![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
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",
- ":\n",
+ "\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": {
diff --git a/CM2/python-colliderbias-hollywood.ipynb b/CM2/python-colliderbias-hollywood.ipynb
index 28d23240..8e2fa6e6 100644
--- a/CM2/python-colliderbias-hollywood.ipynb
+++ b/CM2/python-colliderbias-hollywood.ipynb
@@ -50,9 +50,7 @@
"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",
"import pylab as plt"
]
},
@@ -131,9 +129,14 @@
},
"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."
]
}
],
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/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": {
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/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\")"
]
},
{
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 edc2592b..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"
]
},
{
@@ -318,13 +318,6 @@
"summary['CI upper'] = df['point'] + c * df['stderr']\n",
"summary"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
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 91215793..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"
]
},
{
@@ -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",
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": {
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": {
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/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()
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/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/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"
]
},
{
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",
diff --git a/T-ADML/gasoline_example/.DS_Store b/T-ADML/gasoline_example/.DS_Store
new file mode 100644
index 00000000..266cc7e8
Binary files /dev/null and b/T-ADML/gasoline_example/.DS_Store differ
diff --git a/T-ADML/gasoline_example/BHP2017/data_BHP2_with_region.csv b/T-ADML/gasoline_example/BHP2017/data_BHP2_with_region.csv
new file mode 100644
index 00000000..5c0b86a7
--- /dev/null
+++ b/T-ADML/gasoline_example/BHP2017/data_BHP2_with_region.csv
@@ -0,0 +1,3641 @@
+log_q,log_p,log_y,cl5_secondcity_d,cl5_smtown_d,cl5_suburban_d,cl5_urban_d,distance_oil1000,log_driver,log_hhr_age,log_hhsize,popdensity_d2,popdensity_d3,popdensity_d4,popdensity_d5,popdensity_d6,popdensity_d7,popdensity_d8,publictransit_d,share,state_fips,total_wrkr,region
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diff --git a/T-ADML/gasoline_example/ablation.py b/T-ADML/gasoline_example/ablation.py
new file mode 100644
index 00000000..29681197
--- /dev/null
+++ 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_local.ipynb b/T-ADML/gasoline_example/python_ADML_gas_local.ipynb
new file mode 100644
index 00000000..9163e4d0
--- /dev/null
+++ b/T-ADML/gasoline_example/python_ADML_gas_local.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": [
+ "| \n", + " | q=1 | \n", + "q=2 | \n", + "q=3 | \n", + "q=4 | \n", + "all | \n", + "
|---|---|---|---|---|---|
| dr | \n", + "-0.56 (0.37) [-1.16, 0.04] | \n", + "-0.77 (0.31) [-1.28, -0.26] | \n", + "-0.78 (0.32) [-1.31, -0.25] | \n", + "-1.84 (0.49) [-2.64, -1.04] | \n", + "-0.57 (0.24) [-0.97, -0.18] | \n", + "
| \n", + " | q=1 | \n", + "q=2 | \n", + "q=3 | \n", + "q=4 | \n", + "all | \n", + "
|---|---|---|---|---|---|
| dml | \n", + "-0.45 (0.37) [-1.06, 0.17] | \n", + "-0.85 (0.31) [-1.36, -0.33] | \n", + "-0.78 (0.34) [-1.34, -0.22] | \n", + "-1.94 (0.54) [-2.83, -1.06] | \n", + "-0.84 (0.25) [-1.26, -0.43] | \n", + "
| \n", + " | point | \n", + "Bounds | \n", + "CIBounds | \n", + "RV(v=-1.5) | \n", + "RV(v=-1.5,a=.05) | \n", + "RV(v=0) | \n", + "RV(v=0,a=.05) | \n", + "
|---|---|---|---|---|---|---|---|
| Non-parametric | \n", + "-0.573 (0.240) | \n", + "[-1.104, -0.043] | \n", + "[-1.500, 0.350] | \n", + "0.052 | \n", + "0.030 | \n", + "0.033 | \n", + "0.011 | \n", + "
| Partially linear | \n", + "-0.844 (0.251) | \n", + "[-1.299, -0.390] | \n", + "[-1.712, 0.022] | \n", + "0.043 | \n", + "0.017 | \n", + "0.055 | \n", + "0.029 | \n", + "
| \n", + " | Short ATE | \n", + "Bias bound | \n", + "ATE bounds | \n", + "95% conf. bounds | \n", + "
|---|---|---|---|---|
| Partially linear | \n", + "$9,114 | \n", + "$4,201 | \n", + "[$4,918, $13,311] | \n", + "[$2,725, $15,573] | \n", + "
| Nonparametric | \n", + "$8,198 | \n", + "$4,529 | \n", + "[$3,644, $12,752] | \n", + "[$1,618, $14,805] | \n", + "
| \n", + " | gain.Y | \n", + "gain.D | \n", + "rho | \n", + "
|---|---|---|---|
| inc | \n", + "0.1366 | \n", + "0.0502 | \n", + "-0.3372 | \n", + "
| pira | \n", + "0.0487 | \n", + "0.0081 | \n", + "-0.1317 | \n", + "
| twoearn | \n", + "0.0399 | \n", + "0.0112 | \n", + "0.2589 | \n", + "
| \n", + " | q=1 | \n", + "q=2 | \n", + "q=3 | \n", + "q=4 | \n", + "all | \n", + "
|---|---|---|---|---|---|
| dr | \n", + "-0.56 (0.37) [-1.16, 0.04] | \n", + "-0.77 (0.31) [-1.28, -0.26] | \n", + "-0.78 (0.32) [-1.31, -0.25] | \n", + "-1.84 (0.49) [-2.64, -1.04] | \n", + "-0.57 (0.24) [-0.97, -0.18] | \n", + "
| \n", + " | q=1 | \n", + "q=2 | \n", + "q=3 | \n", + "q=4 | \n", + "all | \n", + "
|---|---|---|---|---|---|
| dml | \n", + "-0.45 (0.37) [-1.06, 0.17] | \n", + "-0.85 (0.31) [-1.36, -0.33] | \n", + "-0.78 (0.34) [-1.34, -0.22] | \n", + "-1.94 (0.54) [-2.83, -1.06] | \n", + "-0.84 (0.25) [-1.26, -0.43] | \n", + "
| \n", + " | point | \n", + "Bounds | \n", + "CIBounds | \n", + "RV(v=-1.5) | \n", + "RV(v=-1.5,a=.05) | \n", + "RV(v=0) | \n", + "RV(v=0,a=.05) | \n", + "
|---|---|---|---|---|---|---|---|
| Non-parametric | \n", + "-0.573 (0.240) | \n", + "[-1.104, -0.043] | \n", + "[-1.500, 0.350] | \n", + "0.052 | \n", + "0.030 | \n", + "0.033 | \n", + "0.011 | \n", + "
| Partially linear | \n", + "-0.844 (0.251) | \n", + "[-1.299, -0.390] | \n", + "[-1.712, 0.022] | \n", + "0.043 | \n", + "0.017 | \n", + "0.055 | \n", + "0.029 | \n", + "
| Model | Short ATE | Bias bound | ATE bounds | 95% conf. bounds |
|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> |
| 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] |
| gain.Y | gain.D | rho | |
|---|---|---|---|
| inc | 0.1366 | 0.0502 | -0.3372 |
| pira | 0.0487 | 0.0081 | -0.1317 |
| twoearn | 0.0399 | 0.0112 | 0.2589 |
| gain.Y | gain.D | rho | |
|---|---|---|---|
| inc | 0.1315 | 0.1288 | -0.218 |
| pira | 0.0267 | 0.0000 | 0.000 |
| twoearn | 0.0113 | 0.0000 | 0.000 |
| q=1 | q=2 | q=3 | q=4 | q=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] |
| q=1 | q=2 | q=3 | q=4 | q=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] |
| point | Bounds | CIBounds | RV(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] | 0.087 | 0.037 | 0.025 | 0.000 |
| Partially linear | -0.406 (0.465) | [-0.848, 0.035] | [-1.647, 0.769] | 0.073 | 0.021 | 0.028 | 0.000 |