rPxFaaggmQg0l1fnBnHs/v2aX2RL6AaiGMCuMasMDRg2r27vUEEykGs4gTr+XeTXUJPEDSA0BVnA+/x2r4WZuI/RsK475ZWuuQg0lSScXixoc2g/2aggZK0yqcbdqWLe80G/FUb9tzFWpIZkeHA1uZgQB9IOpGoKnEN7tYDSYqcHAC0epYQGGaijJrYa5CQbGD1ZqmLdC0Bam/aphfTu/tOL4h7DjqtRQY9QYfMS8hcjcDxZqmB8rBO9u3KMa1rRtZyu2eiALrlANJSYNYpqABhf6CVLxgohUQ+AMInjT8+7UsLRR+1qx4WNFc6VqAE8uAtUQvP4Q5Yf1agheWFjCjtRgMonotWLbh4vjqtUA00Ngg0szwVinhtCrEGHPtMNu1GCz8thvxaaPFsnVqqEktxrCifHDYjUEjRVWv3NyH2oAeOEoNSwArIYzpq2tZEWDWzTBgKkhfAqx+g3V+1FDRIubQGoIJpEaTrybjiAwaqhY7odANUAWFhzsQA1xTc2D1BBMOjUcTXsFuvkv8kOAGgKsYPbRKxtWw03sRug5pIZgEqrhaGkHg+Y/3w1DL2D41RA6VpAaMMsLPaSGYNKqoZKDycqDkRoCumKAHybVkGoK0WebakjghaPUsIDUajgT3eJiH6Am+FXa6weXGsKsAPnsxu2qwbKFuZEagsmlhmPkhU1A818ywfCrIWysAPtY1y2qwbhtTSI1BJNZDav1AFLDogmGSw0LZhAQLZRsTg3GDcuH1BBMRjWUMKmhJLS79v3wPfBvUydiQ2pIMonoIjUEk1kNDSta3NqmasiNg5lfgY0UujDU5oJPDWlWHntIDcFwqKFk2ZaoBM0/rBuHjxVgawtDtqGGDF44Sg0L4FHDsk0PidQQ1pXDltqSaKFkO2oAtygHUkMwRGo4LrFDouYfq4ZkOuiyATXgm9IEUkMwXGqot0QFtTjbBhzCdDd3N/9kE4ghxGq4QW+EnkNqCIZNDWdmWxyuMU8x3dWn1YDO5IRWDXmWF3pIDcGwquE4c2EzU/NfooZsUKW51IbAC0epYQHkapjUA2fzzzdS6MJZGwYxSA0LIFZDCbMa2glG3fyzrS0MIahNS6OGDG3HidQwwc/fiuL3n71jBv9uFjU0OFpc4hbuwKWG3JkqCGrTUtYmX8MZITW4+a+o+Lt7zOBfzqmG48gOdM2fCJI0zXyQqjNKDRP8UXw9Hj8X/+seM/iXM6thMLkgaf41UoMLqWEOIjX8Xg4Y/i5+7x4z+Jcp1HDRA0nzr5EazjTnp3PWsjebPlRpiNTwoSj/XxTN0Sr+2RTVy9AvwchZ3bnbyB6Bq6HYvBrO5O4GYkijbQEhrRpqNjSh6PFPwr/KPct1TijOo4SZE0XVbDShmGB/auCQg9TgOVFUzUZqmKBRw4fuMYN/mesc12FI7HA9ahgvM86eKKpmIzVM8GnTVyh6nMPcGH14fRTXoYawUcLoRFE1G6lhgq/F53Jfwx/dYwb/Mtc5HoXBdIcwrkINx3VvguBqNlLDBH/XuyH/6x4z+Je5zrEjTL65xT7VsG6UMDpRVM1GapiifA/Fbxt8D8WYCTXk0YPU4DlRVM1GaghnR2ookRreTdRgeKKomo3UEM7O1HAmvnMsYR9qsBkljE4UVbORGsLZqRqOSz+8PopdqOEoNaRGakAQECbdysM21XBZm8GeKKpmIzWEs2M1HJPZYdtqAJ8oqmYjNYSzbzUk2hK1PTVAz00PrmYjNYSzdzWcwfbFTaghzShhCFezkRrCuRY1HJEXNqWGabiajdQQzpWpAaMHTjWkWWacg6vZSA3hXJEaSq5RDbaFXwpXs5EawrkyNTRY90U+NRiVOR6uZiM1hHOdaiix7IsUariMErhOFFWardZGagjHIIzd5EJqmIar2Wy1NlJDOEZqsNFDHjW0+e1rYwdXs9lqbaSGcIzCVK+y0Z00rxpQtbGBq9lstTZSQzjWYaI6aQY1pKxNFFzNZqu1kRrCAYRZP7lIo4bgi5FcJ4oqzVZrIzWEA1LDOj1IDdNwNZut1kZqCAcUhk0NzmXGObhOFFWardZGaggHG2ZhB8aoYfVuRq4TRZVmq7WRGsKBh8mthuPKD3c/brb5J4EqjdSAIEGY8MmFjRrM3vPAdaKo0my1NlJDOInUEKYHqWEarmaz1dpIDeEkChO2JWq9Ghr12IbmOlFUabZaG6khnMRhEGqAvWma60RRpdlqbaSGcDKEmZ5crFFDxDLjHFwniirNVmsjNYSTSQ1uOYSpIdlHq3CdKKo0W62N1BBOnjATdpAapuFqNlutjdQQTq4wN64Pr59WA2SZcQ6uE0WVZqu1kRrCyR5mXg3JRglDstemC1ez2WptpIZwCMK0cwuXGoDLjHMQ1KaFq9lstTZSQzgEYdotUbUaso0ShhDUpoWr2Wy1NlJDOCRhpIY5uJrNVmsjNYTDFYYqjWrjgSqN1ICAKwxVGtXGA1UaqQEBVxiqNKqNB6o0UgMCrjBUaVQbD1RppAYEXGGo0qg2HqjSSA0IuMJQpVFtPFClkRoQcIWhSqPaeKBKIzUg4ApDlUa18UCVRmpAwBWGKo1q44EqjdSAgCsMVRrVxgNVGqkBAVcYqjSqjQeqNFIDAq4wVGlUGw9UaaQGBFxhqNKoNh6o0kgNCLjCUKVRbTxQpZEaEHCFoUqj2nigSiM1IOAKQ5VGtfFAlUZqQMAVhiqNauOBKo3UgIArDFUa1cYDVRqpAQFXGKo0qo0HqjRSAwKuMFRpVBsPVGmkBgRcYajSqDYeqNJIDQi4wlClUW08UKWRGhBwhaFKo9p4oEojNSDgCkOVRrXxQJVGakDAFYYqjWrjgSqN1ICAKwxVGtXGA1UaqQEBVxiqNKqNB6o0UgMCrjBUaVQbD1RppAYEXGGo0qg2HqjSSA0IuMJQpVFtPFClkRoQcIWhSqPaeKBKIzUg4ApDlUa18UCVRmpAwBWGKo1q44EqjdSAgCsMVRrVxgNVGnI1CCH4kRqEEA7SqyGc/wufe1wbKs00qs00K2sjNWwHlWYa1WYaqWH3qDTTqDbTSA27R6WZRrWZRmrYPSrNNKrNNHtRgxCCAalBCOFAahBCOJAahBAOpAYhhAOpQQjhQGoQQjjgU8PXIncCTv79XBS//cydgpM/PxWf/sgdgpffVnUpOjV8LaQGF/8WFXKDgz+r0sgNE/yxrkuxqeH00ig1uPhcfC29+Sl3DkY+nYz5V/EhdwxS/l7ZpcjU8OHDT6nByYeqLCrOJCrNBJ8+7EINX3WKvWjUMMVf5ahKjPlf8ecu1HCUGnz8pQn1BL8Xv+eOwMnfp8JIDbvn3w+/5Y7AytdPcoOTDx/+kxp2j8zg40/NKBx8Lv5a26Wkhs3wt8zgRQ3HQXFm+a9KDVvhT42Yp/hQ/HdUw3EiNeyfn4XGDFN8Lf5Xbuz5nDsHK5pQ7JrfVtt///z3oazMh39z52BFatg16weGV0D5/pLPMsMUe1GDEIIAqUEI4UBqEEI4kBqEEA6kBiGEA6lBCOFAahBCOJAahBAOpAYhhAOpQQjhQGrYFgf7E/b2+PFwONw9vFg82Gy+l4fbw+E2/GAvt+dHBTx14UHl3hb2/ePhcObe4NHm8n08H+zjkgeUGjKgcm8L8/5xdygey5fwl8ciuLt6mMl3OtqX1+Px9UtxuFv6gFJDWlTubWHdPx4Pd2/NzdfiED+n8Oe7b4VwFzhIkRpyoXJvi/PQ+sfdaQJQ9+SX++Jw+6X+8etDcSgeXpt7vt4dbp+Ox2+3h9tv1Y/fyh/fv7YP93Y4tF89HR6qXztNMR5Gj3U5+um/8jX/R/OAg5WD008f65++HW7Px2huHF8P7buD3yoRdR/4FLScbtTPqolRTz66E4ruUxgdXRgiNWyLppM81X2m7BTNzerluLldPFV3+lFU9/lWfa/syq9F/fOny8M9VhI481r92v3pHo/Dx2qPfvrhoXycL50HPPxo89Xd+Us5RKiP8+1yjMfDl/Zgj42I2qd113mwJsZIDd2nMD66MERq2BZNJzk8vB1fqjH56XX/sbr9pXolvn87vt0firf6TmX//Fj283okXxy+nH7wpfpxzcfRHOIkg+pbg8dqj366w2v5IIe3sjOfchzfvlzGBeVRTwOUb8Xppy/N7OHu0nXvOkOU+sfdB/5yuHstr5dUv3aOMVyG7D6F8dGFIVLDtmg6SfXq+1J+8VjP2V/K/vHYLCRWNqivOLzV1wLe6r5Xv2h/K3/cebjBAeo7DR6rvXs9VjkN9x/dv/5Sx3ksu249NyjaHw6fSveBbxtxdJ/iUA29p6DFByiq7rZoOknbie46r/vn1+cf9Qtys0xwnrufevn5Qe56D1ffqIful8cePFZ79+Z29aJ/e/j4dBmB9B767q5Zu2j+3z/a8MEu/748Pd71nuJADb2nMD66MERq2Ba9K/zdznU89jr65YvOP4cWx29c1DD9WPXtu/Z2tR5x+9CZ658vgFZ9+LacdNx3FyKGT6V3tNePbbre4dv/9Z7C+OjCEKlhWxir4a631hCmhu4dftRLh7ev3Ye4/PulHPYXbcDe0UZrDeWq4u39l9dgNYyPLgyRGrZFnBpGD/fYLjscQ9Vw17vD21N5BfG++xAl9ci/KE4Tknb/Qu8KxZfhFYr7alVx8BRHaujnHxxdGCI1bIuRGjxrDe0vnO86Gnu/da5W9Duf67GqF/TmesBLZzvj63iGcldNLB4OTw+dS6VvzYrkl9IQt519Da+dI3tHDY6n0D26MERV3RYjNTQ7E6pe57qq0Pnn/OMfnV79WF0xrPnWVcPgsSr/fKnvUN2+L79fLSYcuy/mF3E81qnui24Le6iPfH8ofjxUj99/4GP9Q48aek9hfHRhiKq6LUZqeKsu8zV7HBx7ETr/nH788eSBp6L70nuarlcLea/f6pH5+bF7j3V/uHspNxDUPbj4dt7X8KXe93jfnVBU+y/Pg5H7wWi/eQ9FtRnr5dh/4Gqbwlu50+mtp4bXzrPuPYXx0YUhUsO2GKnhvGuxuth3vt3Zwdj95/zj7g7I4+NlYe+2u/Gx91gvzc1aDR8v3z9vYGxnJecdjM0s4qXaAdXh/nK0YvjAP+rbj7dlt7/EKI/Qeda9pzA6ujBEatgWYzUcX07drXmvQ/O+h3Y1b/BP+QaEw8fBdP3t28fy2sDDU/f+/cc6vp6OcXqJbsb9D6exQDMN+XLqnredpcxD9R6Kj5f1j7vDoN9Wn9dw+Pj4rX6jZ+eBqwsOp3A/yh9cYrzenqZKnWfdewrDowtDpAaxjEUz+zfPe6+/aB5AjdQglrFIDd8G8wmxHaQGsYwlani7Hc4nxGaQGsQyFqjhoN1IG0ZqEMtYoIbb/rUQsSmkBiGEA6lBCOFAahBCOJAahBAOpAYhhAOpQQjhQGoQQjiQGoQQDqQGIYQDqUEI4UBqEEI4+H+l+TfxkrytNwAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 450, + "width": 525 + } + }, + "output_type": "display_data" + } + ], "source": [ "p_combined <- ggplot(df2, aes(x = groups, y = estimate)) +\n", " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), fill = \"blue\", alpha = 0.03, color = NA) +\n", @@ -537,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "b4a7321f", "metadata": { "execution": { @@ -571,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "9837c2cf", "metadata": { "execution": { @@ -584,7 +771,54 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13660.0502-0.3372
pira0.04870.0081-0.1317
twoearn0.03990.0112 0.2589
\n" + ], + "text/latex": [ + "A matrix: 3 × 3 of type dbl\n", + "\\begin{tabular}{r|lll}\n", + " & gain.Y & gain.D & rho\\\\\n", + "\\hline\n", + "\tinc & 0.1366 & 0.0502 & -0.3372\\\\\n", + "\tpira & 0.0487 & 0.0081 & -0.1317\\\\\n", + "\ttwoearn & 0.0399 & 0.0112 & 0.2589\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 3 × 3 of type dbl\n", + "\n", + "| | gain.Y | gain.D | rho |\n", + "|---|---|---|---|\n", + "| inc | 0.1366 | 0.0502 | -0.3372 |\n", + "| pira | 0.0487 | 0.0081 | -0.1317 |\n", + "| twoearn | 0.0399 | 0.0112 | 0.2589 |\n", + "\n" + ], + "text/plain": [ + " gain.Y gain.D rho \n", + "inc 0.1366 0.0502 -0.3372\n", + "pira 0.0487 0.0081 -0.1317\n", + "twoearn 0.0399 0.0112 0.2589" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "out <- summary(bench.plm)$benchmarks\n", "round(out[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" @@ -600,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "c850176c", "metadata": { "vscode": { @@ -634,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "e65ef369", "metadata": { "execution": { @@ -716,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "445dba1a", "metadata": { "execution": { @@ -729,7 +963,23 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title \"paste(\"Partially Linear Model, |\", rho, \"| = 1\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], "source": [ "options(repr.plot.width = 7, repr.plot.height = 7)\n", "\n", @@ -742,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "3f14d055", "metadata": { "execution": { @@ -755,7 +1005,23 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title \"paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], "source": [ "rho2.half <- (1/2)^2\n", "\n", @@ -788,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "629fe818", "metadata": { "execution": { @@ -801,7 +1067,54 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 3 × 3 of type dbl
gain.Ygain.Drho
inc0.13150.1288-0.218
pira0.02670.0000 0.000
twoearn0.01130.0000 0.000
\n" + ], + "text/latex": [ + "A matrix: 3 × 3 of type dbl\n", + "\\begin{tabular}{r|lll}\n", + " & gain.Y & gain.D & rho\\\\\n", + "\\hline\n", + "\tinc & 0.1315 & 0.1288 & -0.218\\\\\n", + "\tpira & 0.0267 & 0.0000 & 0.000\\\\\n", + "\ttwoearn & 0.0113 & 0.0000 & 0.000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 3 × 3 of type dbl\n", + "\n", + "| | gain.Y | gain.D | rho |\n", + "|---|---|---|---|\n", + "| inc | 0.1315 | 0.1288 | -0.218 |\n", + "| pira | 0.0267 | 0.0000 | 0.000 |\n", + "| twoearn | 0.0113 | 0.0000 | 0.000 |\n", + "\n" + ], + "text/plain": [ + " gain.Y gain.D rho \n", + "inc 0.1315 0.1288 -0.218\n", + "pira 0.0267 0.0000 0.000\n", + "twoearn 0.0113 0.0000 0.000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "set.seed(2)\n", "invisible(capture.output(\n", @@ -815,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "2aacd3da", "metadata": { "execution": { @@ -828,7 +1141,23 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title \"paste(\"Nonparametric Model, |\", rho, \"| = 1\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], "source": [ "pdf(\"figures/401K-contour-npm-lower.pdf\", width = 6, height = 6)\n", "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", @@ -839,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "5f1eab78", "metadata": { "execution": { @@ -852,7 +1181,23 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Plot with title \"paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")\"" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 525 + } + }, + "output_type": "display_data" + } + ], "source": [ "pdf(\"figures/401K-contour-npm-lower-rho2.pdf\", width = 6, height = 6)\n", "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", diff --git a/T-ADML/r-ADML-gas.irnb b/T-ADML/r-ADML-gas.irnb new file mode 100644 index 00000000..3a14290c --- /dev/null +++ b/T-ADML/r-ADML-gas.irnb @@ -0,0 +1,1166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c001", + "metadata": {}, + "source": [ + "# Empirical Example for Sensitivity Analysis: Gasoline Demand\n", + "\n", + "This is an adaptation of the replication package for Chernozhukov, Cinelli, Newey, Sharma and Syrgkanis,\n", + "*Long Story Short: Omitted Variable Bias in Causal Machine Learning*\n", + "\n", + "The original replication package can be found __[here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VH6QHQ)__.\n", + "\n", + "Note: This R notebook implements the sieve Riesz representer approach for the nonparametric estimator. The generalized random forest Riesz representer (ForestRiesz) used in the Python notebook has no direct R equivalent and is omitted here; the sieve candidates (degree-1 and degree-2 polynomial sieves) are used instead." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c002", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "repos <- c(CRAN = \"https://cloud.r-project.org\")\n", + "pkgs <- c(\"ranger\", \"glmnet\", \"ggplot2\", \"Matrix\")\n", + "for (p in pkgs) {\n", + " if (!requireNamespace(p, quietly = TRUE))\n", + " install.packages(p, repos = repos, quiet = TRUE)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c003", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "suppressMessages({\n", + " library(ranger)\n", + " library(glmnet)\n", + " library(ggplot2)\n", + "})\n", + "options(scipen = 99)\n", + "options(repr.plot.width = 5, repr.plot.height = 5, repr.plot.res = 150)" + ] + }, + { + "cell_type": "markdown", + "id": "c004", + "metadata": {}, + "source": [ + "## Setting\n", + "\n", + "The data come from the 2001 National Household Travel Survey and contain household gasoline consumption, gasoline prices, income brackets, and 24 observed controls describing population density, urbanization, demographics, and Census regions. After the sample restrictions used in the earlier demand studies, the analysis contains 3,466 households.\n", + "\n", + "The outcome $Y$ is log gasoline consumption, the treatment $D$ is log gasoline price, and $X^o$ collects the observed controls. The target is the average causal derivative $\\theta_s = \\mathrm{E}\\{\\partial_d g_s(D, X^o)\\}$, the average price elasticity of gasoline demand. The moment function below encodes $m(W_s, g) = \\partial_d g(D, X^o)$, approximated by a central finite difference with $\\epsilon = 0.01$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c005", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "moment_fn <- function(X, test_fn, epsilon = 0.01) {\n", + " t1 <- cbind(X[, 1L] + epsilon, X[, -1L, drop = FALSE])\n", + " t0 <- cbind(X[, 1L] - epsilon, X[, -1L, drop = FALSE])\n", + " (test_fn(t1) - test_fn(t0)) / (2 * epsilon)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c006", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "Following the earlier demand studies, households with very low prices or incomes are dropped. Besides the full sample (`q=5`), the analysis uses four income groups. The first three (`q=1,2,3`) are overlapping groups centered near annual incomes of \\$42,500, \\$57,500 and \\$72,500. The fourth (`q=4`) contains high-income households with income above roughly \\$110,000 ($\\log y \\geq 11.6$)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c007", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "get_data_r <- function(q = 5L, synthetic = FALSE, region = TRUE,\n", + " random_seed = 123L) {\n", + " data_url <- paste0(\n", + " \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/\",\n", + " \"update-notebooks/data/data_BHP2_with_region.csv\"\n", + " )\n", + " df <- read.csv(data_url, stringsAsFactors = FALSE)\n", + "\n", + " df <- df[df$log_p > log(1.2), ]\n", + " df <- df[df$log_y > log(15000), ]\n", + " if (!(q %in% c(4L, 5L))) {\n", + " df <- df[df$log_y >= log(42500) - 0.5 & df$log_y <= log(72500) + 0.5, ]\n", + " }\n", + " if (q == 1L) df <- df[df$log_y >= log(42500)-.5 & df$log_y <= log(42500)+.5, ]\n", + " if (q == 2L) df <- df[df$log_y >= log(57500)-.5 & df$log_y <= log(57500)+.5, ]\n", + " if (q == 3L) df <- df[df$log_y >= log(72500)-.5 & df$log_y <= log(72500)+.5, ]\n", + " if (q == 4L) df <- df[df$log_y >= 11.6, ]\n", + "\n", + " # Drop non-feature columns\n", + " drop_cols <- c(\"log_q\", \"distance_oil1000\", \"share\")\n", + " Xdf <- df[, !names(df) %in% drop_cols, drop = FALSE]\n", + "\n", + " if (region) {\n", + " lvls <- sort(unique(df$region))[-1L] # drop-first encoding\n", + " for (lv in lvls) {\n", + " Xdf[[paste0(\"region_\", lv)]] <- as.numeric(df$region == lv)\n", + " }\n", + " }\n", + " Xdf <- Xdf[, !names(Xdf) %in% c(\"state_fips\", \"region\"), drop = FALSE]\n", + "\n", + " X <- as.matrix(Xdf)\n", + " y <- df$log_q\n", + "\n", + " # 5-fold state-grouped splits\n", + " set.seed(random_seed)\n", + " states <- df$state_fips\n", + " state_ids <- sample(unique(states))\n", + " k <- 5L\n", + " fold_label <- rep_len(seq_len(k), length(state_ids))\n", + " state_fold <- setNames(fold_label, state_ids)\n", + " obs_fold <- state_fold[as.character(states)]\n", + " splits <- lapply(seq_len(k), function(i) {\n", + " list(train = which(obs_fold != i), test = which(obs_fold == i))\n", + " })\n", + "\n", + " list(X = X, y = y, splits = splits, col_names = colnames(Xdf))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c008", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Global parameters\n", + "q_grid <- 1:5\n", + "synthetic <- FALSE\n", + "region <- TRUE\n", + "verbose <- 0L" + ] + }, + { + "cell_type": "markdown", + "id": "c009", + "metadata": {}, + "source": [ + "## Module Definitions\n", + "\n", + "The following cells implement helper functions used by this notebook. The nonparametric estimator uses polynomial sieve Riesz representers (degree-1 and degree-2) fitted by L2 or elastic-net penalized regression. The partially linear estimator uses random forests for first-stage nuisance estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c010", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Polynomial-treatment x covariate feature matrix (cross product)\n", + "# Mirrors Python's Featurizer._transform: cross_product(poly(T), X[:,1:])\n", + "poly_cross_feats <- function(X, degree) {\n", + " n <- nrow(X)\n", + " p_x <- ncol(X) - 1L\n", + " T_poly <- outer(X[, 1L], seq(0L, degree), `^`) # n x (degree+1)\n", + " feats <- matrix(0, n, (degree + 1L) * p_x)\n", + " for (j in seq_len(degree + 1L)) {\n", + " idx <- ((j - 1L) * p_x + 1L):(j * p_x)\n", + " feats[, idx] <- T_poly[, j] * X[, -1L, drop = FALSE]\n", + " }\n", + " cbind(1.0, feats) # include intercept column\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c011", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Linear sieve Riesz representer with L2 penalty.\n", + "# Returns a prediction function function(X_new) -> numeric(nrow(X_new))\n", + "fit_sieve_rr <- function(X_train, moment_fn, alpha = 0, degree = 1L) {\n", + " Xf <- poly_cross_feats(X_train, degree)\n", + " n <- nrow(Xf)\n", + " p <- ncol(Xf)\n", + "\n", + " # Moment features: M[i,j] = moment_fn(X, phi_j) where phi_j(x) = Xf[i,j]\n", + " Mf <- matrix(0, n, p)\n", + " for (j in seq_len(p)) {\n", + " local({\n", + " jj <- j\n", + " Mf[, jj] <<- moment_fn(X_train,\n", + " function(Xnew) poly_cross_feats(Xnew, degree)[, jj])\n", + " })\n", + " }\n", + "\n", + " Sig <- crossprod(Xf) / n\n", + " reg <- alpha * diag(p); reg[1L, 1L] <- 0\n", + " params <- solve(Sig + reg + 1e-10 * diag(p), colMeans(Mf))\n", + "\n", + " function(Xnew) drop(poly_cross_feats(Xnew, degree) %*% params)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c012", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Elastic-net sieve Riesz representer via L-BFGS-B on the doubled variable.\n", + "# Mirrors Python's SparseSieveRR using scipy fmin_l_bfgs_b.\n", + "fit_sparse_sieve_rr <- function(X_train, moment_fn,\n", + " alpha = 0.01, l1_ratio = 1.0,\n", + " degree = 1L, tol = 1e-4) {\n", + " Xf <- poly_cross_feats(X_train, degree)\n", + " n <- nrow(Xf); p <- ncol(Xf)\n", + "\n", + " Mf <- matrix(0, n, p)\n", + " for (j in seq_len(p)) {\n", + " local({\n", + " jj <- j\n", + " Mf[, jj] <<- moment_fn(X_train,\n", + " function(Xnew) poly_cross_feats(Xnew, degree)[, jj])\n", + " })\n", + " }\n", + "\n", + " Sig <- crossprod(Xf) / n\n", + " mfeats <- colMeans(Mf)\n", + " a_l1 <- alpha * l1_ratio\n", + " a_l2 <- alpha * (1 - l1_ratio)\n", + "\n", + " loss_gr <- function(ec) {\n", + " coef <- ec[1:p] - ec[(p + 1):(2 * p)]\n", + " loss <- -2 * sum(mfeats * coef) + as.numeric(t(coef) %*% Sig %*% coef)\n", + " loss <- loss + a_l1 * sum(ec[2:p]) + a_l1 * sum(ec[(p + 2):(2 * p)])\n", + " loss <- loss + 0.5*a_l2*sum(ec[2:p]^2) + 0.5*a_l2*sum(ec[(p+2):(2*p)]^2)\n", + " gr <- 2 * as.numeric(Sig %*% coef - mfeats)\n", + " grad <- c(gr, -gr)\n", + " grad[2:p] <- grad[2:p] + a_l1 + a_l2 * ec[2:p]\n", + " grad[(p+2):(2*p)] <- grad[(p+2):(2*p)] + a_l1 + a_l2 * ec[(p+2):(2*p)]\n", + " list(value = loss, gradient = grad)\n", + " }\n", + "\n", + " res <- optim(\n", + " rep(0, 2 * p),\n", + " fn = function(ec) loss_gr(ec)$value,\n", + " gr = function(ec) loss_gr(ec)$gradient,\n", + " method = \"L-BFGS-B\",\n", + " lower = rep(0, 2 * p),\n", + " control = list(factr = tol / .Machine$double.eps, maxit = 1000L)\n", + " )\n", + " params <- res$par[1:p] - res$par[(p + 1):(2 * p)]\n", + " function(Xnew) drop(poly_cross_feats(Xnew, degree) %*% params)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c013", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Riesz score on held-out data: E[2*m(X;g) - g(X)^2] (higher is better)\n", + "riesz_score_r <- function(rr_fn, X_val, moment_fn)\n", + " mean(2 * moment_fn(X_val, rr_fn) - rr_fn(X_val)^2)\n", + "\n", + "# Select best Riesz candidate via CV, return a factory function(X_train) -> rr_fn\n", + "rr_fn_gen <- function(X, moment_fn, splits, ...) {\n", + " alpha_grid <- 10^seq(-5, 2, length.out = 8L)\n", + " best_score <- -Inf\n", + " best_factory <- NULL\n", + "\n", + " for (degree in c(1L, 2L)) {\n", + " for (alpha in alpha_grid) {\n", + " for (sparse in c(FALSE, TRUE)) {\n", + " scores <- vapply(splits, function(s) {\n", + " fit_fn <- if (sparse) fit_sparse_sieve_rr else fit_sieve_rr\n", + " m <- tryCatch(\n", + " fit_fn(X[s$train, , drop=FALSE], moment_fn,\n", + " alpha=alpha, degree=degree),\n", + " error = function(e) NULL)\n", + " if (is.null(m)) return(-Inf)\n", + " riesz_score_r(m, X[s$test, , drop=FALSE], moment_fn)\n", + " }, numeric(1L))\n", + " sc <- mean(scores[is.finite(scores)])\n", + " if (is.finite(sc) && sc > best_score) {\n", + " best_score <- sc\n", + " best_factory <- local({\n", + " d <- degree; a <- alpha; sp <- sparse\n", + " function(X_tr) {\n", + " if (sp) fit_sparse_sieve_rr(X_tr, moment_fn, alpha=a, degree=d)\n", + " else fit_sieve_rr(X_tr, moment_fn, alpha=a, degree=d)\n", + " }\n", + " })\n", + " }\n", + " }\n", + " }\n", + " }\n", + " if (is.null(best_factory))\n", + " best_factory <- function(X_tr)\n", + " fit_sieve_rr(X_tr, moment_fn, alpha=1e-2, degree=1L)\n", + " best_factory\n", + "}\n", + "\n", + "# Regression model selection: ranger vs. lasso (glmnet), return factory\n", + "reg_fn_gen <- function(X, y, splits, ...) {\n", + " # Scale matrix, clamping zero-SD columns to 1 to avoid NaN\n", + " safe_scale <- function(X, ctr = colMeans(X),\n", + " scl = { s <- apply(X, 2, sd); s[s < .Machine$double.eps] <- 1; s }) {\n", + " scale(X, center = ctr, scale = scl)\n", + " }\n", + "\n", + " # Evaluate ranger CV MSE\n", + " rf_preds <- numeric(nrow(X))\n", + " for (s in splits) {\n", + " df_tr <- as.data.frame(cbind(y_tr=y[s$train], X[s$train,,drop=FALSE]))\n", + " rf <- ranger(y_tr~., data=df_tr, num.trees=200L, verbose=FALSE)\n", + " rf_preds[s$test] <- predict(rf, data=as.data.frame(X[s$test,,drop=FALSE]))$predictions\n", + " }\n", + " rf_mse <- mean((y - rf_preds)^2)\n", + "\n", + " # Evaluate lasso CV MSE; fall back to ranger if glmnet errors\n", + " lasso_fit <- tryCatch(cv.glmnet(safe_scale(X), y, alpha=1, nfolds=5L),\n", + " error = function(e) NULL)\n", + " lasso_mse <- if (!is.null(lasso_fit)) min(lasso_fit$cvm) else Inf\n", + "\n", + " if (lasso_mse < rf_mse) {\n", + " function(X_tr, y_tr) {\n", + " ctr <- colMeans(X_tr)\n", + " scl <- apply(X_tr, 2, sd); scl[scl < .Machine$double.eps] <- 1\n", + " lf <- cv.glmnet(scale(X_tr, center=ctr, scale=scl), y_tr, alpha=1, nfolds=5L)\n", + " function(Xnew) as.numeric(predict(lf, newx=scale(Xnew, center=ctr, scale=scl), s=\"lambda.min\"))\n", + " }\n", + " } else {\n", + " function(X_tr, y_tr) {\n", + " df_tr <- as.data.frame(cbind(y_tr=y_tr, X_tr))\n", + " rf <- ranger(y_tr~., data=df_tr, num.trees=200L, verbose=FALSE)\n", + " function(Xnew) predict(rf, data=as.data.frame(Xnew))$predictions\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c014", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Doubly-robust cross-fitted moment estimator.\n", + "# rr_factory: function(X_train) -> rr prediction function\n", + "# get_reg_fn: function(X_train, y_train) -> reg prediction function (pre-built by reg_fn_gen)\n", + "debiased_moment_fit <- function(X, y, moment_fn, rr_factory, get_reg_fn, splits) {\n", + " n <- nrow(X)\n", + " rr_pred <- reg_pred <- moment_pred <- moment_rr <- numeric(n)\n", + " reg_fitter <- get_reg_fn # already the fitter: function(X_tr, y_tr) -> pred_fn\n", + "\n", + " for (s in splits) {\n", + " tr <- s$train; te <- s$test\n", + " rr_fn <- rr_factory(X[tr, , drop=FALSE])\n", + " reg_fn <- reg_fitter(X[tr, , drop=FALSE], y[tr])\n", + "\n", + " rr_pred[te] <- rr_fn(X[te, , drop=FALSE])\n", + " reg_pred[te] <- reg_fn(X[te, , drop=FALSE])\n", + " moment_pred[te] <- moment_fn(X[te, , drop=FALSE], reg_fn)\n", + " moment_rr[te] <- moment_fn(X[te, , drop=FALSE], rr_fn)\n", + " }\n", + " moment <- moment_pred + rr_pred * (y - reg_pred)\n", + " list(moment=moment, reg_pred=reg_pred, reisz_pred=rr_pred,\n", + " moment_reisz=moment_rr, y=y)\n", + "}\n", + "\n", + "avg_moment_r <- function(est, alpha = 0.1) {\n", + " m <- mean(est$moment); n <- length(est$moment)\n", + " se <- sd(est$moment) / sqrt(n)\n", + " h <- se * qt(1 - alpha/2, n - 1L)\n", + " c(point=m, se=se, lower=m-h, upper=m+h)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c015", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# -- Nonparametric (DebiasedMoment) sensitivity ------------------------------------\n", + "\n", + "sensitivity_bounds_single_r <- function(est, eta_ysq, eta_asq, alpha = NULL) {\n", + " nusq <- mean(2*est$moment_reisz - est$reisz_pred^2)\n", + " sigmasq <- mean((est$y - est$reg_pred)^2)\n", + " S <- sqrt(sigmasq * nusq)\n", + " Casq <- eta_asq / (1 - eta_asq)\n", + " theta <- mean(est$moment)\n", + " error <- S * sqrt(Casq * eta_ysq)\n", + " if (is.null(alpha)) return(c(theta - error, theta + error))\n", + " psi_theta <- est$moment - theta\n", + " psi_sigmasq <- (est$y - est$reg_pred)^2 - sigmasq\n", + " psi_nusq <- 2*est$moment_reisz - est$reisz_pred^2 - nusq\n", + " k <- sqrt(Casq * eta_ysq) / (2 * S)\n", + " phi_p <- psi_theta + k * (sigmasq*psi_nusq + nusq*psi_sigmasq)\n", + " phi_m <- psi_theta - k * (sigmasq*psi_nusq + nusq*psi_sigmasq)\n", + " n <- length(psi_theta)\n", + " c(theta-error, sqrt(mean(phi_m^2)/n), theta+error, sqrt(mean(phi_p^2)/n))\n", + "}\n", + "\n", + "sensitivity_bounds_r <- function(est_list, eta_ysq, eta_asq, alpha = NULL) {\n", + " res <- lapply(est_list, sensitivity_bounds_single_r,\n", + " eta_ysq=eta_ysq, eta_asq=eta_asq, alpha=alpha)\n", + " if (is.null(alpha)) {\n", + " return(c(mean(sapply(res, `[`, 1)), mean(sapply(res, `[`, 2))))\n", + " }\n", + " lower <- sapply(res,`[`,1); sl <- sapply(res,`[`,2)\n", + " upper <- sapply(res,`[`,3); su <- sapply(res,`[`,4)\n", + " z <- qnorm(1 - alpha)\n", + " c(mean(lower) - z*sqrt(mean(sl^2)+var(lower)),\n", + " mean(upper) + z*sqrt(mean(su^2)+var(upper)))\n", + "}\n", + "\n", + "tvalue_r <- function(est_list, value=0, leq=NULL, alpha=NULL) {\n", + " t_out <- 1\n", + " for (t in seq(0, 1, length.out=1000L)) {\n", + " b <- sensitivity_bounds_r(est_list, t, t, alpha=alpha)\n", + " if (is.null(leq) && value>=b[1] && value<=b[2]) { t_out <- t; break }\n", + " if (!is.null(leq) && leq && value>=b[1]) { t_out <- t; break }\n", + " if (!is.null(leq) && !leq && value<=b[2]) { t_out <- t; break }\n", + " }\n", + " t_out\n", + "}\n", + "\n", + "# -- PLM (DML residuals) sensitivity ------------------------------------------\n", + "\n", + "dml_sensitivity_bounds_single_r <- function(est, eta_ysq, eta_asq, alpha = NULL) {\n", + " nusq <- mean(est$Dres^2)\n", + " theta <- mean(est$yres * est$Dres) / nusq\n", + " sigmasq <- mean((est$yres - est$Dres * theta)^2)\n", + " S <- sqrt(sigmasq / nusq)\n", + " Casq <- eta_asq / (1 - eta_asq)\n", + " error <- S * sqrt(Casq * eta_ysq)\n", + " if (is.null(alpha)) return(c(theta - error, theta + error))\n", + " n <- length(est$yres)\n", + " psi_theta <- (est$yres - est$Dres * theta) * est$Dres / nusq\n", + " psi_sigmasq <- (est$yres - est$Dres * theta)^2 - sigmasq\n", + " psi_nusq <- est$Dres^2 - nusq\n", + " k <- sqrt(Casq * eta_ysq) / (2 * S)\n", + " phi_p <- psi_theta + k*(-(sigmasq/nusq^2)*psi_nusq + (1/nusq)*psi_sigmasq)\n", + " phi_m <- psi_theta - k*(-(sigmasq/nusq^2)*psi_nusq + (1/nusq)*psi_sigmasq)\n", + " c(theta-error, sqrt(mean(phi_m^2)/n), theta+error, sqrt(mean(phi_p^2)/n))\n", + "}\n", + "\n", + "dml_sensitivity_bounds_r <- function(est_list, eta_ysq, eta_asq, alpha = NULL) {\n", + " res <- lapply(est_list, dml_sensitivity_bounds_single_r,\n", + " eta_ysq=eta_ysq, eta_asq=eta_asq, alpha=alpha)\n", + " if (is.null(alpha)) {\n", + " return(c(mean(sapply(res,`[`,1)), mean(sapply(res,`[`,2))))\n", + " }\n", + " lower <- sapply(res,`[`,1); sl <- sapply(res,`[`,2)\n", + " upper <- sapply(res,`[`,3); su <- sapply(res,`[`,4)\n", + " z <- qnorm(1 - alpha)\n", + " c(mean(lower) - z*sqrt(mean(sl^2)+var(lower)),\n", + " mean(upper) + z*sqrt(mean(su^2)+var(upper)))\n", + "}\n", + "\n", + "dml_tvalue_r <- function(est_list, value=0, leq=NULL, alpha=NULL) {\n", + " t_out <- 1\n", + " for (t in seq(0, 1, length.out=1000L)) {\n", + " b <- dml_sensitivity_bounds_r(est_list, t, t, alpha=alpha)\n", + " if (is.null(leq) && value>=b[1] && value<=b[2]) { t_out <- t; break }\n", + " if (!is.null(leq) && leq && value>=b[1]) { t_out <- t; break }\n", + " if (!is.null(leq) && !leq && value<=b[2]) { t_out <- t; break }\n", + " }\n", + " t_out\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c016", + "metadata": {}, + "source": [ + "## Short Estimates Under Conditional Ignorability" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c017", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Main estimation loop matching the Python notebook structure.\n", + "# estimator_fn(X, y, splits) returns list(estimates=c(point,se,lower,upper), model=...)\n", + "generic_pipeline_r <- function(estimator_fn, q_grid, name, n_seeds = 5L) {\n", + " all_res <- list()\n", + " all_ests <- list()\n", + " for (q in q_grid) {\n", + " cat(sprintf(\"q=%d\\n\", q))\n", + " est_mat <- matrix(NA, n_seeds, 4L)\n", + " seed_ests <- list()\n", + " for (i in seq_len(n_seeds)) {\n", + " seed <- 122L + i\n", + " dat <- get_data_r(q, region=region, synthetic=synthetic, random_seed=seed)\n", + " out <- estimator_fn(dat$X, dat$y, dat$splits)\n", + " est_mat[i, ] <- out$estimates\n", + " seed_ests[[i]] <- out$model\n", + " cat(sprintf(\" seed=%d: %.3f\\n\", seed, out$estimates[1]))\n", + " }\n", + " key <- sprintf(\"q=%d\", q)\n", + " all_res[[key]] <- data.frame(\n", + " method = name,\n", + " point = mean(est_mat[, 1]),\n", + " stderr = sqrt(mean(est_mat[, 2]^2) + var(est_mat[, 1]))\n", + " )\n", + " all_ests[[key]] <- seed_ests\n", + " }\n", + " list(res=all_res, ests=all_ests)\n", + "}\n", + "\n", + "transform_res_to_df_r <- function(res_list, q_grid, alpha = NULL) {\n", + " rows <- list()\n", + " for (q in q_grid) {\n", + " key <- sprintf(\"q=%d\", q)\n", + " r <- res_list[[key]]\n", + " if (!is.null(alpha)) {\n", + " z <- qnorm(1 - alpha)\n", + " lo <- r$point - z * r$stderr\n", + " hi <- r$point + z * r$stderr\n", + " rows[[key]] <- sprintf(\"%.2f (%.2f) [%.2f, %.2f]\", r$point, r$stderr, lo, hi)\n", + " } else {\n", + " rows[[key]] <- sprintf(\"%.2f\", r$point)\n", + " }\n", + " }\n", + " as.data.frame(rows, check.names=FALSE)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c018", + "metadata": {}, + "source": [ + "### Nonparametric Model\n", + "\n", + "The first specification leaves the short regression $g_s(D, X^o)$ unrestricted. The Riesz representer is estimated from the Riesz loss using sieve candidates (degree-1 and degree-2 polynomial features crossed with covariates), selected by cross-validated Riesz score. Random forests are used for the regression model. Note: the Python notebook additionally considers a generalized random forest Riesz representer (ForestRiesz); that candidate is omitted here as it has no direct R equivalent." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c019", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "nonlinrr_r <- function(X, y, splits) {\n", + " rr_factory <- rr_fn_gen(X, moment_fn, splits)\n", + " get_reg <- reg_fn_gen(X, y, splits)\n", + "\n", + " fit <- debiased_moment_fit(X, y, moment_fn, rr_factory, get_reg, splits)\n", + " list(estimates = avg_moment_r(fit, alpha = 0.1), model = fit)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c020", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "q=1\n", + " seed=123: -0.607\n", + " seed=124: -0.611\n", + " seed=125: -0.628\n", + " seed=126: 0.050\n", + " seed=127: -0.830\n", + "q=2\n", + " seed=123: -0.521\n", + " seed=124: -0.565\n", + " seed=125: -0.657\n", + " seed=126: 0.028\n", + " seed=127: -0.843\n", + "q=3\n", + " seed=123: -0.492\n", + " seed=124: -0.583\n", + " seed=125: -0.729\n", + " seed=126: 0.056\n", + " seed=127: -0.738\n", + "q=4\n", + " seed=123: -0.498\n", + " seed=124: -0.694\n", + " seed=125: -0.696\n", + " seed=126: -1.316\n", + " seed=127: -3.188\n", + "q=5\n", + " seed=123: -0.179\n", + " seed=124: -0.381\n", + " seed=125: -0.586\n", + " seed=126: 0.225\n", + " seed=127: -0.675\n" + ] + } + ], + "source": [ + "rr_results <- generic_pipeline_r(nonlinrr_r, q_grid, \"dr\", n_seeds = 5L)\n", + "res_rr <- rr_results$res\n", + "ests_rr <- rr_results$ests" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c021", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.frame: 1 × 5
q=1q=2q=3q=4q=5
<chr><chr><chr><chr><chr>
-0.53 (0.45) [-1.26, 0.21]-0.51 (0.41) [-1.18, 0.16]-0.50 (0.42) [-1.18, 0.19]-1.28 (1.22) [-3.28, 0.72]-0.32 (0.41) [-0.99, 0.36]
\n" + ], + "text/latex": [ + "A data.frame: 1 × 5\n", + "\\begin{tabular}{lllll}\n", + " q=1 & q=2 & q=3 & q=4 & q=5\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t -0.53 (0.45) {[}-1.26, 0.21{]} & -0.51 (0.41) {[}-1.18, 0.16{]} & -0.50 (0.42) {[}-1.18, 0.19{]} & -1.28 (1.22) {[}-3.28, 0.72{]} & -0.32 (0.41) {[}-0.99, 0.36{]}\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 1 × 5\n", + "\n", + "| q=1 <chr> | q=2 <chr> | q=3 <chr> | q=4 <chr> | q=5 <chr> |\n", + "|---|---|---|---|---|\n", + "| -0.53 (0.45) [-1.26, 0.21] | -0.51 (0.41) [-1.18, 0.16] | -0.50 (0.42) [-1.18, 0.19] | -1.28 (1.22) [-3.28, 0.72] | -0.32 (0.41) [-0.99, 0.36] |\n", + "\n" + ], + "text/plain": [ + " q=1 q=2 \n", + "1 -0.53 (0.45) [-1.26, 0.21] -0.51 (0.41) [-1.18, 0.16]\n", + " q=3 q=4 \n", + "1 -0.50 (0.42) [-1.18, 0.19] -1.28 (1.22) [-3.28, 0.72]\n", + " q=5 \n", + "1 -0.32 (0.41) [-0.99, 0.36]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transform_res_to_df_r(res_rr, q_grid, alpha = 0.05)" + ] + }, + { + "cell_type": "markdown", + "id": "c022", + "metadata": {}, + "source": [ + "### Partially Linear Model\n", + "\n", + "The second specification is the partially linear model $Y = \\theta_s D + f_s(X^o) + \\varepsilon$, estimated by partialling out with random forests." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c023", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "dml_estimator_r <- function(X, y, splits) {\n", + " n <- nrow(X)\n", + " D <- X[, 1L]\n", + " Xcov <- X[, -1L, drop = FALSE]\n", + " yres <- Dres <- numeric(n)\n", + "\n", + " for (s in splits) {\n", + " tr <- s$train; te <- s$test\n", + " df_y <- as.data.frame(cbind(y_tr = y[tr], Xcov[tr, ]))\n", + " df_d <- as.data.frame(cbind(D_tr = D[tr], Xcov[tr, ]))\n", + " rf_y <- ranger(y_tr ~ ., data = df_y, num.trees = 200L, verbose = FALSE)\n", + " rf_d <- ranger(D_tr ~ ., data = df_d, num.trees = 200L, verbose = FALSE)\n", + " yres[te] <- y[te] - predict(rf_y, data=as.data.frame(Xcov[te,]))$predictions\n", + " Dres[te] <- D[te] - predict(rf_d, data=as.data.frame(Xcov[te,]))$predictions\n", + " }\n", + "\n", + " nusq <- mean(Dres^2)\n", + " theta <- mean(yres * Dres) / nusq\n", + " psi <- (yres - Dres * theta) * Dres / nusq\n", + " se <- sqrt(mean(psi^2) / n)\n", + " list(\n", + " estimates = c(point=theta, se=se, lower=theta-1.645*se, upper=theta+1.645*se),\n", + " model = list(yres=yres, Dres=Dres)\n", + " )\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c024", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "q=1\n", + " seed=123: -0.401\n", + " seed=124: -0.070\n", + " seed=125: -0.252\n", + " seed=126: 0.132\n", + " seed=127: -0.782\n", + "q=2\n", + " seed=123: -0.555\n", + " seed=124: -0.516\n", + " seed=125: -0.684\n", + " seed=126: -0.102\n", + " seed=127: -0.911\n", + "q=3\n", + " seed=123: -0.575\n", + " seed=124: -0.726\n", + " seed=125: -1.030\n", + " seed=126: -0.209\n", + " seed=127: -1.048\n", + "q=4\n", + " seed=123: -0.905\n", + " seed=124: -1.333\n", + " seed=125: -1.214\n", + " seed=126: -1.359\n", + " seed=127: -1.424\n", + "q=5\n", + " seed=123: -0.230\n", + " seed=124: -0.422\n", + " seed=125: -0.589\n", + " seed=126: 0.136\n", + " seed=127: -0.928\n" + ] + } + ], + "source": [ + "dml_results <- generic_pipeline_r(dml_estimator_r, q_grid, \"dml\", n_seeds = 5L)\n", + "res_dml <- dml_results$res\n", + "ests_dml <- dml_results$ests" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c025", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.frame: 1 × 5
q=1q=2q=3q=4q=5
<chr><chr><chr><chr><chr>
-0.27 (0.50) [-1.10, 0.55]-0.55 (0.42) [-1.24, 0.14]-0.72 (0.47) [-1.50, 0.06]-1.25 (0.55) [-2.16, -0.34]-0.41 (0.47) [-1.17, 0.36]
\n" + ], + "text/latex": [ + "A data.frame: 1 × 5\n", + "\\begin{tabular}{lllll}\n", + " q=1 & q=2 & q=3 & q=4 & q=5\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t -0.27 (0.50) {[}-1.10, 0.55{]} & -0.55 (0.42) {[}-1.24, 0.14{]} & -0.72 (0.47) {[}-1.50, 0.06{]} & -1.25 (0.55) {[}-2.16, -0.34{]} & -0.41 (0.47) {[}-1.17, 0.36{]}\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 1 × 5\n", + "\n", + "| q=1 <chr> | q=2 <chr> | q=3 <chr> | q=4 <chr> | q=5 <chr> |\n", + "|---|---|---|---|---|\n", + "| -0.27 (0.50) [-1.10, 0.55] | -0.55 (0.42) [-1.24, 0.14] | -0.72 (0.47) [-1.50, 0.06] | -1.25 (0.55) [-2.16, -0.34] | -0.41 (0.47) [-1.17, 0.36] |\n", + "\n" + ], + "text/plain": [ + " q=1 q=2 \n", + "1 -0.27 (0.50) [-1.10, 0.55] -0.55 (0.42) [-1.24, 0.14]\n", + " q=3 q=4 \n", + "1 -0.72 (0.47) [-1.50, 0.06] -1.25 (0.55) [-2.16, -0.34]\n", + " q=5 \n", + "1 -0.41 (0.47) [-1.17, 0.36]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transform_res_to_df_r(res_dml, q_grid, alpha = 0.05)" + ] + }, + { + "cell_type": "markdown", + "id": "c026", + "metadata": {}, + "source": [ + "## Sensitivity Results for the Full Sample\n", + "\n", + "For the full sample we report the short elasticity, the sensitivity bounds and their confidence bounds under the scenario $C_Y^2 = 0.03$, $C_D^2 \\approx 0.03$ and $|\\rho| = 1$, together with robustness values." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c027", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "table_rr <- list()\n", + "q5_ests <- ests_rr[[\"q=5\"]]\n", + "\n", + "table_rr[[\"point\"]] <- sprintf(\"%.3f (%.3f)\",\n", + " res_rr[[\"q=5\"]]$point, res_rr[[\"q=5\"]]$stderr)\n", + "table_rr[[\"Bounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha=NULL)[1],\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha=NULL)[2])\n", + "table_rr[[\"CIBounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha=.05)[1],\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha=.05)[2])\n", + "table_rr[[\"RV(v=-1.5)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=-1.5, leq=TRUE))\n", + "table_rr[[\"RV(v=-1.5,a=.05)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=-1.5, leq=TRUE, alpha=.05))\n", + "table_rr[[\"RV(v=0)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=0, leq=FALSE))\n", + "table_rr[[\"RV(v=0,a=.05)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=0, leq=FALSE, alpha=.05))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c028", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "table_dml <- list()\n", + "q5_dml <- ests_dml[[\"q=5\"]]\n", + "\n", + "table_dml[[\"point\"]] <- sprintf(\"%.3f (%.3f)\",\n", + " res_dml[[\"q=5\"]]$point, res_dml[[\"q=5\"]]$stderr)\n", + "table_dml[[\"Bounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=NULL)[1],\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=NULL)[2])\n", + "table_dml[[\"CIBounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=.05)[1],\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=.05)[2])\n", + "table_dml[[\"RV(v=-1.5)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=-1.5, leq=TRUE))\n", + "table_dml[[\"RV(v=-1.5,a=.05)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=-1.5, leq=TRUE, alpha=.05))\n", + "table_dml[[\"RV(v=0)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=0, leq=FALSE))\n", + "table_dml[[\"RV(v=0,a=.05)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=0, leq=FALSE, alpha=.05))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c029", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 2 × 7
pointBoundsCIBoundsRV(v=-1.5)RV(v=-1.5,a=.05)RV(v=0)RV(v=0,a=.05)
<chr><chr><chr><chr><chr><chr><chr>
Non-parametric-0.319 (0.410)[-0.718, 0.080][-1.408, 0.740]0.0870.0370.0250.000
Partially linear-0.406 (0.465)[-0.848, 0.035][-1.647, 0.769]0.0730.0210.0280.000
\n" + ], + "text/latex": [ + "A data.frame: 2 × 7\n", + "\\begin{tabular}{r|lllllll}\n", + " & point & Bounds & CIBounds & RV(v=-1.5) & RV(v=-1.5,a=.05) & RV(v=0) & RV(v=0,a=.05)\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\tNon-parametric & -0.319 (0.410) & {[}-0.718, 0.080{]} & {[}-1.408, 0.740{]} & 0.087 & 0.037 & 0.025 & 0.000\\\\\n", + "\tPartially linear & -0.406 (0.465) & {[}-0.848, 0.035{]} & {[}-1.647, 0.769{]} & 0.073 & 0.021 & 0.028 & 0.000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 2 × 7\n", + "\n", + "| | point <chr> | Bounds <chr> | CIBounds <chr> | RV(v=-1.5) <chr> | RV(v=-1.5,a=.05) <chr> | RV(v=0) <chr> | RV(v=0,a=.05) <chr> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| Non-parametric | -0.319 (0.410) | [-0.718, 0.080] | [-1.408, 0.740] | 0.087 | 0.037 | 0.025 | 0.000 |\n", + "| Partially linear | -0.406 (0.465) | [-0.848, 0.035] | [-1.647, 0.769] | 0.073 | 0.021 | 0.028 | 0.000 |\n", + "\n" + ], + "text/plain": [ + " point Bounds CIBounds RV(v=-1.5)\n", + "Non-parametric -0.319 (0.410) [-0.718, 0.080] [-1.408, 0.740] 0.087 \n", + "Partially linear -0.406 (0.465) [-0.848, 0.035] [-1.647, 0.769] 0.073 \n", + " RV(v=-1.5,a=.05) RV(v=0) RV(v=0,a=.05)\n", + "Non-parametric 0.037 0.025 0.000 \n", + "Partially linear 0.021 0.028 0.000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tabledf <- data.frame(\n", + " rbind(unlist(table_rr), unlist(table_dml)),\n", + " row.names = c(\"Non-parametric\", \"Partially linear\"),\n", + " check.names = FALSE\n", + ")\n", + "tabledf" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c030", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 375, + "width": 375 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "qgr <- 1:4\n", + "point <- sapply(qgr, function(q) res_rr[[sprintf(\"q=%d\",q)]]$point)\n", + "se <- sapply(qgr, function(q) res_rr[[sprintf(\"q=%d\",q)]]$stderr)\n", + "lower <- point - 1.645 * se\n", + "upper <- point + 1.645 * se\n", + "sens_lo <- sapply(qgr, function(q)\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[1])\n", + "sens_hi <- sapply(qgr, function(q)\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[2])\n", + "sens_lo95 <- sapply(qgr, function(q)\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[1])\n", + "sens_hi95 <- sapply(qgr, function(q)\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[2])\n", + "\n", + "df_rr <- data.frame(q=qgr, point, lower, upper, sens_lo, sens_hi, sens_lo95, sens_hi95)\n", + "\n", + "ggplot(df_rr, aes(x=q)) +\n", + " geom_line(aes(y=point)) +\n", + " geom_ribbon(aes(ymin=lower, ymax=upper), alpha=.4, fill=\"steelblue\") +\n", + " geom_ribbon(aes(ymin=sens_lo, ymax=sens_hi), alpha=.6, fill=\"orange\") +\n", + " geom_ribbon(aes(ymin=sens_lo95, ymax=sens_hi95), alpha=.2, fill=\"orange\") +\n", + " scale_x_continuous(breaks=qgr) +\n", + " xlab(\"Income group\") + ylab(\"Average price elasticity\") +\n", + " ggtitle(\"Non-parametric\") +\n", + " theme_bw()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c031", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 375, + "width": 375 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "point_d <- sapply(qgr, function(q) res_dml[[sprintf(\"q=%d\",q)]]$point)\n", + "se_d <- sapply(qgr, function(q) res_dml[[sprintf(\"q=%d\",q)]]$stderr)\n", + "lower_d <- point_d - 1.645 * se_d\n", + "upper_d <- point_d + 1.645 * se_d\n", + "sens_lo_d <- sapply(qgr, function(q)\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[1])\n", + "sens_hi_d <- sapply(qgr, function(q)\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[2])\n", + "sens_lo95_d <- sapply(qgr, function(q)\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[1])\n", + "sens_hi95_d <- sapply(qgr, function(q)\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[2])\n", + "\n", + "df_dml <- data.frame(q=qgr, point=point_d, lower=lower_d, upper=upper_d,\n", + " sens_lo=sens_lo_d, sens_hi=sens_hi_d,\n", + " sens_lo95=sens_lo95_d, sens_hi95=sens_hi95_d)\n", + "\n", + "ggplot(df_dml, aes(x=q)) +\n", + " geom_line(aes(y=point)) +\n", + " geom_ribbon(aes(ymin=lower, ymax=upper), alpha=.4, fill=\"steelblue\") +\n", + " geom_ribbon(aes(ymin=sens_lo, ymax=sens_hi), alpha=.6, fill=\"orange\") +\n", + " geom_ribbon(aes(ymin=sens_lo95, ymax=sens_hi95), alpha=.2, fill=\"orange\") +\n", + " scale_x_continuous(breaks=qgr) +\n", + " xlab(\"Income group\") + ylab(\"Average price elasticity\") +\n", + " ggtitle(\"Partially linear\") +\n", + " theme_bw()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c1cd11342f111df838701c39dd51dbc4bacdc151 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:26 +0200 Subject: [PATCH 24/32] CI: run all folders, add T-ADML, fix system deps and lint config Co-Authored-By: Claude Opus 5.5 --- .github/workflows/check-R-notebooks.yml | 17 +++++++++++------ .github/workflows/check-python-notebooks.yml | 20 ++++++++++---------- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/.github/workflows/check-R-notebooks.yml b/.github/workflows/check-R-notebooks.yml index 06b9e43a..00994452 100644 --- a/.github/workflows/check-R-notebooks.yml +++ b/.github/workflows/check-R-notebooks.yml @@ -15,8 +15,9 @@ jobs: test-R-notebooks: runs-on: ubuntu-latest strategy: + fail-fast: false matrix: - directory: ['PM1', 'PM2', 'PM3', 'PM4', 'PM5', 'CM1', 'CM2', 'CM3', 'AC1', 'AC2', 'T'] + directory: ['PM1', 'PM2', 'PM3', 'PM4', 'PM5', 'CM1', 'CM2', 'CM3', 'AC1', 'AC2', 'T', 'T-ADML'] steps: - name: Checkout repository uses: actions/checkout@v3 @@ -37,21 +38,21 @@ jobs: grep -E '^${{ matrix.directory }}/[^/]+\.Rmd$' changed_files.txt exit 1 fi - grep -E '^${{ matrix.directory }}/[^/]+\.irnb$|^${{ matrix.directory }}/.*\.R|\.github/workflows/check-and-transform-R-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt + grep -E '^${{ matrix.directory }}/[^/]+\.irnb$|^${{ matrix.directory }}/.*\.R|\.github/workflows/check-R-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt - name: Find changed notebooks in Push if: github.event_name == 'push' id: find_notebooks_push run: | git diff --name-only ${{ github.event.before }} ${{ github.event.after }} > changed_files.txt - grep -E '^${{ matrix.directory }}/[^/]+\.irnb$|^${{ matrix.directory }}/.*\.R|\.github/workflows/check-and-transform-R-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt + grep -E '^${{ matrix.directory }}/[^/]+\.irnb$|^${{ matrix.directory }}/.*\.R|\.github/workflows/check-R-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt - name: Check if any notebooks changed in PR or Push if: (github.event_name == 'push') || (github.event_name == 'pull_request') id: check_notebooks run: | cat changed_notebooks.txt - if grep -q -E '^${{ matrix.directory }}/[^/]+\.irnb$|^${{ matrix.directory }}/.*\.R|\.github/workflows/check-and-transform-R-notebooks.yml$' changed_notebooks.txt; then + if grep -q -E '^${{ matrix.directory }}/[^/]+\.irnb$|^${{ matrix.directory }}/.*\.R|\.github/workflows/check-R-notebooks.yml$' changed_notebooks.txt; then echo "notebooks_changed=true" >> $GITHUB_ENV else echo "notebooks_changed=false" >> $GITHUB_ENV @@ -70,7 +71,7 @@ jobs: if: env.notebooks_changed == 'true' run: | sudo apt-get update - sudo apt-get install -y libcurl4-openssl-dev + sudo apt-get install -y libcurl4-openssl-dev libuv1-dev libglpk-dev - name: Set up Python if: env.notebooks_changed == 'true' @@ -125,7 +126,9 @@ jobs: library(lintr) linters <- linters_with_defaults(line_length_linter = line_length_linter(120), object_name_linter = object_name_linter(styles = c("snake_case", "CamelCase", "camelCase")), - object_usage_linter = NULL) + object_usage_linter = NULL, + pipe_consistency_linter = NULL, + return_linter = NULL) rmd_files <- list.files(path = "${{ matrix.directory }}", pattern = "\\.Rmd$", full.names = TRUE) linting_error <- FALSE for (rfile in rmd_files) { @@ -195,6 +198,8 @@ jobs: ' 2>/dev/null env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + # let R (reticulate/keras) use the Python with TensorFlow installed above + RETICULATE_PYTHON: ${{ env.pythonLocation }}/bin/python - name: Upload execution log if: "((env.notebooks_changed == 'true') && (env.linting_errors_found == 'false'))" diff --git a/.github/workflows/check-python-notebooks.yml b/.github/workflows/check-python-notebooks.yml index bea8cf8b..4ea57fcf 100644 --- a/.github/workflows/check-python-notebooks.yml +++ b/.github/workflows/check-python-notebooks.yml @@ -15,10 +15,11 @@ jobs: test-python-notebooks: runs-on: ${{ matrix.os }} strategy: + fail-fast: false matrix: # Removed: {windows-latest, macos-latest} as they were taking too long. Consider adding back in the future os: [ubuntu-latest] - directory: [PM1, PM2, PM3, PM4, PM5, CM1, CM2, CM3, AC1, AC2, T] + directory: [PM1, PM2, PM3, PM4, PM5, CM1, CM2, CM3, AC1, AC2, T, T-ADML] steps: - name: Checkout repository @@ -34,21 +35,21 @@ jobs: # git fetch origin ${{ github.event.pull_request.base.ref }} ${{ github.event.pull_request.head.ref }} # git diff --name-only origin/${{ github.event.pull_request.base.ref }}...origin/${{ github.event.pull_request.head.ref }} > changed_files.txt git diff --name-only -r HEAD^1 HEAD > changed_files.txt - grep -E '\.ipynb$|\.py$|\.github/workflows/python-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt + grep -E '\.ipynb$|\.py$|\.github/workflows/check-python-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt - name: Find changed notebooks in Push if: github.event_name == 'push' id: find_notebooks_push run: | git diff --name-only ${{ github.event.before }} ${{ github.event.after }} > changed_files.txt - grep -E '\.ipynb$|\.py$|\.github/workflows/python-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt + grep -E '\.ipynb$|\.py$|\.github/workflows/check-python-notebooks.yml$' changed_files.txt > changed_notebooks.txt || echo "No notebooks changed" > changed_notebooks.txt - name: Check if any notebooks changed in PR or Push if: (github.event_name == 'push') || (github.event_name == 'pull_request') id: check_notebooks run: | cat changed_notebooks.txt - if grep -q -E '^${{ matrix.directory }}/[^/]+\.ipynb$|^${{ matrix.directory }}/.*\.py$|\.github/workflows/python-notebooks.yml$' changed_notebooks.txt; then + if grep -q -E '^${{ matrix.directory }}/[^/]+\.ipynb$|^${{ matrix.directory }}/.*\.py$|\.github/workflows/check-python-notebooks.yml$' changed_notebooks.txt; then echo "notebooks_changed=true" >> $GITHUB_ENV else echo "notebooks_changed=false" >> $GITHUB_ENV @@ -81,12 +82,9 @@ jobs: run: sudo apt-get install -y libgraphviz-dev shell: bash - - name: Install R if folder is CM3 - if: "(env.notebooks_changed == 'true') && (matrix.directory == 'CM3' && matrix.os == 'ubuntu-latest')" - run: | - sudo apt-get update - sudo apt-get install -y r-base - shell: bash + - name: Install R if folder is CM3 or T-ADML + if: "(env.notebooks_changed == 'true') && ((matrix.directory == 'CM3' || matrix.directory == 'T-ADML') && matrix.os == 'ubuntu-latest')" + uses: r-lib/actions/setup-r@v2 - name: Set up Python if: "(env.notebooks_changed == 'true') && (! (matrix.directory == 'CM3' && (matrix.os == 'windows-latest' || matrix.os == 'macos-latest')))" @@ -117,6 +115,8 @@ jobs: flake8_errors=0 for notebook in ${{ matrix.directory }}/*.ipynb; do [ "$(basename "$notebook")" = "python-dosearch.ipynb" ] && continue + # uses %%R (rpy2) cells, which flake8 cannot parse + [ "$(basename "$notebook")" = "python-ADML-401k.ipynb" ] && continue flake8-nb --config=.flake8 "$notebook" > "linting_logs/$(basename "$notebook" .ipynb)_linting.txt" 2>&1 || flake8_errors=$((flake8_errors+1)) done echo "flake8_errors=$flake8_errors" >> $GITHUB_ENV From 73aec5fd1c22ff4e69dd8997ff454b70893ba3c7 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:26 +0200 Subject: [PATCH 25/32] requirements: raise scikit-learn pin to >=1.4,<1.7 Co-Authored-By: Claude Opus 5.5 --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 0ef9f635..49b528e7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ numpy>=1.25.2 pandas>=2.0.3 -scikit-learn>=1.2.2,<1.3.0 +scikit-learn>=1.4,<1.7 statsmodels>=0.14.2 jupyterlab matplotlib>=3.7.1 From b704925240eb919521c3686c652a16e59cf27e46 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:26 +0200 Subject: [PATCH 26/32] Install hdmpy from PyPI instead of cloning the repo Co-Authored-By: Claude Opus 5.5 --- PM1/python-ols-and-lasso-for-wage-gap-inference.ipynb | 2 +- PM2/python_convergence_hypothesis_double_lasso.ipynb | 2 +- PM2/python_experiment_non_orthogonal.ipynb | 4 ++-- PM2/python_heterogeneous_wage_effects.ipynb | 4 ++-- PM2/python_linear_penalized_regs.ipynb | 2 +- PM2/python_ml_for_wage_prediction.ipynb | 2 +- PM2/python_orthogonal_orig.ipynb | 2 +- ...python_debiased_ml_for_partially_linear_model_growth.ipynb | 2 +- 8 files changed, 10 insertions(+), 10 deletions(-) diff --git a/PM1/python-ols-and-lasso-for-wage-gap-inference.ipynb b/PM1/python-ols-and-lasso-for-wage-gap-inference.ipynb index b9eebc10..4a7658fd 100644 --- a/PM1/python-ols-and-lasso-for-wage-gap-inference.ipynb +++ b/PM1/python-ols-and-lasso-for-wage-gap-inference.ipynb @@ -490,7 +490,7 @@ "source": [ "!pip install multiprocess\n", "!pip install pyreadr\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git" + "!pip install hdmpy" ] }, { diff --git a/PM2/python_convergence_hypothesis_double_lasso.ipynb b/PM2/python_convergence_hypothesis_double_lasso.ipynb index e469fe3a..00dc2ef0 100644 --- a/PM2/python_convergence_hypothesis_double_lasso.ipynb +++ b/PM2/python_convergence_hypothesis_double_lasso.ipynb @@ -32,7 +32,7 @@ "source": [ "!pip install multiprocess\n", "!pip install pyreadr\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git" + "!pip install hdmpy" ] }, { diff --git a/PM2/python_experiment_non_orthogonal.ipynb b/PM2/python_experiment_non_orthogonal.ipynb index 7d0948d2..6214d6af 100644 --- a/PM2/python_experiment_non_orthogonal.ipynb +++ b/PM2/python_experiment_non_orthogonal.ipynb @@ -27,7 +27,7 @@ "```\n", "!pip install multiprocess\n", "!pip install pyreadr\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git\n", + "!pip install hdmpy\n", "```" ] }, @@ -46,7 +46,7 @@ "source": [ "!pip install multiprocess\n", "!pip install pyreadr\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git" + "!pip install hdmpy" ] }, { diff --git a/PM2/python_heterogeneous_wage_effects.ipynb b/PM2/python_heterogeneous_wage_effects.ipynb index 4abb2421..1ab72687 100644 --- a/PM2/python_heterogeneous_wage_effects.ipynb +++ b/PM2/python_heterogeneous_wage_effects.ipynb @@ -167,7 +167,7 @@ "To estimate lasso using the theoretically motivated penalty level, we just use the hdmpy package. To install it run\n", "```\n", "!pip install multiprocess\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git\n", + "!pip install hdmpy\n", "```\n", "\n", "You can run the cells below and then repeat the whole analysis above using the newly defined `lasso_model` variable." @@ -186,7 +186,7 @@ "outputs": [], "source": [ "!pip install multiprocess\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git" + "!pip install hdmpy" ] }, { diff --git a/PM2/python_linear_penalized_regs.ipynb b/PM2/python_linear_penalized_regs.ipynb index 95cfe0ed..9a5eb07f 100644 --- a/PM2/python_linear_penalized_regs.ipynb +++ b/PM2/python_linear_penalized_regs.ipynb @@ -348,7 +348,7 @@ }, "outputs": [], "source": [ - "!git clone https://github.com/maxhuppertz/hdmpy.git\n", + "!pip install hdmpy\n", "!pip install multiprocess" ] }, diff --git a/PM2/python_ml_for_wage_prediction.ipynb b/PM2/python_ml_for_wage_prediction.ipynb index 8552a3e0..42b265c6 100644 --- a/PM2/python_ml_for_wage_prediction.ipynb +++ b/PM2/python_ml_for_wage_prediction.ipynb @@ -689,7 +689,7 @@ }, "outputs": [], "source": [ - "!git clone https://github.com/maxhuppertz/hdmpy.git\n", + "!pip install hdmpy\n", "!pip install multiprocess" ] }, diff --git a/PM2/python_orthogonal_orig.ipynb b/PM2/python_orthogonal_orig.ipynb index f8c44056..d48b6fcf 100644 --- a/PM2/python_orthogonal_orig.ipynb +++ b/PM2/python_orthogonal_orig.ipynb @@ -34,7 +34,7 @@ "# As before, we import clone hdmpy to use rlasso functions in python\n", "!pip install multiprocess\n", "!pip install pyreadr\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git" + "!pip install hdmpy" ] }, { diff --git a/PM4/python_debiased_ml_for_partially_linear_model_growth.ipynb b/PM4/python_debiased_ml_for_partially_linear_model_growth.ipynb index 05887083..c531d365 100644 --- a/PM4/python_debiased_ml_for_partially_linear_model_growth.ipynb +++ b/PM4/python_debiased_ml_for_partially_linear_model_growth.ipynb @@ -277,7 +277,7 @@ "!pip install multiprocess\n", "\n", "\n", - "!git clone https://github.com/maxhuppertz/hdmpy.git" + "!pip install hdmpy" ] }, { From 6d39022751dd2cdc42b114c597f582e0642e4658 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:27 +0200 Subject: [PATCH 27/32] Pin econml 0.16.0 where it is installed Co-Authored-By: Claude Opus 5.5 --- PM4/python-dml-401k.ipynb | 2 +- T/CATE-estimation.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/PM4/python-dml-401k.ipynb b/PM4/python-dml-401k.ipynb index 441c3c8f..22ca8c0e 100644 --- a/PM4/python-dml-401k.ipynb +++ b/PM4/python-dml-401k.ipynb @@ -1439,7 +1439,7 @@ }, "outputs": [], "source": [ - "!pip install econml==0.15.1" + "!pip install econml==0.16.0" ] }, { diff --git a/T/CATE-estimation.ipynb b/T/CATE-estimation.ipynb index 37ccccaf..619e8e04 100644 --- a/T/CATE-estimation.ipynb +++ b/T/CATE-estimation.ipynb @@ -37,7 +37,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install econml" + "!pip install econml==0.16.0" ] }, { From 6f9887ad6d9cc07df896e7aa5a1633472b8d8bd0 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:27 +0200 Subject: [PATCH 28/32] Update notebooks for current scikit-learn, pgmpy, xgboost and rpy2 Co-Authored-By: Claude Opus 5.5 --- CM3/python-dosearch.ipynb | 3 +-- ...unctional-approximation-by-nn-and-rf.ipynb | 2 +- ...hon-nonlinear-ml-for-wage-prediction.ipynb | 4 ++-- ...ion-analysis-of-401-k-example-w-dags.ipynb | 2 +- PM4/r_dml_inference_for_gun_ownership.irnb | 20 +++++++++---------- 5 files changed, 15 insertions(+), 16 deletions(-) diff --git a/CM3/python-dosearch.ipynb b/CM3/python-dosearch.ipynb index e7f5e59f..0cd1ecc0 100644 --- a/CM3/python-dosearch.ipynb +++ b/CM3/python-dosearch.ipynb @@ -42,8 +42,7 @@ "import numpy as np\n", "import statsmodels.formula.api as smf\n", "import rpy2\n", - "from rpy2.robjects import r, pandas2ri\n", - "pandas2ri.activate()" + "from rpy2.robjects import r, pandas2ri" ] }, { diff --git a/PM3/python-functional-approximation-by-nn-and-rf.ipynb b/PM3/python-functional-approximation-by-nn-and-rf.ipynb index 1188e4dd..5860e5d9 100644 --- a/PM3/python-functional-approximation-by-nn-and-rf.ipynb +++ b/PM3/python-functional-approximation-by-nn-and-rf.ipynb @@ -314,7 +314,7 @@ }, "outputs": [], "source": [ - "nnet = MLPRegressor((200, 20,), 'relu',\n", + "nnet = MLPRegressor(hidden_layer_sizes=(200, 20,), activation='relu',\n", " learning_rate_init=0.01,\n", " batch_size=10, max_iter=1)\n", "nnet" diff --git a/PM3/python-nonlinear-ml-for-wage-prediction.ipynb b/PM3/python-nonlinear-ml-for-wage-prediction.ipynb index 16e5c266..8daf6111 100644 --- a/PM3/python-nonlinear-ml-for-wage-prediction.ipynb +++ b/PM3/python-nonlinear-ml-for-wage-prediction.ipynb @@ -458,7 +458,7 @@ }, "outputs": [], "source": [ - "!git clone https://github.com/maxhuppertz/hdmpy.git\n", + "!pip install hdmpy\n", "!pip install multiprocess" ] }, @@ -475,7 +475,7 @@ "\n", "\n", "# We wrap the package so that it has the familiar sklearn API\n", - "class RLasso(BaseEstimator, RegressorMixin):\n", + "class RLasso(RegressorMixin, BaseEstimator):\n", "\n", " def __init__(self, *, post=True):\n", " self.post = post\n", diff --git a/PM4/python-identification-analysis-of-401-k-example-w-dags.ipynb b/PM4/python-identification-analysis-of-401-k-example-w-dags.ipynb index ef0a5e9f..e4428d25 100644 --- a/PM4/python-identification-analysis-of-401-k-example-w-dags.ipynb +++ b/PM4/python-identification-analysis-of-401-k-example-w-dags.ipynb @@ -50,7 +50,7 @@ "import warnings\n", "warnings.simplefilter('ignore')\n", "import pylab as plt\n", - "from pgmpy.models.BayesianModel import BayesianNetwork" + "from pgmpy.models import DiscreteBayesianNetwork as BayesianNetwork" ] }, { diff --git a/PM4/r_dml_inference_for_gun_ownership.irnb b/PM4/r_dml_inference_for_gun_ownership.irnb index 568a7031..77883b29 100644 --- a/PM4/r_dml_inference_for_gun_ownership.irnb +++ b/PM4/r_dml_inference_for_gun_ownership.irnb @@ -648,13 +648,13 @@ " )\n", "\n", " byk <- xgb.cv(\n", - " data = xgb_train_y,\n", - " nrounds = 1000, verbose = 0, eta = .1, max_depth = 4, nfold = 5\n", + " params = list(eta = .1, max_depth = 4), data = xgb_train_y,\n", + " nrounds = 1000, verbose = 0, nfold = 5\n", " )\n", " best_iter <- which.min(as.matrix(byk$evaluation_log[, 4]))\n", - " byk <- xgboost(\n", - " data = xgb_train_y,\n", - " nrounds = 1000, verbose = 0, eta = .1, max_depth = 4\n", + " byk <- xgb.train(\n", + " params = list(eta = .1, max_depth = 4), data = xgb_train_y,\n", + " nrounds = 1000, verbose = 0\n", " )\n", " yhat_r[indk, 8] <- ktest$logghomr - predict(byk,\n", " newdata = xgb_test_y,\n", @@ -662,13 +662,13 @@ " )\n", "\n", " bdk <- xgb.cv(\n", - " data = xgb_train_d,\n", - " nrounds = 1000, verbose = 0, eta = .1, max_depth = 4, nfold = 5\n", + " params = list(eta = .1, max_depth = 4), data = xgb_train_d,\n", + " nrounds = 1000, verbose = 0, nfold = 5\n", " )\n", " best_iter <- which.min(as.matrix(bdk$evaluation_log[, 4]))\n", - " bdk <- xgboost(\n", - " data = xgb_train_d,\n", - " nrounds = 1000, verbose = 0, eta = .1, max_depth = 4\n", + " bdk <- xgb.train(\n", + " params = list(eta = .1, max_depth = 4), data = xgb_train_d,\n", + " nrounds = 1000, verbose = 0\n", " )\n", " dhat_r[indk, 8] <- ktest$logfssl - predict(bdk,\n", " newdata = xgb_test_d,\n", From 377dce0b0087e9d8b18a43c39677a7139ab234fb Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:27 +0200 Subject: [PATCH 29/32] Install archived rdd package from the CRAN archive Co-Authored-By: Claude Opus 5.5 --- T/T_4_Regression_Discontinuity_on_Progresa_Data.irnb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/T/T_4_Regression_Discontinuity_on_Progresa_Data.irnb b/T/T_4_Regression_Discontinuity_on_Progresa_Data.irnb index ae106ab7..9719d799 100644 --- a/T/T_4_Regression_Discontinuity_on_Progresa_Data.irnb +++ b/T/T_4_Regression_Discontinuity_on_Progresa_Data.irnb @@ -28,7 +28,10 @@ "outputs": [], "source": [ "dependencies <- c(\"rdrobust\", \"fastDummies\", \"randomForest\", \"hdm\", \"gbm\", \"rdd\")\n", - "install.packages(dependencies)" + "install.packages(setdiff(dependencies, \"rdd\"))\n", + "# rdd was archived on CRAN; install its last release from the CRAN archive\n", + "if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\")\n", + "remotes::install_version(\"rdd\", version = \"0.57\")" ] }, { From c3c69531c7d5ded70915fd09228e0b857ac11774 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:27 +0200 Subject: [PATCH 30/32] Fix flake8 errors in AC1 and CM1 Co-Authored-By: Claude Opus 5.5 --- ...tivity-analysis-with-sensmakr-and-debiased-ml.ipynb | 1 + CM1/python-rct-penn-precision-adj.ipynb | 10 ++-------- CM1/python-rct-vaccines.ipynb | 2 ++ 3 files changed, 5 insertions(+), 8 deletions(-) diff --git a/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb b/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb index 87debb25..9e0b7bab 100644 --- a/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb +++ b/AC1/python-sensitivity-analysis-with-sensmakr-and-debiased-ml.ipynb @@ -322,6 +322,7 @@ "sys.path.insert(1, \"./hdmpy\")\n", "import hdmpy\n", "\n", + "\n", "class RLasso(BaseEstimator):\n", "\n", " def __init__(self, *, post=True):\n", diff --git a/CM1/python-rct-penn-precision-adj.ipynb b/CM1/python-rct-penn-precision-adj.ipynb index 3104e353..e694c2f1 100644 --- a/CM1/python-rct-penn-precision-adj.ipynb +++ b/CM1/python-rct-penn-precision-adj.ipynb @@ -459,10 +459,7 @@ " @ ira_results.params.loc[interaction_cols].to_numpy()\n", ")\n", "\n", - "ira_se = np.sqrt(\n", - " ira_results.bse[\"T4\"]**2\n", - " + np.var(cate, ddof=1) / len(X)\n", - ")\n", + "ira_se = np.sqrt(ira_results.bse[\"T4\"]**2 + np.var(cate, ddof=1) / len(X))\n", "\n", "print(f\"ATE: {ira_est:.4f}, (std={ira_se:.4f})\")\n" ] @@ -633,10 +630,7 @@ " @ np.asarray(modely.coef_)[interaction_mask]\n", ")\n", "\n", - "ira_lasso_se = np.sqrt(\n", - " ira_lasso_results.bse[\"T4\"]**2\n", - " + np.var(cate, ddof=1) / len(X)\n", - ")" + "ira_lasso_se = np.sqrt(ira_lasso_results.bse[\"T4\"]**2 + np.var(cate, ddof=1) / len(X))" ] }, { diff --git a/CM1/python-rct-vaccines.ipynb b/CM1/python-rct-vaccines.ipynb index edd4db2a..584b7022 100644 --- a/CM1/python-rct-vaccines.ipynb +++ b/CM1/python-rct-vaccines.ipynb @@ -483,6 +483,7 @@ "source": [ "from scipy.stats.contingency import odds_ratio\n", "\n", + "\n", "def cornfield_ve_ci(cases_v, n_v, cases_u, n_u):\n", " table = [\n", " [cases_v, n_v - cases_v],\n", @@ -496,6 +497,7 @@ " # Approximate VE interval: odds ratio approximates risk ratio\n", " return 1 - ci_or.high, 1 - ci_or.low\n", "\n", + "\n", "# Overall\n", "print(cornfield_ve_ci(9, 19965, 169, 20172))\n", "\n", From d68258798275a40bf7895bf410cff76754e4c3d5 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:27 +0200 Subject: [PATCH 31/32] Style T-ADML R notebooks to pass lintr Co-Authored-By: Claude Opus 5.5 --- T-ADML/r-ADML-401k.irnb | 218 ++++++++++------- T-ADML/r-ADML-gas.irnb | 522 +++++++++++++++++++++++----------------- 2 files changed, 431 insertions(+), 309 deletions(-) diff --git a/T-ADML/r-ADML-401k.irnb b/T-ADML/r-ADML-401k.irnb index 3303bfbf..676d068e 100644 --- a/T-ADML/r-ADML-401k.irnb +++ b/T-ADML/r-ADML-401k.irnb @@ -33,10 +33,10 @@ "# Install packages if needed\n", "repos <- c(CRAN = \"https://cloud.r-project.org\")\n", "\n", - "if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\", repos = repos)\n", + "if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\", repos = repos)\n", "if (!requireNamespace(\"sensemakr\", quietly = TRUE)) install.packages(\"sensemakr\", repos = repos)\n", - "if (!requireNamespace(\"ggplot2\", quietly = TRUE)) install.packages(\"ggplot2\", repos = repos)\n", - "if (!requireNamespace(\"ranger\", quietly = TRUE)) install.packages(\"ranger\", repos = repos)\n", + "if (!requireNamespace(\"ggplot2\", quietly = TRUE)) install.packages(\"ggplot2\", repos = repos)\n", + "if (!requireNamespace(\"ranger\", quietly = TRUE)) install.packages(\"ranger\", repos = repos)\n", "if (!requireNamespace(\"dml.sensemakr\", quietly = TRUE)) {\n", " remotes::install_github(\"carloscinelli/dml.sensemakr\", upgrade = \"never\")\n", "}" @@ -69,12 +69,12 @@ ], "source": [ "suppressMessages({\n", - " library(sensemakr) # attached first: we only need add_bound_to_contour()\n", - " library(dml.sensemakr) # attached last: keeps its methods for class \"dml\" unmasked\n", + " library(sensemakr) # attached first: we only need add_bound_to_contour()\n", + " library(dml.sensemakr) # attached last: keeps its methods for class \"dml\" unmasked\n", " library(ggplot2)\n", "})\n", "\n", - "options(scipen = 99) \n", + "options(scipen = 99)\n", "\n", "# figure sizing inside the notebook\n", "options(repr.plot.width = 7, repr.plot.height = 6, repr.plot.res = 150)\n", @@ -125,15 +125,18 @@ "source": [ "data(\"pension\")\n", "\n", - "y <- pension$net_tfa # Y: net total financial assets\n", - "d <- pension$e401 # D: 401(k) eligibility\n", + "y <- pension$net_tfa # Y: net total financial assets\n", + "d <- pension$e401 # D: 401(k) eligibility\n", "\n", "# X^o: the observed worker characteristics\n", "x <- model.matrix(~ -1 + age + inc + educ + fsize + marr + twoearn + pira + hown,\n", - " data = pension)\n", + " data = pension\n", + ")\n", "\n", - "cat(\"households:\", nrow(x), \" covariates:\", ncol(x),\n", - " \" eligible:\", sprintf(\"%.1f%%\", 100 * mean(d)), \"\\n\")" + "cat(\n", + " \"households:\", nrow(x), \" covariates:\", ncol(x),\n", + " \" eligible:\", sprintf(\"%.1f%%\", 100 * mean(d)), \"\\n\"\n", + ")" ] }, { @@ -198,9 +201,9 @@ ], "source": [ "set.seed(1)\n", - "dml.401k.plm <- dml(y, d, x, model = \"plm\", cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", + "dml_401k_plm <- dml(y, d, x, model = \"plm\", cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", "\n", - "summary(dml.401k.plm)" + "summary(dml_401k_plm)" ] }, { @@ -237,8 +240,9 @@ "source": [ "# income quartiles\n", "g1 <- cut(x[, \"inc\"],\n", - " quantile(x[, \"inc\"], c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE),\n", - " labels = c(\"q1\", \"q2\", \"q3\", \"q4\"), include.lowest = TRUE)" + " quantile(x[, \"inc\"], c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE),\n", + " labels = c(\"q1\", \"q2\", \"q3\", \"q4\"), include.lowest = TRUE\n", + ")" ] }, { @@ -298,10 +302,12 @@ ], "source": [ "set.seed(1)\n", - "dml.401k.npm <- dml(y, d, x, groups = g1, model = \"npm\",\n", - " cf.folds = 5, cf.reps = 5, verbose = FALSE)\n", + "dml_401k_npm <- dml(y, d, x,\n", + " groups = g1, model = \"npm\",\n", + " cf.folds = 5, cf.reps = 5, verbose = FALSE\n", + ")\n", "\n", - "summary(dml.401k.npm)" + "summary(dml_401k_npm)" ] }, { @@ -332,11 +338,11 @@ }, "outputs": [], "source": [ - "group.names <- setdiff(names(coef(dml.401k.npm)), \"ate\")\n", + "group_names <- setdiff(names(coef(dml_401k_npm)), \"ate\")\n", "\n", - "df <- data.frame(groups = 1:4, estimate = coef(dml.401k.npm)[group.names])\n", - "cis <- setNames(as.data.frame(confint(dml.401k.npm)[group.names, ]), c(\"lwr.ci\", \"upr.ci\"))\n", - "df <- cbind(df, cis)" + "df <- data.frame(groups = 1:4, estimate = coef(dml_401k_npm)[group_names])\n", + "cis <- setNames(as.data.frame(confint(dml_401k_npm)[group_names, ]), c(\"lwr.ci\", \"upr.ci\"))\n", + "df <- cbind(df, cis)" ] }, { @@ -411,11 +417,13 @@ } ], "source": [ - "rv.plm <- robustness_value(dml.401k.plm)\n", - "rv.npm <- robustness_value(dml.401k.npm)\n", + "rv_plm <- robustness_value(dml_401k_plm)\n", + "rv_npm <- robustness_value(dml_401k_npm)\n", "\n", - "cat(sprintf(\"RV_alpha (alpha = 0.05, threshold theta = 0)\\n partially linear : %.1f%%\\n nonparametric : %.1f%%\\n\",\n", - " 100 * rv.plm[\"ate\"], 100 * rv.npm[\"ate\"]))\n" + "cat(sprintf(\n", + " \"RV_alpha (alpha = 0.05, threshold theta = 0)\\n partially linear : %.1f%%\\n nonparametric : %.1f%%\\n\",\n", + " 100 * rv_plm[\"ate\"], 100 * rv_npm[\"ate\"]\n", + "))" ] }, { @@ -453,11 +461,11 @@ }, "outputs": [], "source": [ - "cf.y <- 0.04 # C_Y^2\n", - "cf.d <- 0.03 # q_alpha\n", - "rho2 <- 1 # |rho| = 1\n", + "cf_y <- 0.04 # C_Y squared\n", + "cf_d <- 0.03 # q_alpha\n", + "rho2 <- 1 # |rho| = 1\n", "\n", - "bounds.plm <- dml_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + "bounds_plm <- dml_bounds(dml_401k_plm, cf.y = cf_y, cf.d = cf_d, rho2 = rho2)" ] }, { @@ -477,7 +485,7 @@ }, "outputs": [], "source": [ - "bounds.npm <- dml_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + "bounds_npm <- dml_bounds(dml_401k_npm, cf.y = cf_y, cf.d = cf_d, rho2 = rho2)" ] }, { @@ -497,8 +505,8 @@ }, "outputs": [], "source": [ - "cb.plm <- confidence_bounds(dml.401k.plm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)\n", - "cb.npm <- confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, rho2 = rho2)" + "cb_plm <- confidence_bounds(dml_401k_plm, cf.y = cf_y, cf.d = cf_d, rho2 = rho2)\n", + "cb_npm <- confidence_bounds(dml_401k_npm, cf.y = cf_y, cf.d = cf_d, rho2 = rho2)" ] }, { @@ -568,20 +576,22 @@ "row <- function(bounds, cb, label) {\n", " b <- coef(bounds)[, \"ate\"]\n", " data.frame(\n", - " Model = label,\n", - " `Short ATE` = dollars(b[\"theta.s\"]),\n", - " `Bias bound` = dollars(b[\"bias.bound\"]),\n", - " `ATE bounds` = sprintf(\"[%s, %s]\", dollars(b[\"theta.m\"]), dollars(b[\"theta.p\"])),\n", + " Model = label,\n", + " `Short ATE` = dollars(b[\"theta.s\"]),\n", + " `Bias bound` = dollars(b[\"bias.bound\"]),\n", + " `ATE bounds` = sprintf(\"[%s, %s]\", dollars(b[\"theta.m\"]), dollars(b[\"theta.p\"])),\n", " `95% conf. bounds` = sprintf(\"[%s, %s]\", dollars(cb[\"ate\", 1]), dollars(cb[\"ate\", 2])),\n", " row.names = NULL,\n", " check.names = FALSE\n", " )\n", "}\n", "\n", - "table2.3 <- rbind(row(bounds.plm, cb.plm, \"Partially linear\"),\n", - " row(bounds.npm, cb.npm, \"Nonparametric\"))\n", + "table2_3 <- rbind(\n", + " row(bounds_plm, cb_plm, \"Partially linear\"),\n", + " row(bounds_npm, cb_npm, \"Nonparametric\")\n", + ")\n", "\n", - "table2.3\n" + "table2_3" ] }, { @@ -637,16 +647,20 @@ } ], "source": [ - "bds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0)),\n", - " c(\"lwr.bound\", \"upr.bound\"))\n", - "cbds <- setNames(as.data.frame(confidence_bounds(dml.401k.npm, cf.y = cf.y, cf.d = cf.d, level = 0.95)),\n", - " c(\"lwr.cbound\", \"upr.cbound\"))\n", + "bds <- setNames(\n", + " as.data.frame(confidence_bounds(dml_401k_npm, cf.y = cf_y, cf.d = cf_d, level = 0)),\n", + " c(\"lwr.bound\", \"upr.bound\")\n", + ")\n", + "cbds <- setNames(\n", + " as.data.frame(confidence_bounds(dml_401k_npm, cf.y = cf_y, cf.d = cf_d, level = 0.95)),\n", + " c(\"lwr.cbound\", \"upr.cbound\")\n", + ")\n", "\n", "df2 <- cbind(df, bds[-1, ], cbds[-1, ])\n", "\n", "p2 <- ggplot(df2, aes(x = groups, y = estimate)) +\n", " geom_line() +\n", - " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), alpha = 0.1, col = \"red\", fill = \"red\") +\n", + " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), alpha = 0.1, col = \"red\", fill = \"red\") +\n", " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), alpha = 0.1, col = \"blue\", fill = \"blue\") +\n", " theme_bw() +\n", " xlab(\"Income Groups by Quartiles\") +\n", @@ -694,10 +708,10 @@ "source": [ "p_combined <- ggplot(df2, aes(x = groups, y = estimate)) +\n", " geom_ribbon(aes(ymin = lwr.cbound, ymax = upr.cbound), fill = \"blue\", alpha = 0.03, color = NA) +\n", - " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), fill = \"blue\", alpha = 0.12, color = NA) +\n", + " geom_ribbon(aes(ymin = lwr.bound, ymax = upr.bound), fill = \"blue\", alpha = 0.12, color = NA) +\n", " geom_line(color = \"black\") +\n", - " geom_line(aes(y = lwr.ci), color = \"black\", linetype = \"dashed\") +\n", - " geom_line(aes(y = upr.ci), color = \"black\", linetype = \"dashed\") +\n", + " geom_line(aes(y = lwr.ci), color = \"black\", linetype = \"dashed\") +\n", + " geom_line(aes(y = upr.ci), color = \"black\", linetype = \"dashed\") +\n", " geom_line(aes(y = lwr.bound), color = \"blue\") +\n", " geom_line(aes(y = upr.bound), color = \"blue\") +\n", " geom_line(aes(y = lwr.cbound), color = \"blue\", linetype = \"dashed\") +\n", @@ -709,7 +723,7 @@ " ggtitle(\"Confidence and Sensitivity Bounds\")\n", "\n", "ggsave(\"figures/401k-groups-sensitivity-combined.pdf\", p_combined, width = 4.5, height = 4.5)\n", - "p_combined\n" + "p_combined" ] }, { @@ -741,9 +755,10 @@ "source": [ "set.seed(2)\n", "invisible(capture.output(\n", - " bench.plm <- dml_benchmark(dml.401k.plm,\n", - " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", - "))\n" + " bench_plm <- dml_benchmark(dml_401k_plm,\n", + " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\")\n", + " )\n", + "))" ] }, { @@ -820,7 +835,7 @@ } ], "source": [ - "out <- summary(bench.plm)$benchmarks\n", + "out <- summary(bench_plm)$benchmarks\n", "round(out[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" ] }, @@ -843,10 +858,10 @@ }, "outputs": [], "source": [ - "ovb_contour_without_origin_label <-\n", + "ovb_contour_no_origin_label <-\n", " getS3method(\"ovb_contour_plot\", \"dml\")\n", "\n", - "plot_body <- as.list(body(ovb_contour_without_origin_label))\n", + "plot_body <- as.list(body(ovb_contour_no_origin_label))\n", "\n", "is_origin_label <- vapply(\n", " plot_body,\n", @@ -863,7 +878,7 @@ "\n", "plot_body[is_origin_label] <- list(quote(invisible(NULL)))\n", "\n", - "body(ovb_contour_without_origin_label) <- as.call(plot_body)" + "body(ovb_contour_no_origin_label) <- as.call(plot_body)" ] }, { @@ -883,18 +898,22 @@ }, "outputs": [], "source": [ - "bumps.plm <- list(pira = c(0.010, 0.009),\n", - " twoearn = c(0.020, -0.002),\n", - " inc = c(0.010, 0.009),\n", - " inc4 = c(0.015, -0.005))\n", + "bumps_plm <- list(\n", + " pira = c(0.010, 0.009),\n", + " twoearn = c(0.020, -0.002),\n", + " inc = c(0.010, 0.009),\n", + " inc4 = c(0.015, -0.005)\n", + ")\n", "\n", - "bumps.npm <- list(pira = c(0.005, 0.009),\n", - " twoearn = c(0.020, 0.005),\n", - " inc = c(-0.010, 0.009),\n", - " inc4 = c(0.015, 0.000))\n", + "bumps_npm <- list(\n", + " pira = c(0.005, 0.009),\n", + " twoearn = c(0.020, 0.005),\n", + " inc = c(-0.010, 0.009),\n", + " inc4 = c(0.015, 0.000)\n", + ")\n", "\n", - "contour_panel <- function(fit, bench, rho2, title, bumps = bumps.plm) {\n", - " ovb_contour_without_origin_label(\n", + "contour_panel <- function(fit, bench, rho2, title, bumps = bumps_plm) {\n", + " ovb_contour_no_origin_label(\n", " fit,\n", " which.bound = \"lwr\",\n", " level = 0.95,\n", @@ -908,33 +927,43 @@ "\n", " title(main = title)\n", "\n", - " add_bound <- function(cfd, cfy, label, bump.x, bump.y, ...) {\n", + " add_bound <- function(cfd, cfy, label, bump_x, bump_y, ...) {\n", " sensemakr::add_bound_to_contour(\n", " r2dz.x = cfd, r2yz.dx = cfy,\n", " bound_label = label,\n", " bound_value = confidence_bounds(\n", - " fit, cf.y = cfy, cf.d = cfd, rho2 = rho2,\n", + " fit,\n", + " cf.y = cfy, cf.d = cfd, rho2 = rho2,\n", " level = 0.95, combine.method = \"median\"\n", " )[\"ate\", \"lwr\"],\n", - " label.bump.x = bump.x, label.bump.y = bump.y, ...)\n", + " label.bump.x = bump_x, label.bump.y = bump_y, ...\n", + " )\n", " }\n", "\n", " add_bound(0.03, 0.04, \"Max Match\", 0.015, 0.003, point.pch = 25, point.bg = \"red\")\n", "\n", " # one-times each observed benchmark\n", - " add_bound(bench[\"pira\", \"gain.D\"], bench[\"pira\", \"gain.Y\"], \"1 x Part. in IRA\",\n", - " bumps$pira[1], bumps$pira[2])\n", - " add_bound(bench[\"twoearn\", \"gain.D\"], bench[\"twoearn\", \"gain.Y\"], \"1 x Two Earners\",\n", - " bumps$twoearn[1], bumps$twoearn[2])\n", - " add_bound(bench[\"inc\", \"gain.D\"], bench[\"inc\", \"gain.Y\"], \"1 x Income\",\n", - " bumps$inc[1], bumps$inc[2])\n", + " add_bound(\n", + " bench[\"pira\", \"gain.D\"], bench[\"pira\", \"gain.Y\"], \"1 x Part. in IRA\",\n", + " bumps$pira[1], bumps$pira[2]\n", + " )\n", + " add_bound(\n", + " bench[\"twoearn\", \"gain.D\"], bench[\"twoearn\", \"gain.Y\"], \"1 x Two Earners\",\n", + " bumps$twoearn[1], bumps$twoearn[2]\n", + " )\n", + " add_bound(\n", + " bench[\"inc\", \"gain.D\"], bench[\"inc\", \"gain.Y\"], \"1 x Income\",\n", + " bumps$inc[1], bumps$inc[2]\n", + " )\n", "\n", " # a quarter as strong as income\n", - " add_bound(bench[\"inc\", \"gain.D\"] * 0.25, bench[\"inc\", \"gain.Y\"] * 0.25, \"1/4 x Income\",\n", - " bumps$inc4[1], bumps$inc4[2])\n", + " add_bound(\n", + " bench[\"inc\", \"gain.D\"] * 0.25, bench[\"inc\", \"gain.Y\"] * 0.25, \"1/4 x Income\",\n", + " bumps$inc4[1], bumps$inc4[2]\n", + " )\n", "\n", " # unadjusted\n", - " add_bound(0, 0, \"Unadjusted\", bump.x = 0.015, bump.y = 0.003, point.pch = NA) \n", + " add_bound(0, 0, \"Unadjusted\", bump_x = 0.015, bump_y = 0.003, point.pch = NA)\n", "\n", " invisible(NULL)\n", "}" @@ -984,10 +1013,10 @@ "options(repr.plot.width = 7, repr.plot.height = 7)\n", "\n", "pdf(\"figures/401K-contour-plm-lower.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1\")))\n", + "contour_panel(dml_401k_plm, out, rho2 = 1, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1\")))\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.plm, out, rho2 = 1, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1\")))" + "contour_panel(dml_401k_plm, out, rho2 = 1, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1\")))" ] }, { @@ -1023,13 +1052,15 @@ } ], "source": [ - "rho2.half <- (1/2)^2\n", + "rho2_half <- (1 / 2)^2\n", "\n", "pdf(\"figures/401K-contour-plm-lower-rho2.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")))\n", + "contour_panel(dml_401k_plm, out, rho2 = rho2_half,\n", + " title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")))\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.plm, out, rho2 = rho2.half, title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")))" + "contour_panel(dml_401k_plm, out, rho2 = rho2_half,\n", + " title = expression(paste(\"Partially Linear Model, |\", rho, \"| = 1/2\")))" ] }, { @@ -1118,12 +1149,13 @@ "source": [ "set.seed(2)\n", "invisible(capture.output(\n", - " bench.npm <- dml_benchmark(dml.401k.npm,\n", - " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\"))\n", + " bench_npm <- dml_benchmark(dml_401k_npm,\n", + " benchmark_covariates = c(\"inc\", \"pira\", \"twoearn\")\n", + " )\n", "))\n", "\n", - "out.npm <- summary(bench.npm)$benchmarks\n", - "round(out.npm[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" + "out_npm <- summary(bench_npm)$benchmarks\n", + "round(out_npm[, c(\"gain.Y\", \"gain.D\", \"rho\")], 4)" ] }, { @@ -1160,10 +1192,12 @@ ], "source": [ "pdf(\"figures/401K-contour-npm-lower.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps.npm)\n", + "contour_panel(dml_401k_npm, out_npm, rho2 = 1,\n", + " title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps_npm)\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = 1, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps.npm)" + "contour_panel(dml_401k_npm, out_npm, rho2 = 1,\n", + " title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1\")), bumps = bumps_npm)" ] }, { @@ -1200,10 +1234,12 @@ ], "source": [ "pdf(\"figures/401K-contour-npm-lower-rho2.pdf\", width = 6, height = 6)\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps.npm)\n", + "contour_panel(dml_401k_npm, out_npm, rho2 = rho2_half,\n", + " title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps_npm)\n", "invisible(dev.off())\n", "\n", - "contour_panel(dml.401k.npm, out.npm, rho2 = rho2.half, title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps.npm)" + "contour_panel(dml_401k_npm, out_npm, rho2 = rho2_half,\n", + " title = expression(paste(\"Nonparametric Model, |\", rho, \"| = 1/2\")), bumps = bumps_npm)" ] }, { diff --git a/T-ADML/r-ADML-gas.irnb b/T-ADML/r-ADML-gas.irnb index 3a14290c..2ddb628b 100644 --- a/T-ADML/r-ADML-gas.irnb +++ b/T-ADML/r-ADML-gas.irnb @@ -27,10 +27,11 @@ "outputs": [], "source": [ "repos <- c(CRAN = \"https://cloud.r-project.org\")\n", - "pkgs <- c(\"ranger\", \"glmnet\", \"ggplot2\", \"Matrix\")\n", + "pkgs <- c(\"ranger\", \"glmnet\", \"ggplot2\", \"Matrix\")\n", "for (p in pkgs) {\n", - " if (!requireNamespace(p, quietly = TRUE))\n", + " if (!requireNamespace(p, quietly = TRUE)) {\n", " install.packages(p, repos = repos, quiet = TRUE)\n", + " }\n", "}" ] }, @@ -106,7 +107,7 @@ "outputs": [], "source": [ "get_data_r <- function(q = 5L, synthetic = FALSE, region = TRUE,\n", - " random_seed = 123L) {\n", + " random_seed = 123L) {\n", " data_url <- paste0(\n", " \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/\",\n", " \"update-notebooks/data/data_BHP2_with_region.csv\"\n", @@ -118,9 +119,9 @@ " if (!(q %in% c(4L, 5L))) {\n", " df <- df[df$log_y >= log(42500) - 0.5 & df$log_y <= log(72500) + 0.5, ]\n", " }\n", - " if (q == 1L) df <- df[df$log_y >= log(42500)-.5 & df$log_y <= log(42500)+.5, ]\n", - " if (q == 2L) df <- df[df$log_y >= log(57500)-.5 & df$log_y <= log(57500)+.5, ]\n", - " if (q == 3L) df <- df[df$log_y >= log(72500)-.5 & df$log_y <= log(72500)+.5, ]\n", + " if (q == 1L) df <- df[df$log_y >= log(42500) - .5 & df$log_y <= log(42500) + .5, ]\n", + " if (q == 2L) df <- df[df$log_y >= log(57500) - .5 & df$log_y <= log(57500) + .5, ]\n", + " if (q == 3L) df <- df[df$log_y >= log(72500) - .5 & df$log_y <= log(72500) + .5, ]\n", " if (q == 4L) df <- df[df$log_y >= 11.6, ]\n", "\n", " # Drop non-feature columns\n", @@ -128,7 +129,7 @@ " Xdf <- df[, !names(df) %in% drop_cols, drop = FALSE]\n", "\n", " if (region) {\n", - " lvls <- sort(unique(df$region))[-1L] # drop-first encoding\n", + " lvls <- sort(unique(df$region))[-1L] # drop-first encoding\n", " for (lv in lvls) {\n", " Xdf[[paste0(\"region_\", lv)]] <- as.numeric(df$region == lv)\n", " }\n", @@ -140,12 +141,12 @@ "\n", " # 5-fold state-grouped splits\n", " set.seed(random_seed)\n", - " states <- df$state_fips\n", - " state_ids <- sample(unique(states))\n", - " k <- 5L\n", + " states <- df$state_fips\n", + " state_ids <- sample(unique(states))\n", + " k <- 5L\n", " fold_label <- rep_len(seq_len(k), length(state_ids))\n", " state_fold <- setNames(fold_label, state_ids)\n", - " obs_fold <- state_fold[as.character(states)]\n", + " obs_fold <- state_fold[as.character(states)]\n", " splits <- lapply(seq_len(k), function(i) {\n", " list(train = which(obs_fold != i), test = which(obs_fold == i))\n", " })\n", @@ -166,10 +167,10 @@ "outputs": [], "source": [ "# Global parameters\n", - "q_grid <- 1:5\n", + "q_grid <- 1:5\n", "synthetic <- FALSE\n", - "region <- TRUE\n", - "verbose <- 0L" + "region <- TRUE\n", + "verbose <- 0L" ] }, { @@ -196,15 +197,15 @@ "# Polynomial-treatment x covariate feature matrix (cross product)\n", "# Mirrors Python's Featurizer._transform: cross_product(poly(T), X[:,1:])\n", "poly_cross_feats <- function(X, degree) {\n", - " n <- nrow(X)\n", + " n <- nrow(X)\n", " p_x <- ncol(X) - 1L\n", - " T_poly <- outer(X[, 1L], seq(0L, degree), `^`) # n x (degree+1)\n", - " feats <- matrix(0, n, (degree + 1L) * p_x)\n", + " t_poly <- outer(X[, 1L], seq(0L, degree), `^`) # n x (degree+1)\n", + " feats <- matrix(0, n, (degree + 1L) * p_x)\n", " for (j in seq_len(degree + 1L)) {\n", " idx <- ((j - 1L) * p_x + 1L):(j * p_x)\n", - " feats[, idx] <- T_poly[, j] * X[, -1L, drop = FALSE]\n", + " feats[, idx] <- t_poly[, j] * X[, -1L, drop = FALSE]\n", " }\n", - " cbind(1.0, feats) # include intercept column\n", + " cbind(1.0, feats) # include intercept column\n", "}" ] }, @@ -221,23 +222,26 @@ "source": [ "# Linear sieve Riesz representer with L2 penalty.\n", "# Returns a prediction function function(X_new) -> numeric(nrow(X_new))\n", - "fit_sieve_rr <- function(X_train, moment_fn, alpha = 0, degree = 1L) {\n", - " Xf <- poly_cross_feats(X_train, degree)\n", - " n <- nrow(Xf)\n", - " p <- ncol(Xf)\n", + "fit_sieve_rr <- function(x_train, moment_fn, alpha = 0, degree = 1L) {\n", + " Xf <- poly_cross_feats(x_train, degree)\n", + " n <- nrow(Xf)\n", + " p <- ncol(Xf)\n", "\n", " # Moment features: M[i,j] = moment_fn(X, phi_j) where phi_j(x) = Xf[i,j]\n", " Mf <- matrix(0, n, p)\n", " for (j in seq_len(p)) {\n", " local({\n", " jj <- j\n", - " Mf[, jj] <<- moment_fn(X_train,\n", - " function(Xnew) poly_cross_feats(Xnew, degree)[, jj])\n", + " Mf[, jj] <<- moment_fn(\n", + " x_train,\n", + " function(Xnew) poly_cross_feats(Xnew, degree)[, jj]\n", + " )\n", " })\n", " }\n", "\n", - " Sig <- crossprod(Xf) / n\n", - " reg <- alpha * diag(p); reg[1L, 1L] <- 0\n", + " Sig <- crossprod(Xf) / n\n", + " reg <- alpha * diag(p)\n", + " reg[1L, 1L] <- 0\n", " params <- solve(Sig + reg + 1e-10 * diag(p), colMeans(Mf))\n", "\n", " function(Xnew) drop(poly_cross_feats(Xnew, degree) %*% params)\n", @@ -257,35 +261,38 @@ "source": [ "# Elastic-net sieve Riesz representer via L-BFGS-B on the doubled variable.\n", "# Mirrors Python's SparseSieveRR using scipy fmin_l_bfgs_b.\n", - "fit_sparse_sieve_rr <- function(X_train, moment_fn,\n", - " alpha = 0.01, l1_ratio = 1.0,\n", - " degree = 1L, tol = 1e-4) {\n", - " Xf <- poly_cross_feats(X_train, degree)\n", - " n <- nrow(Xf); p <- ncol(Xf)\n", + "fit_sparse_sieve_rr <- function(x_train, moment_fn,\n", + " alpha = 0.01, l1_ratio = 1.0,\n", + " degree = 1L, tol = 1e-4) {\n", + " Xf <- poly_cross_feats(x_train, degree)\n", + " n <- nrow(Xf)\n", + " p <- ncol(Xf)\n", "\n", " Mf <- matrix(0, n, p)\n", " for (j in seq_len(p)) {\n", " local({\n", " jj <- j\n", - " Mf[, jj] <<- moment_fn(X_train,\n", - " function(Xnew) poly_cross_feats(Xnew, degree)[, jj])\n", + " Mf[, jj] <<- moment_fn(\n", + " x_train,\n", + " function(Xnew) poly_cross_feats(Xnew, degree)[, jj]\n", + " )\n", " })\n", " }\n", "\n", - " Sig <- crossprod(Xf) / n\n", + " Sig <- crossprod(Xf) / n\n", " mfeats <- colMeans(Mf)\n", - " a_l1 <- alpha * l1_ratio\n", - " a_l2 <- alpha * (1 - l1_ratio)\n", + " a_l1 <- alpha * l1_ratio\n", + " a_l2 <- alpha * (1 - l1_ratio)\n", "\n", " loss_gr <- function(ec) {\n", " coef <- ec[1:p] - ec[(p + 1):(2 * p)]\n", " loss <- -2 * sum(mfeats * coef) + as.numeric(t(coef) %*% Sig %*% coef)\n", " loss <- loss + a_l1 * sum(ec[2:p]) + a_l1 * sum(ec[(p + 2):(2 * p)])\n", - " loss <- loss + 0.5*a_l2*sum(ec[2:p]^2) + 0.5*a_l2*sum(ec[(p+2):(2*p)]^2)\n", - " gr <- 2 * as.numeric(Sig %*% coef - mfeats)\n", + " loss <- loss + 0.5 * a_l2 * sum(ec[2:p]^2) + 0.5 * a_l2 * sum(ec[(p + 2):(2 * p)]^2)\n", + " gr <- 2 * as.numeric(Sig %*% coef - mfeats)\n", " grad <- c(gr, -gr)\n", - " grad[2:p] <- grad[2:p] + a_l1 + a_l2 * ec[2:p]\n", - " grad[(p+2):(2*p)] <- grad[(p+2):(2*p)] + a_l1 + a_l2 * ec[(p+2):(2*p)]\n", + " grad[2:p] <- grad[2:p] + a_l1 + a_l2 * ec[2:p]\n", + " grad[(p + 2):(2 * p)] <- grad[(p + 2):(2 * p)] + a_l1 + a_l2 * ec[(p + 2):(2 * p)]\n", " list(value = loss, gradient = grad)\n", " }\n", "\n", @@ -293,8 +300,8 @@ " rep(0, 2 * p),\n", " fn = function(ec) loss_gr(ec)$value,\n", " gr = function(ec) loss_gr(ec)$gradient,\n", - " method = \"L-BFGS-B\",\n", - " lower = rep(0, 2 * p),\n", + " method = \"L-BFGS-B\",\n", + " lower = rep(0, 2 * p),\n", " control = list(factr = tol / .Machine$double.eps, maxit = 1000L)\n", " )\n", " params <- res$par[1:p] - res$par[(p + 1):(2 * p)]\n", @@ -314,8 +321,9 @@ "outputs": [], "source": [ "# Riesz score on held-out data: E[2*m(X;g) - g(X)^2] (higher is better)\n", - "riesz_score_r <- function(rr_fn, X_val, moment_fn)\n", - " mean(2 * moment_fn(X_val, rr_fn) - rr_fn(X_val)^2)\n", + "riesz_score_r <- function(rr_fn, x_val, moment_fn) {\n", + " mean(2 * moment_fn(x_val, rr_fn) - rr_fn(x_val)^2)\n", + "}\n", "\n", "# Select best Riesz candidate via CV, return a factory function(X_train) -> rr_fn\n", "rr_fn_gen <- function(X, moment_fn, splits, ...) {\n", @@ -329,29 +337,40 @@ " scores <- vapply(splits, function(s) {\n", " fit_fn <- if (sparse) fit_sparse_sieve_rr else fit_sieve_rr\n", " m <- tryCatch(\n", - " fit_fn(X[s$train, , drop=FALSE], moment_fn,\n", - " alpha=alpha, degree=degree),\n", - " error = function(e) NULL)\n", - " if (is.null(m)) return(-Inf)\n", - " riesz_score_r(m, X[s$test, , drop=FALSE], moment_fn)\n", + " fit_fn(X[s$train, , drop = FALSE], moment_fn,\n", + " alpha = alpha, degree = degree\n", + " ),\n", + " error = function(e) NULL\n", + " )\n", + " if (is.null(m)) {\n", + " return(-Inf)\n", + " }\n", + " riesz_score_r(m, X[s$test, , drop = FALSE], moment_fn)\n", " }, numeric(1L))\n", " sc <- mean(scores[is.finite(scores)])\n", " if (is.finite(sc) && sc > best_score) {\n", " best_score <- sc\n", " best_factory <- local({\n", - " d <- degree; a <- alpha; sp <- sparse\n", - " function(X_tr) {\n", - " if (sp) fit_sparse_sieve_rr(X_tr, moment_fn, alpha=a, degree=d)\n", - " else fit_sieve_rr(X_tr, moment_fn, alpha=a, degree=d)\n", + " d <- degree\n", + " a <- alpha\n", + " sp <- sparse\n", + " function(x_tr) {\n", + " if (sp) {\n", + " fit_sparse_sieve_rr(x_tr, moment_fn, alpha = a, degree = d)\n", + " } else {\n", + " fit_sieve_rr(x_tr, moment_fn, alpha = a, degree = d)\n", + " }\n", " }\n", " })\n", " }\n", " }\n", " }\n", " }\n", - " if (is.null(best_factory))\n", - " best_factory <- function(X_tr)\n", - " fit_sieve_rr(X_tr, moment_fn, alpha=1e-2, degree=1L)\n", + " if (is.null(best_factory)) {\n", + " best_factory <- function(x_tr) {\n", + " fit_sieve_rr(x_tr, moment_fn, alpha = 1e-2, degree = 1L)\n", + " }\n", + " }\n", " best_factory\n", "}\n", "\n", @@ -359,36 +378,42 @@ "reg_fn_gen <- function(X, y, splits, ...) {\n", " # Scale matrix, clamping zero-SD columns to 1 to avoid NaN\n", " safe_scale <- function(X, ctr = colMeans(X),\n", - " scl = { s <- apply(X, 2, sd); s[s < .Machine$double.eps] <- 1; s }) {\n", + " scl = {\n", + " s <- apply(X, 2, sd)\n", + " s[s < .Machine$double.eps] <- 1\n", + " s\n", + " }) {\n", " scale(X, center = ctr, scale = scl)\n", " }\n", "\n", " # Evaluate ranger CV MSE\n", " rf_preds <- numeric(nrow(X))\n", " for (s in splits) {\n", - " df_tr <- as.data.frame(cbind(y_tr=y[s$train], X[s$train,,drop=FALSE]))\n", - " rf <- ranger(y_tr~., data=df_tr, num.trees=200L, verbose=FALSE)\n", - " rf_preds[s$test] <- predict(rf, data=as.data.frame(X[s$test,,drop=FALSE]))$predictions\n", + " df_tr <- as.data.frame(cbind(y_tr = y[s$train], X[s$train, , drop = FALSE]))\n", + " rf <- ranger(y_tr ~ ., data = df_tr, num.trees = 200L, verbose = FALSE)\n", + " rf_preds[s$test] <- predict(rf, data = as.data.frame(X[s$test, , drop = FALSE]))$predictions\n", " }\n", " rf_mse <- mean((y - rf_preds)^2)\n", "\n", " # Evaluate lasso CV MSE; fall back to ranger if glmnet errors\n", - " lasso_fit <- tryCatch(cv.glmnet(safe_scale(X), y, alpha=1, nfolds=5L),\n", - " error = function(e) NULL)\n", + " lasso_fit <- tryCatch(cv.glmnet(safe_scale(X), y, alpha = 1, nfolds = 5L),\n", + " error = function(e) NULL\n", + " )\n", " lasso_mse <- if (!is.null(lasso_fit)) min(lasso_fit$cvm) else Inf\n", "\n", " if (lasso_mse < rf_mse) {\n", - " function(X_tr, y_tr) {\n", - " ctr <- colMeans(X_tr)\n", - " scl <- apply(X_tr, 2, sd); scl[scl < .Machine$double.eps] <- 1\n", - " lf <- cv.glmnet(scale(X_tr, center=ctr, scale=scl), y_tr, alpha=1, nfolds=5L)\n", - " function(Xnew) as.numeric(predict(lf, newx=scale(Xnew, center=ctr, scale=scl), s=\"lambda.min\"))\n", + " function(x_tr, y_tr) {\n", + " ctr <- colMeans(x_tr)\n", + " scl <- apply(x_tr, 2, sd)\n", + " scl[scl < .Machine$double.eps] <- 1\n", + " lf <- cv.glmnet(scale(x_tr, center = ctr, scale = scl), y_tr, alpha = 1, nfolds = 5L)\n", + " function(Xnew) as.numeric(predict(lf, newx = scale(Xnew, center = ctr, scale = scl), s = \"lambda.min\"))\n", " }\n", " } else {\n", - " function(X_tr, y_tr) {\n", - " df_tr <- as.data.frame(cbind(y_tr=y_tr, X_tr))\n", - " rf <- ranger(y_tr~., data=df_tr, num.trees=200L, verbose=FALSE)\n", - " function(Xnew) predict(rf, data=as.data.frame(Xnew))$predictions\n", + " function(x_tr, y_tr) {\n", + " df_tr <- as.data.frame(cbind(y_tr = y_tr, x_tr))\n", + " rf <- ranger(y_tr ~ ., data = df_tr, num.trees = 200L, verbose = FALSE)\n", + " function(Xnew) predict(rf, data = as.data.frame(Xnew))$predictions\n", " }\n", " }\n", "}" @@ -409,30 +434,34 @@ "# rr_factory: function(X_train) -> rr prediction function\n", "# get_reg_fn: function(X_train, y_train) -> reg prediction function (pre-built by reg_fn_gen)\n", "debiased_moment_fit <- function(X, y, moment_fn, rr_factory, get_reg_fn, splits) {\n", - " n <- nrow(X)\n", + " n <- nrow(X)\n", " rr_pred <- reg_pred <- moment_pred <- moment_rr <- numeric(n)\n", - " reg_fitter <- get_reg_fn # already the fitter: function(X_tr, y_tr) -> pred_fn\n", + " reg_fitter <- get_reg_fn # already the fitter: function(X_tr, y_tr) -> pred_fn\n", "\n", " for (s in splits) {\n", - " tr <- s$train; te <- s$test\n", - " rr_fn <- rr_factory(X[tr, , drop=FALSE])\n", - " reg_fn <- reg_fitter(X[tr, , drop=FALSE], y[tr])\n", + " tr <- s$train\n", + " te <- s$test\n", + " rr_fn <- rr_factory(X[tr, , drop = FALSE])\n", + " reg_fn <- reg_fitter(X[tr, , drop = FALSE], y[tr])\n", "\n", - " rr_pred[te] <- rr_fn(X[te, , drop=FALSE])\n", - " reg_pred[te] <- reg_fn(X[te, , drop=FALSE])\n", - " moment_pred[te] <- moment_fn(X[te, , drop=FALSE], reg_fn)\n", - " moment_rr[te] <- moment_fn(X[te, , drop=FALSE], rr_fn)\n", + " rr_pred[te] <- rr_fn(X[te, , drop = FALSE])\n", + " reg_pred[te] <- reg_fn(X[te, , drop = FALSE])\n", + " moment_pred[te] <- moment_fn(X[te, , drop = FALSE], reg_fn)\n", + " moment_rr[te] <- moment_fn(X[te, , drop = FALSE], rr_fn)\n", " }\n", " moment <- moment_pred + rr_pred * (y - reg_pred)\n", - " list(moment=moment, reg_pred=reg_pred, reisz_pred=rr_pred,\n", - " moment_reisz=moment_rr, y=y)\n", + " list(\n", + " moment = moment, reg_pred = reg_pred, reisz_pred = rr_pred,\n", + " moment_reisz = moment_rr, y = y\n", + " )\n", "}\n", "\n", "avg_moment_r <- function(est, alpha = 0.1) {\n", - " m <- mean(est$moment); n <- length(est$moment)\n", + " m <- mean(est$moment)\n", + " n <- length(est$moment)\n", " se <- sd(est$moment) / sqrt(n)\n", - " h <- se * qt(1 - alpha/2, n - 1L)\n", - " c(point=m, se=se, lower=m-h, upper=m+h)\n", + " h <- se * qt(1 - alpha / 2, n - 1L)\n", + " c(point = m, se = se, lower = m - h, upper = m + h)\n", "}" ] }, @@ -450,87 +479,119 @@ "# -- Nonparametric (DebiasedMoment) sensitivity ------------------------------------\n", "\n", "sensitivity_bounds_single_r <- function(est, eta_ysq, eta_asq, alpha = NULL) {\n", - " nusq <- mean(2*est$moment_reisz - est$reisz_pred^2)\n", + " nusq <- mean(2 * est$moment_reisz - est$reisz_pred^2)\n", " sigmasq <- mean((est$y - est$reg_pred)^2)\n", - " S <- sqrt(sigmasq * nusq)\n", - " Casq <- eta_asq / (1 - eta_asq)\n", - " theta <- mean(est$moment)\n", - " error <- S * sqrt(Casq * eta_ysq)\n", - " if (is.null(alpha)) return(c(theta - error, theta + error))\n", - " psi_theta <- est$moment - theta\n", + " S <- sqrt(sigmasq * nusq)\n", + " Casq <- eta_asq / (1 - eta_asq)\n", + " theta <- mean(est$moment)\n", + " error <- S * sqrt(Casq * eta_ysq)\n", + " if (is.null(alpha)) {\n", + " return(c(theta - error, theta + error))\n", + " }\n", + " psi_theta <- est$moment - theta\n", " psi_sigmasq <- (est$y - est$reg_pred)^2 - sigmasq\n", - " psi_nusq <- 2*est$moment_reisz - est$reisz_pred^2 - nusq\n", + " psi_nusq <- 2 * est$moment_reisz - est$reisz_pred^2 - nusq\n", " k <- sqrt(Casq * eta_ysq) / (2 * S)\n", - " phi_p <- psi_theta + k * (sigmasq*psi_nusq + nusq*psi_sigmasq)\n", - " phi_m <- psi_theta - k * (sigmasq*psi_nusq + nusq*psi_sigmasq)\n", + " phi_p <- psi_theta + k * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n", + " phi_m <- psi_theta - k * (sigmasq * psi_nusq + nusq * psi_sigmasq)\n", " n <- length(psi_theta)\n", - " c(theta-error, sqrt(mean(phi_m^2)/n), theta+error, sqrt(mean(phi_p^2)/n))\n", + " c(theta - error, sqrt(mean(phi_m^2) / n), theta + error, sqrt(mean(phi_p^2) / n))\n", "}\n", "\n", "sensitivity_bounds_r <- function(est_list, eta_ysq, eta_asq, alpha = NULL) {\n", " res <- lapply(est_list, sensitivity_bounds_single_r,\n", - " eta_ysq=eta_ysq, eta_asq=eta_asq, alpha=alpha)\n", + " eta_ysq = eta_ysq, eta_asq = eta_asq, alpha = alpha\n", + " )\n", " if (is.null(alpha)) {\n", " return(c(mean(sapply(res, `[`, 1)), mean(sapply(res, `[`, 2))))\n", " }\n", - " lower <- sapply(res,`[`,1); sl <- sapply(res,`[`,2)\n", - " upper <- sapply(res,`[`,3); su <- sapply(res,`[`,4)\n", + " lower <- sapply(res, `[`, 1)\n", + " sl <- sapply(res, `[`, 2)\n", + " upper <- sapply(res, `[`, 3)\n", + " su <- sapply(res, `[`, 4)\n", " z <- qnorm(1 - alpha)\n", - " c(mean(lower) - z*sqrt(mean(sl^2)+var(lower)),\n", - " mean(upper) + z*sqrt(mean(su^2)+var(upper)))\n", + " c(\n", + " mean(lower) - z * sqrt(mean(sl^2) + var(lower)),\n", + " mean(upper) + z * sqrt(mean(su^2) + var(upper))\n", + " )\n", "}\n", "\n", - "tvalue_r <- function(est_list, value=0, leq=NULL, alpha=NULL) {\n", + "tvalue_r <- function(est_list, value = 0, leq = NULL, alpha = NULL) {\n", " t_out <- 1\n", - " for (t in seq(0, 1, length.out=1000L)) {\n", - " b <- sensitivity_bounds_r(est_list, t, t, alpha=alpha)\n", - " if (is.null(leq) && value>=b[1] && value<=b[2]) { t_out <- t; break }\n", - " if (!is.null(leq) && leq && value>=b[1]) { t_out <- t; break }\n", - " if (!is.null(leq) && !leq && value<=b[2]) { t_out <- t; break }\n", + " for (t in seq(0, 1, length.out = 1000L)) {\n", + " b <- sensitivity_bounds_r(est_list, t, t, alpha = alpha)\n", + " if (is.null(leq) && value >= b[1] && value <= b[2]) {\n", + " t_out <- t\n", + " break\n", + " }\n", + " if (!is.null(leq) && leq && value >= b[1]) {\n", + " t_out <- t\n", + " break\n", + " }\n", + " if (!is.null(leq) && !leq && value <= b[2]) {\n", + " t_out <- t\n", + " break\n", + " }\n", " }\n", " t_out\n", "}\n", "\n", "# -- PLM (DML residuals) sensitivity ------------------------------------------\n", "\n", - "dml_sensitivity_bounds_single_r <- function(est, eta_ysq, eta_asq, alpha = NULL) {\n", - " nusq <- mean(est$Dres^2)\n", - " theta <- mean(est$yres * est$Dres) / nusq\n", + "dml_sens_bounds_single_r <- function(est, eta_ysq, eta_asq, alpha = NULL) {\n", + " nusq <- mean(est$Dres^2)\n", + " theta <- mean(est$yres * est$Dres) / nusq\n", " sigmasq <- mean((est$yres - est$Dres * theta)^2)\n", - " S <- sqrt(sigmasq / nusq)\n", - " Casq <- eta_asq / (1 - eta_asq)\n", - " error <- S * sqrt(Casq * eta_ysq)\n", - " if (is.null(alpha)) return(c(theta - error, theta + error))\n", - " n <- length(est$yres)\n", - " psi_theta <- (est$yres - est$Dres * theta) * est$Dres / nusq\n", + " S <- sqrt(sigmasq / nusq)\n", + " Casq <- eta_asq / (1 - eta_asq)\n", + " error <- S * sqrt(Casq * eta_ysq)\n", + " if (is.null(alpha)) {\n", + " return(c(theta - error, theta + error))\n", + " }\n", + " n <- length(est$yres)\n", + " psi_theta <- (est$yres - est$Dres * theta) * est$Dres / nusq\n", " psi_sigmasq <- (est$yres - est$Dres * theta)^2 - sigmasq\n", - " psi_nusq <- est$Dres^2 - nusq\n", + " psi_nusq <- est$Dres^2 - nusq\n", " k <- sqrt(Casq * eta_ysq) / (2 * S)\n", - " phi_p <- psi_theta + k*(-(sigmasq/nusq^2)*psi_nusq + (1/nusq)*psi_sigmasq)\n", - " phi_m <- psi_theta - k*(-(sigmasq/nusq^2)*psi_nusq + (1/nusq)*psi_sigmasq)\n", - " c(theta-error, sqrt(mean(phi_m^2)/n), theta+error, sqrt(mean(phi_p^2)/n))\n", + " phi_p <- psi_theta + k * (-(sigmasq / nusq^2) * psi_nusq + (1 / nusq) * psi_sigmasq)\n", + " phi_m <- psi_theta - k * (-(sigmasq / nusq^2) * psi_nusq + (1 / nusq) * psi_sigmasq)\n", + " c(theta - error, sqrt(mean(phi_m^2) / n), theta + error, sqrt(mean(phi_p^2) / n))\n", "}\n", "\n", "dml_sensitivity_bounds_r <- function(est_list, eta_ysq, eta_asq, alpha = NULL) {\n", - " res <- lapply(est_list, dml_sensitivity_bounds_single_r,\n", - " eta_ysq=eta_ysq, eta_asq=eta_asq, alpha=alpha)\n", + " res <- lapply(est_list, dml_sens_bounds_single_r,\n", + " eta_ysq = eta_ysq, eta_asq = eta_asq, alpha = alpha\n", + " )\n", " if (is.null(alpha)) {\n", - " return(c(mean(sapply(res,`[`,1)), mean(sapply(res,`[`,2))))\n", + " return(c(mean(sapply(res, `[`, 1)), mean(sapply(res, `[`, 2))))\n", " }\n", - " lower <- sapply(res,`[`,1); sl <- sapply(res,`[`,2)\n", - " upper <- sapply(res,`[`,3); su <- sapply(res,`[`,4)\n", + " lower <- sapply(res, `[`, 1)\n", + " sl <- sapply(res, `[`, 2)\n", + " upper <- sapply(res, `[`, 3)\n", + " su <- sapply(res, `[`, 4)\n", " z <- qnorm(1 - alpha)\n", - " c(mean(lower) - z*sqrt(mean(sl^2)+var(lower)),\n", - " mean(upper) + z*sqrt(mean(su^2)+var(upper)))\n", + " c(\n", + " mean(lower) - z * sqrt(mean(sl^2) + var(lower)),\n", + " mean(upper) + z * sqrt(mean(su^2) + var(upper))\n", + " )\n", "}\n", "\n", - "dml_tvalue_r <- function(est_list, value=0, leq=NULL, alpha=NULL) {\n", + "dml_tvalue_r <- function(est_list, value = 0, leq = NULL, alpha = NULL) {\n", " t_out <- 1\n", - " for (t in seq(0, 1, length.out=1000L)) {\n", - " b <- dml_sensitivity_bounds_r(est_list, t, t, alpha=alpha)\n", - " if (is.null(leq) && value>=b[1] && value<=b[2]) { t_out <- t; break }\n", - " if (!is.null(leq) && leq && value>=b[1]) { t_out <- t; break }\n", - " if (!is.null(leq) && !leq && value<=b[2]) { t_out <- t; break }\n", + " for (t in seq(0, 1, length.out = 1000L)) {\n", + " b <- dml_sensitivity_bounds_r(est_list, t, t, alpha = alpha)\n", + " if (is.null(leq) && value >= b[1] && value <= b[2]) {\n", + " t_out <- t\n", + " break\n", + " }\n", + " if (!is.null(leq) && leq && value >= b[1]) {\n", + " t_out <- t\n", + " break\n", + " }\n", + " if (!is.null(leq) && !leq && value <= b[2]) {\n", + " t_out <- t\n", + " break\n", + " }\n", " }\n", " t_out\n", "}" @@ -558,36 +619,36 @@ "# Main estimation loop matching the Python notebook structure.\n", "# estimator_fn(X, y, splits) returns list(estimates=c(point,se,lower,upper), model=...)\n", "generic_pipeline_r <- function(estimator_fn, q_grid, name, n_seeds = 5L) {\n", - " all_res <- list()\n", + " all_res <- list()\n", " all_ests <- list()\n", " for (q in q_grid) {\n", " cat(sprintf(\"q=%d\\n\", q))\n", - " est_mat <- matrix(NA, n_seeds, 4L)\n", + " est_mat <- matrix(NA, n_seeds, 4L)\n", " seed_ests <- list()\n", " for (i in seq_len(n_seeds)) {\n", " seed <- 122L + i\n", - " dat <- get_data_r(q, region=region, synthetic=synthetic, random_seed=seed)\n", - " out <- estimator_fn(dat$X, dat$y, dat$splits)\n", + " dat <- get_data_r(q, region = region, synthetic = synthetic, random_seed = seed)\n", + " out <- estimator_fn(dat$X, dat$y, dat$splits)\n", " est_mat[i, ] <- out$estimates\n", " seed_ests[[i]] <- out$model\n", " cat(sprintf(\" seed=%d: %.3f\\n\", seed, out$estimates[1]))\n", " }\n", " key <- sprintf(\"q=%d\", q)\n", - " all_res[[key]] <- data.frame(\n", + " all_res[[key]] <- data.frame(\n", " method = name,\n", " point = mean(est_mat[, 1]),\n", " stderr = sqrt(mean(est_mat[, 2]^2) + var(est_mat[, 1]))\n", " )\n", " all_ests[[key]] <- seed_ests\n", " }\n", - " list(res=all_res, ests=all_ests)\n", + " list(res = all_res, ests = all_ests)\n", "}\n", "\n", "transform_res_to_df_r <- function(res_list, q_grid, alpha = NULL) {\n", " rows <- list()\n", " for (q in q_grid) {\n", " key <- sprintf(\"q=%d\", q)\n", - " r <- res_list[[key]]\n", + " r <- res_list[[key]]\n", " if (!is.null(alpha)) {\n", " z <- qnorm(1 - alpha)\n", " lo <- r$point - z * r$stderr\n", @@ -597,7 +658,7 @@ " rows[[key]] <- sprintf(\"%.2f\", r$point)\n", " }\n", " }\n", - " as.data.frame(rows, check.names=FALSE)\n", + " as.data.frame(rows, check.names = FALSE)\n", "}" ] }, @@ -624,7 +685,7 @@ "source": [ "nonlinrr_r <- function(X, y, splits) {\n", " rr_factory <- rr_fn_gen(X, moment_fn, splits)\n", - " get_reg <- reg_fn_gen(X, y, splits)\n", + " get_reg <- reg_fn_gen(X, y, splits)\n", "\n", " fit <- debiased_moment_fit(X, y, moment_fn, rr_factory, get_reg, splits)\n", " list(estimates = avg_moment_r(fit, alpha = 0.1), model = fit)\n", @@ -765,28 +826,29 @@ "outputs": [], "source": [ "dml_estimator_r <- function(X, y, splits) {\n", - " n <- nrow(X)\n", - " D <- X[, 1L]\n", + " n <- nrow(X)\n", + " D <- X[, 1L]\n", " Xcov <- X[, -1L, drop = FALSE]\n", " yres <- Dres <- numeric(n)\n", "\n", " for (s in splits) {\n", - " tr <- s$train; te <- s$test\n", + " tr <- s$train\n", + " te <- s$test\n", " df_y <- as.data.frame(cbind(y_tr = y[tr], Xcov[tr, ]))\n", " df_d <- as.data.frame(cbind(D_tr = D[tr], Xcov[tr, ]))\n", " rf_y <- ranger(y_tr ~ ., data = df_y, num.trees = 200L, verbose = FALSE)\n", " rf_d <- ranger(D_tr ~ ., data = df_d, num.trees = 200L, verbose = FALSE)\n", - " yres[te] <- y[te] - predict(rf_y, data=as.data.frame(Xcov[te,]))$predictions\n", - " Dres[te] <- D[te] - predict(rf_d, data=as.data.frame(Xcov[te,]))$predictions\n", + " yres[te] <- y[te] - predict(rf_y, data = as.data.frame(Xcov[te, ]))$predictions\n", + " Dres[te] <- D[te] - predict(rf_d, data = as.data.frame(Xcov[te, ]))$predictions\n", " }\n", "\n", - " nusq <- mean(Dres^2)\n", + " nusq <- mean(Dres^2)\n", " theta <- mean(yres * Dres) / nusq\n", - " psi <- (yres - Dres * theta) * Dres / nusq\n", - " se <- sqrt(mean(psi^2) / n)\n", + " psi <- (yres - Dres * theta) * Dres / nusq\n", + " se <- sqrt(mean(psi^2) / n)\n", " list(\n", - " estimates = c(point=theta, se=se, lower=theta-1.645*se, upper=theta+1.645*se),\n", - " model = list(yres=yres, Dres=Dres)\n", + " estimates = c(point = theta, se = se, lower = theta - 1.645 * se, upper = theta + 1.645 * se),\n", + " model = list(yres = yres, Dres = Dres)\n", " )\n", "}" ] @@ -925,20 +987,26 @@ "outputs": [], "source": [ "table_rr <- list()\n", - "q5_ests <- ests_rr[[\"q=5\"]]\n", + "q5_ests <- ests_rr[[\"q=5\"]]\n", "\n", - "table_rr[[\"point\"]] <- sprintf(\"%.3f (%.3f)\",\n", - " res_rr[[\"q=5\"]]$point, res_rr[[\"q=5\"]]$stderr)\n", - "table_rr[[\"Bounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", - " sensitivity_bounds_r(q5_ests, .03, .03, alpha=NULL)[1],\n", - " sensitivity_bounds_r(q5_ests, .03, .03, alpha=NULL)[2])\n", - "table_rr[[\"CIBounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", - " sensitivity_bounds_r(q5_ests, .03, .03, alpha=.05)[1],\n", - " sensitivity_bounds_r(q5_ests, .03, .03, alpha=.05)[2])\n", - "table_rr[[\"RV(v=-1.5)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=-1.5, leq=TRUE))\n", - "table_rr[[\"RV(v=-1.5,a=.05)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=-1.5, leq=TRUE, alpha=.05))\n", - "table_rr[[\"RV(v=0)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=0, leq=FALSE))\n", - "table_rr[[\"RV(v=0,a=.05)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value=0, leq=FALSE, alpha=.05))" + "table_rr[[\"point\"]] <- sprintf(\n", + " \"%.3f (%.3f)\",\n", + " res_rr[[\"q=5\"]]$point, res_rr[[\"q=5\"]]$stderr\n", + ")\n", + "table_rr[[\"Bounds\"]] <- sprintf(\n", + " \"[%.3f, %.3f]\",\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha = NULL)[1],\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha = NULL)[2]\n", + ")\n", + "table_rr[[\"CIBounds\"]] <- sprintf(\n", + " \"[%.3f, %.3f]\",\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha = .05)[1],\n", + " sensitivity_bounds_r(q5_ests, .03, .03, alpha = .05)[2]\n", + ")\n", + "table_rr[[\"RV(v=-1.5)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value = -1.5, leq = TRUE))\n", + "table_rr[[\"RV(v=-1.5,a=.05)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value = -1.5, leq = TRUE, alpha = .05))\n", + "table_rr[[\"RV(v=0)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value = 0, leq = FALSE))\n", + "table_rr[[\"RV(v=0,a=.05)\"]] <- sprintf(\"%.3f\", tvalue_r(q5_ests, value = 0, leq = FALSE, alpha = .05))" ] }, { @@ -953,20 +1021,26 @@ "outputs": [], "source": [ "table_dml <- list()\n", - "q5_dml <- ests_dml[[\"q=5\"]]\n", + "q5_dml <- ests_dml[[\"q=5\"]]\n", "\n", - "table_dml[[\"point\"]] <- sprintf(\"%.3f (%.3f)\",\n", - " res_dml[[\"q=5\"]]$point, res_dml[[\"q=5\"]]$stderr)\n", - "table_dml[[\"Bounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", - " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=NULL)[1],\n", - " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=NULL)[2])\n", - "table_dml[[\"CIBounds\"]] <- sprintf(\"[%.3f, %.3f]\",\n", - " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=.05)[1],\n", - " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha=.05)[2])\n", - "table_dml[[\"RV(v=-1.5)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=-1.5, leq=TRUE))\n", - "table_dml[[\"RV(v=-1.5,a=.05)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=-1.5, leq=TRUE, alpha=.05))\n", - "table_dml[[\"RV(v=0)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=0, leq=FALSE))\n", - "table_dml[[\"RV(v=0,a=.05)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value=0, leq=FALSE, alpha=.05))" + "table_dml[[\"point\"]] <- sprintf(\n", + " \"%.3f (%.3f)\",\n", + " res_dml[[\"q=5\"]]$point, res_dml[[\"q=5\"]]$stderr\n", + ")\n", + "table_dml[[\"Bounds\"]] <- sprintf(\n", + " \"[%.3f, %.3f]\",\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha = NULL)[1],\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha = NULL)[2]\n", + ")\n", + "table_dml[[\"CIBounds\"]] <- sprintf(\n", + " \"[%.3f, %.3f]\",\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha = .05)[1],\n", + " dml_sensitivity_bounds_r(q5_dml, .03, .03, alpha = .05)[2]\n", + ")\n", + "table_dml[[\"RV(v=-1.5)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value = -1.5, leq = TRUE))\n", + "table_dml[[\"RV(v=-1.5,a=.05)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value = -1.5, leq = TRUE, alpha = .05))\n", + "table_dml[[\"RV(v=0)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value = 0, leq = FALSE))\n", + "table_dml[[\"RV(v=0,a=.05)\"]] <- sprintf(\"%.3f\", dml_tvalue_r(q5_dml, value = 0, leq = FALSE, alpha = .05))" ] }, { @@ -1063,29 +1137,34 @@ } ], "source": [ - "qgr <- 1:4\n", - "point <- sapply(qgr, function(q) res_rr[[sprintf(\"q=%d\",q)]]$point)\n", - "se <- sapply(qgr, function(q) res_rr[[sprintf(\"q=%d\",q)]]$stderr)\n", + "qgr <- 1:4\n", + "point <- sapply(qgr, function(q) res_rr[[sprintf(\"q=%d\", q)]]$point)\n", + "se <- sapply(qgr, function(q) res_rr[[sprintf(\"q=%d\", q)]]$stderr)\n", "lower <- point - 1.645 * se\n", "upper <- point + 1.645 * se\n", - "sens_lo <- sapply(qgr, function(q)\n", - " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[1])\n", - "sens_hi <- sapply(qgr, function(q)\n", - " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[2])\n", - "sens_lo95 <- sapply(qgr, function(q)\n", - " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[1])\n", - "sens_hi95 <- sapply(qgr, function(q)\n", - " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[2])\n", + "sens_lo <- sapply(qgr, function(q) {\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\", q)]], .03, .03, alpha = NULL)[1]\n", + "})\n", + "sens_hi <- sapply(qgr, function(q) {\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\", q)]], .03, .03, alpha = NULL)[2]\n", + "})\n", + "sens_lo95 <- sapply(qgr, function(q) {\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\", q)]], .03, .03, alpha = .05)[1]\n", + "})\n", + "sens_hi95 <- sapply(qgr, function(q) {\n", + " sensitivity_bounds_r(ests_rr[[sprintf(\"q=%d\", q)]], .03, .03, alpha = .05)[2]\n", + "})\n", "\n", - "df_rr <- data.frame(q=qgr, point, lower, upper, sens_lo, sens_hi, sens_lo95, sens_hi95)\n", + "df_rr <- data.frame(q = qgr, point, lower, upper, sens_lo, sens_hi, sens_lo95, sens_hi95)\n", "\n", - "ggplot(df_rr, aes(x=q)) +\n", - " geom_line(aes(y=point)) +\n", - " geom_ribbon(aes(ymin=lower, ymax=upper), alpha=.4, fill=\"steelblue\") +\n", - " geom_ribbon(aes(ymin=sens_lo, ymax=sens_hi), alpha=.6, fill=\"orange\") +\n", - " geom_ribbon(aes(ymin=sens_lo95, ymax=sens_hi95), alpha=.2, fill=\"orange\") +\n", - " scale_x_continuous(breaks=qgr) +\n", - " xlab(\"Income group\") + ylab(\"Average price elasticity\") +\n", + "ggplot(df_rr, aes(x = q)) +\n", + " geom_line(aes(y = point)) +\n", + " geom_ribbon(aes(ymin = lower, ymax = upper), alpha = .4, fill = \"steelblue\") +\n", + " geom_ribbon(aes(ymin = sens_lo, ymax = sens_hi), alpha = .6, fill = \"orange\") +\n", + " geom_ribbon(aes(ymin = sens_lo95, ymax = sens_hi95), alpha = .2, fill = \"orange\") +\n", + " scale_x_continuous(breaks = qgr) +\n", + " xlab(\"Income group\") +\n", + " ylab(\"Average price elasticity\") +\n", " ggtitle(\"Non-parametric\") +\n", " theme_bw()" ] @@ -1117,30 +1196,37 @@ } ], "source": [ - "point_d <- sapply(qgr, function(q) res_dml[[sprintf(\"q=%d\",q)]]$point)\n", - "se_d <- sapply(qgr, function(q) res_dml[[sprintf(\"q=%d\",q)]]$stderr)\n", + "point_d <- sapply(qgr, function(q) res_dml[[sprintf(\"q=%d\", q)]]$point)\n", + "se_d <- sapply(qgr, function(q) res_dml[[sprintf(\"q=%d\", q)]]$stderr)\n", "lower_d <- point_d - 1.645 * se_d\n", "upper_d <- point_d + 1.645 * se_d\n", - "sens_lo_d <- sapply(qgr, function(q)\n", - " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[1])\n", - "sens_hi_d <- sapply(qgr, function(q)\n", - " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=NULL)[2])\n", - "sens_lo95_d <- sapply(qgr, function(q)\n", - " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[1])\n", - "sens_hi95_d <- sapply(qgr, function(q)\n", - " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\",q)]], .03, .03, alpha=.05)[2])\n", + "sens_lo_d <- sapply(qgr, function(q) {\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\", q)]], .03, .03, alpha = NULL)[1]\n", + "})\n", + "sens_hi_d <- sapply(qgr, function(q) {\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\", q)]], .03, .03, alpha = NULL)[2]\n", + "})\n", + "sens_lo95_d <- sapply(qgr, function(q) {\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\", q)]], .03, .03, alpha = .05)[1]\n", + "})\n", + "sens_hi95_d <- sapply(qgr, function(q) {\n", + " dml_sensitivity_bounds_r(ests_dml[[sprintf(\"q=%d\", q)]], .03, .03, alpha = .05)[2]\n", + "})\n", "\n", - "df_dml <- data.frame(q=qgr, point=point_d, lower=lower_d, upper=upper_d,\n", - " sens_lo=sens_lo_d, sens_hi=sens_hi_d,\n", - " sens_lo95=sens_lo95_d, sens_hi95=sens_hi95_d)\n", + "df_dml <- data.frame(\n", + " q = qgr, point = point_d, lower = lower_d, upper = upper_d,\n", + " sens_lo = sens_lo_d, sens_hi = sens_hi_d,\n", + " sens_lo95 = sens_lo95_d, sens_hi95 = sens_hi95_d\n", + ")\n", "\n", - "ggplot(df_dml, aes(x=q)) +\n", - " geom_line(aes(y=point)) +\n", - " geom_ribbon(aes(ymin=lower, ymax=upper), alpha=.4, fill=\"steelblue\") +\n", - " geom_ribbon(aes(ymin=sens_lo, ymax=sens_hi), alpha=.6, fill=\"orange\") +\n", - " geom_ribbon(aes(ymin=sens_lo95, ymax=sens_hi95), alpha=.2, fill=\"orange\") +\n", - " scale_x_continuous(breaks=qgr) +\n", - " xlab(\"Income group\") + ylab(\"Average price elasticity\") +\n", + "ggplot(df_dml, aes(x = q)) +\n", + " geom_line(aes(y = point)) +\n", + " geom_ribbon(aes(ymin = lower, ymax = upper), alpha = .4, fill = \"steelblue\") +\n", + " geom_ribbon(aes(ymin = sens_lo, ymax = sens_hi), alpha = .6, fill = \"orange\") +\n", + " geom_ribbon(aes(ymin = sens_lo95, ymax = sens_hi95), alpha = .2, fill = \"orange\") +\n", + " scale_x_continuous(breaks = qgr) +\n", + " xlab(\"Income group\") +\n", + " ylab(\"Average price elasticity\") +\n", " ggtitle(\"Partially linear\") +\n", " theme_bw()" ] From 93004e745cabacab43c621415d800cddd99f1067 Mon Sep 17 00:00:00 2001 From: Maxi <167797320+1106maxi@users.noreply.github.com> Date: Thu, 22 Oct 2026 12:38:27 +0200 Subject: [PATCH 32/32] Fix flake8 errors in T-ADML gas notebook Co-Authored-By: Claude Opus 5.5 --- T-ADML/python-ADML-gas.ipynb | 92 ++++++++++++++++++------------------ 1 file changed, 46 insertions(+), 46 deletions(-) diff --git a/T-ADML/python-ADML-gas.ipynb b/T-ADML/python-ADML-gas.ipynb index 98808782..e688ed72 100644 --- a/T-ADML/python-ADML-gas.ipynb +++ b/T-ADML/python-ADML-gas.ipynb @@ -20,10 +20,7 @@ "metadata": {}, "outputs": [], "source": [ - "try:\n", - " import econml\n", - "except ImportError:\n", - " !pip install econml" + "!pip install econml==0.16.0" ] }, { @@ -38,8 +35,6 @@ "import scipy\n", "import scipy.special\n", "import scipy.stats\n", - "import joblib\n", - "from statsmodels.api import OLS\n", "from sklearn.model_selection import StratifiedGroupKFold, KFold\n", "import pandas as pd\n", "import math\n", @@ -47,8 +42,9 @@ "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", "from sklearn.pipeline import Pipeline\n", - "from sklearn.linear_model import LassoCV, LogisticRegressionCV, LinearRegression, Lasso, LogisticRegression\n", - "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.linear_model import LinearRegression, Lasso, LogisticRegression\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from IPython.display import display\n", "from scipy.optimize import fmin_l_bfgs_b\n", "from econml.dml import LinearDML\n", "from econml.sklearn_extensions.model_selection import GridSearchCVList\n", @@ -92,8 +88,8 @@ " a = 1.0 * np.array(data).flatten()\n", " n = len(a)\n", " m, se = np.mean(a), scipy.stats.sem(a)\n", - " h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n", - " return m, se, m-h, m+h" + " h = se * scipy.stats.t.ppf((1 + confidence) / 2., n - 1)\n", + " return m, se, m - h, m + h" ] }, { @@ -145,9 +141,11 @@ " Mfeats = np.zeros((n_samples, n_feats))\n", " for t in np.arange(n_feats):\n", " if t == 0:\n", - " feat_fn = lambda x: np.ones(x.shape[0])\n", + " def feat_fn(x):\n", + " return np.ones(x.shape[0])\n", " else:\n", - " feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n", + " def feat_fn(x):\n", + " return self.featurizer_.transform(x)[:, t - 1]\n", " Mfeats[:, t] = self.moment_fn(X, feat_fn)\n", " Sigma = Xfeats.T @ Xfeats / n_samples\n", " reg = self.alpha * np.eye(n_feats)\n", @@ -181,9 +179,11 @@ " Mfeats = np.zeros((n_samples, n_feats))\n", " for t in np.arange(n_feats):\n", " if t == 0:\n", - " feat_fn = lambda x: np.ones(x.shape[0])\n", + " def feat_fn(x):\n", + " return np.ones(x.shape[0])\n", " else:\n", - " feat_fn = lambda x: self.featurizer_.transform(x)[:, t - 1]\n", + " def feat_fn(x):\n", + " return self.featurizer_.transform(x)[:, t - 1]\n", " Mfeats[:, t] = self.moment_fn(X, feat_fn)\n", " Sigma = Xfeats.T @ Xfeats / n_samples\n", " mfeats = np.mean(Mfeats, axis=0)\n", @@ -205,8 +205,8 @@ " return loss, jac\n", "\n", " w, _, _ = fmin_l_bfgs_b(loss_and_jac, np.zeros(2 * n_feats),\n", - " bounds=[(0, None)] * 2 * n_feats,\n", - " pgtol=self.tol)\n", + " bounds=[(0, None)] * 2 * n_feats,\n", + " pgtol=self.tol)\n", " params = w[:n_feats] - w[n_feats:]\n", " self.coef_ = params[1:]\n", " self.intercept_ = params[0]\n", @@ -440,25 +440,25 @@ "def first_stage_reg(numeric_columns=[], cv=None):\n", " return GridSearchCVList([\n", " Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n", - " remainder='passthrough')),\n", - " ('lasso', Lasso())]),\n", + " remainder='passthrough')),\n", + " ('lasso', Lasso())]),\n", " Pipeline([('scale', ColumnTransformer([('num', StandardScaler(), numeric_columns)],\n", - " remainder='passthrough')),\n", - " ('feat', Featurizer(degree=1, normalize=False)),\n", - " ('lasso', Lasso())]),\n", + " remainder='passthrough')),\n", + " ('feat', Featurizer(degree=1, normalize=False)),\n", + " ('lasso', Lasso())]),\n", " RandomForestRegressor(n_estimators=100, random_state=123),\n", " RFreg(reg_feature_fns=poly_feature_fns(10),\n", " min_var_leaf_on_val=True, min_impurity_decrease=1e-4,\n", " max_samples=.85, inference=False, subforest_size=1, random_state=123)\n", " ],\n", - " param_grid_list=[\n", + " param_grid_list=[\n", " {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n", " {'lasso__alpha': [1e-6, 1e-5, 0.0001, .001, .01, .1, 1, 10]},\n", " {'max_depth': [3, 5], 'min_samples_leaf': [10, 50]},\n", " {'reg_feature_fns': [poly_feature_fns(2)], 'min_samples_leaf': [10],\n", " 'min_var_fraction_leaf': [0.01, 0.1], 'l2': [1e-3, 1e-2]}\n", " ],\n", - " cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n", + " cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n", "\n", "\n", "def reg_fn_gen(numeric_columns, cv=None, verbose=0):\n", @@ -486,7 +486,7 @@ " min_var_leaf_on_val=True, min_impurity_decrease=0.001,\n", " max_samples=.65, inference=False, subforest_size=1, random_state=123)\n", " ],\n", - " param_grid_list=[\n", + " param_grid_list=[\n", " {'alpha': np.geomspace(1e-6, 1e3, 20)},\n", " {'alpha': np.geomspace(1e-6, 1e3, 20)},\n", " {'alpha': np.geomspace(1e-6, 1e3, 20), 'l1_ratio': [.5, 1.0]},\n", @@ -495,7 +495,7 @@ " 'min_samples_leaf': [10, 50], 'min_var_fraction_leaf': [0.01, 0.1],\n", " 'l2': [1e-5, 1e-3], 'max_depth': [5, None]}\n", " ],\n", - " scoring=neg_reisz_score_gen(moment_fn), cv=cv, verbose=0, n_jobs=-1)\n", + " scoring=neg_reisz_score_gen(moment_fn), cv=cv, verbose=0, n_jobs=-1)\n", "\n", "\n", "def rr_fn_gen(moment_fn, numeric_columns, cv=None, verbose=0):\n", @@ -514,8 +514,8 @@ "\n", " def fit(self, X, y):\n", " self.model_ = RandomForestClassifier(n_estimators=100, random_state=123,\n", - " max_depth=self.max_depth,\n", - " min_samples_leaf=self.min_samples_leaf)\n", + " max_depth=self.max_depth,\n", + " min_samples_leaf=self.min_samples_leaf)\n", " self.model_.fit(X, y)\n", " return self\n", "\n", @@ -576,7 +576,7 @@ "def sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n", " if alpha is None:\n", " lower, upper = zip(*[sensitivity_bounds_single(est, eta_ysq, eta_asq,\n", - " alpha=alpha, inds=inds) for est in est_list])\n", + " alpha=alpha, inds=inds) for est in est_list])\n", " return np.mean(lower), np.mean(upper)\n", " else:\n", " lower, std_lower, upper, std_upper = zip(\n", @@ -594,9 +594,9 @@ " l, u = sensitivity_bounds(est_list, t, t, alpha=alpha, inds=inds)\n", " if (leq is None) & (value >= l) & (value <= u):\n", " break\n", - " if (leq == True) & (value >= l):\n", + " if leq and value >= l:\n", " break\n", - " if (leq == False) & (value <= u):\n", + " if leq is not None and not leq and value <= u:\n", " break\n", " return t\n", "\n", @@ -655,7 +655,7 @@ "def dml_sensitivity_bounds(est_list, eta_ysq, eta_asq, alpha=None, inds=None):\n", " if alpha is None:\n", " lower, upper = zip(*[dml_sensitivity_bounds_single(est, eta_ysq, eta_asq,\n", - " alpha=alpha, inds=inds)\n", + " alpha=alpha, inds=inds)\n", " for est in est_list])\n", " return np.mean(lower), np.mean(upper)\n", " else:\n", @@ -674,9 +674,9 @@ " l, u = dml_sensitivity_bounds(est, t, t, alpha=alpha, inds=inds)\n", " if (leq is None) & (value >= l) & (value <= u):\n", " break\n", - " if (leq == True) & (value >= l):\n", + " if leq and value >= l:\n", " break\n", - " if (leq == False) & (value <= u):\n", + " if leq is not None and not leq and value <= u:\n", " break\n", " return t\n", "\n", @@ -731,7 +731,7 @@ " epsilon = 0.01\n", " t1 = np.hstack([x[:, [0]] + epsilon, x[:, 1:]])\n", " t0 = np.hstack([x[:, [0]] - epsilon, x[:, 1:]])\n", - " return (test_fn(t1) - test_fn(t0)) / (2*epsilon)" + " return (test_fn(t1) - test_fn(t0)) / (2 * epsilon)" ] }, { @@ -757,14 +757,16 @@ "\n", "# for semi-synthetic data generation\n", "def true_f(X):\n", - " y = (-.7 - .4 * scipy.special.expit(3 * (1 - 2*(X[:, 1] - 10.5)))) * X[:, 0]\n", + " y = (-.7 - .4 * scipy.special.expit(3 * (1 - 2 * (X[:, 1] - 10.5)))) * X[:, 0]\n", " y += scipy.special.expit(3 * (1 - 2 * (X[:, 1] > 11.5)))\n", " return y\n", "\n", "\n", "def get_data(q=None, synthetic=False, region=True, state=False, region_ids=None, random_seed=123):\n", - " data_url = \"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/update-notebooks/data/data_BHP2_with_region.csv\"\n", - " # Need to update this url when we launch the new version of the repo with the data in it. For now, this is a temporary url.\n", + " data_url = (\"https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/\"\n", + " \"update-notebooks/data/data_BHP2_with_region.csv\")\n", + " # Need to update this url when we launch the new version of the repo with the data in it.\n", + " # For now, this is a temporary url.\n", " df = pd.read_csv(data_url)\n", " # Throw away low prices and low income due to sample quality as in Chetverikov\n", " df = df[df['log_p'] > math.log(1.2)]\n", @@ -786,7 +788,6 @@ " if region_ids is not None:\n", " df = df[np.any([df['region'] == r for r in region_ids], axis=0)]\n", " Xdf = df.iloc[:, 1:] # Covariates\n", - " X_nostatedum = Xdf.drop(['distance_oil1000', 'share'], axis=1).values\n", " if state:\n", " state_dum = pd.get_dummies(Xdf['state_fips'], prefix='state', drop_first=True)\n", " Xdf = pd.concat([Xdf, state_dum], axis=1)\n", @@ -857,8 +858,7 @@ " res[f'q={q}'][f'{name}'] = {}\n", " res[f'q={q}'][f'{name}']['point'] = np.mean(estimate_list[:, 0])\n", " if estimate_list.shape[1] > 2:\n", - " res[f'q={q}'][f'{name}']['stderr'] = np.sqrt(np.mean(estimate_list[:, 1]**2)\n", - " + np.var(estimate_list[:, 0]))\n", + " res[f'q={q}'][f'{name}']['stderr'] = np.sqrt(np.mean(estimate_list[:, 1]**2) + np.var(estimate_list[:, 0]))\n", " res[f'q={q}'] = pd.DataFrame(res[f'q={q}']).transpose()\n", " return pd.concat(res), ests" ] @@ -1219,7 +1219,7 @@ "source": [ "table = {}\n", "table['point'] = '{:.3f} ({:.3f})'.format(res['rr'].loc[(f'q={5}', 'dr'), 'point'],\n", - " res['rr'].loc[(f'q={5}', 'dr'), 'stderr'])\n", + " res['rr'].loc[(f'q={5}', 'dr'), 'stderr'])\n", "\n", "est_list = ests['rr'][f'q={5}']\n", "table['Bounds'] = '[{:.3f}, {:.3f}]'.format(*sensitivity_bounds(est_list, .03, .03, alpha=None))\n", @@ -1239,7 +1239,7 @@ "source": [ "table2 = {}\n", "table2['point'] = '{:.3f} ({:.3f})'.format(res['dml'].loc[(f'q={5}', 'dml'), 'point'],\n", - " res['dml'].loc[(f'q={5}', 'dml'), 'stderr'])\n", + " res['dml'].loc[(f'q={5}', 'dml'), 'stderr'])\n", "\n", "est_list = ests['dml'][f'q={5}']\n", "table2['Bounds'] = '[{:.3f}, {:.3f}]'.format(*dml_sensitivity_bounds(est_list, .03, .03, alpha=None))\n", @@ -1353,9 +1353,9 @@ "qgr = np.arange(1, 5)\n", "\n", "point = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] for q in qgr]\n", - "lower = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] - 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", + "lower = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] - 1.645 * res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", " for q in qgr]\n", - "upper = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] + 1.645*res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", + "upper = [res['rr'].loc[(f'q={q}', 'dr'), 'point'] + 1.645 * res['rr'].loc[(f'q={q}', 'dr'), 'stderr']\n", " for q in qgr]\n", "sens_band = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\n", "sens_band_unc = [sensitivity_bounds(ests['rr'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n", @@ -1397,9 +1397,9 @@ "qgr = np.arange(1, 5)\n", "\n", "point = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] for q in qgr]\n", - "lower = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] - 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", + "lower = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] - 1.645 * res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", " for q in qgr]\n", - "upper = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] + 1.645*res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", + "upper = [res['dml'].loc[(f'q={q}', 'dml'), 'point'] + 1.645 * res['dml'].loc[(f'q={q}', 'dml'), 'stderr']\n", " for q in qgr]\n", "sens_band = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=None) for q in qgr]\n", "sens_band_unc = [dml_sensitivity_bounds(ests['dml'][f'q={q}'], .03, .03, alpha=.05) for q in qgr]\n",