From 7b7d5a6426c6615cd346594abc05f5ab66fcbe56 Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Thu, 1 Aug 2024 16:34:26 -0700 Subject: [PATCH 1/2] add method to compute ATEs using pseudo-outcomes --- CHANGELOG.rst | 9 + docs/examples/example_estimating_ates.ipynb | 558 ++++++++++++++++++++ metalearners/drlearner.py | 29 + 3 files changed, 596 insertions(+) create mode 100644 docs/examples/example_estimating_ates.ipynb diff --git a/CHANGELOG.rst b/CHANGELOG.rst index bd987b68..53378c20 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -7,6 +7,15 @@ Changelog ========= +0.9.1 (2024-07-xx) +------------------ + +**New features** + +* Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to compute AIPW point estimate and standard error without requiring a full model fit. + + + 0.9.0 (2024-07-xx) ------------------ diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb new file mode 100644 index 00000000..ac8d8c7e --- /dev/null +++ b/docs/examples/example_estimating_ates.ipynb @@ -0,0 +1,558 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(example-ate)=\n", + "\n", + " Example: Estimating Average Treatment Effects\n", + "=============================\n", + "\n", + "Motivation\n", + "----------\n", + "\n", + "Estimating average treatment effects (ATEs) involves a subset of the tasks involved in estimating Conditional Average Treatment Effects (CATEs), so we can use methods that are designed for estimating CATEs to estimate ATEs. In this example, we simulate some data with confounding and demonstrate the `treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`).\n", + "\n", + "Example\n", + "-------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pyfixest as pf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DGP with confounding" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# generate covariate matrix with mixture of continuous and categorical variables\n", + "np.random.seed(123)\n", + "\n", + "\n", + "def dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", + " \"\"\"DGP for a confounded treatment assignment dgp\n", + "\n", + " Args:\n", + " n (int): sample size\n", + " k (int): number of continuous covariates\n", + " pscore_fn (lambda): propensity score function\n", + " tau_fn (lambda): treatment effect function. Can be scalar for constant effect.\n", + " outcome_fn (lambda): outcome DGP\n", + "\n", + " Returns:\n", + " _type_: _description_\n", + " \"\"\"\n", + " Sigma = np.random.uniform(-1, 1, (k, k))\n", + " Sigma = Sigma @ Sigma.T\n", + " Xnum = np.random.multivariate_normal(np.zeros(k), Sigma, n)\n", + " # generate categorical variables\n", + " Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n", + " X = np.c_[Xnum, Xcat]\n", + " W = np.random.binomial(1, pscore_fn(X), n)\n", + " Y = outcome_fn(X, W, tau_fn)\n", + " df = pd.DataFrame(\n", + " np.c_[Y, W, X], columns=[\"Y\", \"W\"] + [f\"X{i}\" for i in range(k + 1)]\n", + " )\n", + " return df\n", + "\n", + "\n", + "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", + "\n", + "\n", + "# tau_fn = lambda x: 1 + 2 * x[:, 0] + 3 * x[:, 1] + 4 * x[:, 2] + 5 * x[:, 3]\n", + "def outcome_fn(x, w, taufn):\n", + " return (\n", + " taufn(x) * w\n", + " + x[:, 0]\n", + " + 2 * x[:, 1] ** 2\n", + " + 3 * x[:, 3] * x[:, 1]\n", + " + x[:, 2]\n", + " + x[:, 3]\n", + " + np.random.normal(0, 1, n)\n", + " )\n", + "\n", + "\n", + "n, k = 10_000, 3\n", + "df = dgp(n, k, pscore_fn, tau_fn=lambda x: 1, outcome_fn=outcome_fn)\n", + "outcome_column, treatment_column = \"Y\", \"W\"\n", + "feature_columns = [f\"X{i}\" for i in range(k + 1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Regression (`pyfixest`)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.62494388 0.0468862 ]\n" + ] + } + ], + "source": [ + "print(\n", + " naive_est := pf.feols(f\"{outcome_column} ~ {treatment_column}\", df)\n", + " .tidy()\n", + " .query(\"Coefficient == 'W'\")\n", + " .iloc[0, :2]\n", + " .values\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.132635 0.02973861]\n" + ] + } + ], + "source": [ + "print(\n", + " linreg_est := pf.feols(\n", + " f\"{outcome_column} ~ {treatment_column}+{'+'.join(feature_columns)}\", df\n", + " )\n", + " .tidy()\n", + " .query(\"Coefficient == 'W'\")\n", + " .iloc[0, :2].values\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear model is misspecified, so both the naive and conditional estimates are biased." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `metalearners`: `DRLearner`\n", + "\n", + "Point estimates and standard errors for treatment effects for the AIPW estimator can be computed by aggregating the pseudo-outcome computed by the `DRLearner` class." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from metalearners import DRLearner\n", + "from lightgbm import LGBMRegressor, LGBMClassifier\n", + "from sklearn.dummy import DummyRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.01467296, 0.03699228]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metalearners_dr = DRLearner(\n", + " nuisance_model_factory=LGBMRegressor,\n", + " treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n", + " propensity_model_factory=LGBMClassifier,\n", + " is_classification=False,\n", + " n_variants=2,\n", + " nuisance_model_params={\"verbose\": -1},\n", + " propensity_model_params={\"verbose\": -1},\n", + ")\n", + "\n", + "metalearners_dr.fit_all_nuisance(\n", + " X=df[feature_columns],\n", + " y=df[outcome_column],\n", + " w=df[treatment_column],\n", + ")\n", + "\n", + "metalearners_est = metalearners_dr.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + " X=df[feature_columns],\n", + " w=df[treatment_column],\n", + " y=df[outcome_column],\n", + ")\n", + "metalearners_est" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Manual computation with pseudo outcome method produces the same estimate (`treatment_effect` does a generalisation of this under the hood) yields the same estimate" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "est: 1.0146729600419584, se: 0.036994131087587\n" + ] + } + ], + "source": [ + "gamma_i = metalearners_dr._pseudo_outcome(\n", + " X=df[feature_columns],\n", + " w=df[treatment_column],\n", + " y=df[outcome_column],\n", + " treatment_variant=1,\n", + " is_oos=False,\n", + ")\n", + "gamma_i.mean(), gamma_i.std()/np.sqrt(n)\n", + "est, se = gamma_i.mean(), gamma_i.std()/np.sqrt(n)\n", + "print(f\"est: {est}, se: {se}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `doubleml`: `DoubleMLIRM`\n", + "\n", + "The `doubleML` library focuses on estimating average effects and has an 'interactive regression model (IRM)' class that estimates the ATE using the same pseudo-outcome method as the `DRLearner` class." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "from doubleml import DoubleMLIRM, DoubleMLData\n", + "dml_data = DoubleMLData(\n", + " df,\n", + " x_cols=feature_columns,\n", + " y_col=outcome_column,\n", + " d_cols=treatment_column,\n", + ")\n", + "\n", + "aipw_mod = DoubleMLIRM(\n", + " dml_data,\n", + " ml_g = LGBMRegressor(),\n", + " ml_m = LGBMClassifier(),\n", + " n_folds=5,\n", + ")\n", + "\n", + "aipw_mod.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.01366331 0.03880938]\n" + ] + } + ], + "source": [ + "print(doubleml_est := aipw_mod.summary.values[0, :2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `econML`: `LinearDRLearner`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from econml.dr import LinearDRLearner\n", + "import formulaic as fm" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Y ~ 0 + X0+X1+X2+X3\n" + ] + } + ], + "source": [ + "print(ff := f\"{outcome_column} ~ 0 + {'+'.join(feature_columns)}\")\n", + "y, X = fm.Formula(ff).get_model_matrix(df, output=\"numpy\")\n", + "W = df[treatment_column].values[:, np.newaxis]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "econml_dr = LinearDRLearner(model_regression=LGBMRegressor(), model_propensity=LGBMClassifier())\n", + "econml_dr.fit(y, T=W, W=X)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.069 0.059]\n" + ] + } + ], + "source": [ + "print(econml_est := econml_dr.intercept__inference(1).summary_frame().iloc[0, :2].values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## comparison\n", + "\n", + "All ml-based estimators yield comparable results." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " naive linreg metalearners doubleml econml\n", + "est 1.624944 1.132635 1.014673 1.013663 1.069\n", + "0.036994 0.046886 0.029739 0.036992 0.038809 0.059" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(\n", + " np.c_[\n", + " naive_est,\n", + " linreg_est,\n", + " metalearners_est.flatten(),\n", + " doubleml_est,\n", + " econml_est,\n", + "], index = ['est', se],\n", + "columns = ['naive', 'linreg', 'metalearners', 'doubleml', 'econml']\n", + ")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index 1a29487e..ae5f7c40 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -331,6 +331,35 @@ def evaluate( return variant_outcome_evaluation | propensity_evaluation | treatment_evaluation + def treatment_effect( + self, + X: Matrix, + y: Vector, + w: Vector, + ) -> np.ndarray: + """Compute Average Treatment Effect (ATE) for each treatment variant using the Augmented IPW estimator (Robins et al 1994). Does not require fitting a second-stage treatment model: it uses the pseudo-outcome alone and computes the average and SE. Can be used following the `fit_all_nuisance` method. + + Args: + X (Matrix): Covariate matrix. + y (Vector): Outcome vector. + w (Vector): Treatment vector + + Returns: + np.ndarray: Treatment effect and standard error for each treatment variant. + """ + gamma_matrix = np.zeros((len(X), self.n_variants - 1)) + for treatment_variant in range(1, self.n_variants): + gamma_matrix[:, treatment_variant - 1] = self._pseudo_outcome( + X=X, + w=w, + y=y, + treatment_variant=treatment_variant, + is_oos=False, + ) + treatment_effect = gamma_matrix.mean(axis=0) + standard_error = gamma_matrix.std(axis=0) / np.sqrt(len(X)) + return np.c_[treatment_effect, standard_error] + def _pseudo_outcome( self, X: Matrix, From 0fe24a60dae96ca97b191b1d5b1d965f3e021dcd Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Thu, 1 Aug 2024 17:39:55 -0700 Subject: [PATCH 2/2] add deps for doc build --- docs/examples/example_estimating_ates.ipynb | 178 +- pixi.lock | 3073 ++++++++++++++++--- pixi.toml | 3 + 3 files changed, 2752 insertions(+), 502 deletions(-) diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index ac8d8c7e..e6539188 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -22,92 +22,11 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", - "import pyfixest as pf" + "import statsmodels.formula.api as smf" ] }, { @@ -180,7 +99,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Linear Regression (`pyfixest`)" + "## Linear Regression " ] }, { @@ -189,21 +108,20 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.62494388 0.0468862 ]\n" - ] + "data": { + "text/plain": [ + "(1.624943884109307, 0.04532725031682251)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(\n", - " naive_est := pf.feols(f\"{outcome_column} ~ {treatment_column}\", df)\n", - " .tidy()\n", - " .query(\"Coefficient == 'W'\")\n", - " .iloc[0, :2]\n", - " .values\n", - ")" + "naive_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}\", df) .fit(cov_type=\"HC1\")\n", + "naive_est = naive_lm.params.iloc[1], naive_lm.bse.iloc[1]\n", + "naive_est" ] }, { @@ -212,22 +130,21 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.132635 0.02973861]\n" - ] + "data": { + "text/plain": [ + "(1.1326349969274776, 0.02972906033475406)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(\n", - " linreg_est := pf.feols(\n", - " f\"{outcome_column} ~ {treatment_column}+{'+'.join(feature_columns)}\", df\n", - " )\n", - " .tidy()\n", - " .query(\"Coefficient == 'W'\")\n", - " .iloc[0, :2].values\n", - ")" + "covaradjust_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}+{'+'.join(feature_columns)}\",\n", + " df) .fit(cov_type=\"HC1\")\n", + "linreg_est = covaradjust_lm.params.iloc[1], covaradjust_lm.bse.iloc[1]\n", + "linreg_est" ] }, { @@ -250,7 +167,40 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from metalearners import DRLearner\n", "from lightgbm import LGBMRegressor, LGBMClassifier\n", @@ -499,8 +449,8 @@ " \n", " \n", " 0.036994\n", - " 0.046886\n", - " 0.029739\n", + " 0.045327\n", + " 0.029729\n", " 0.036992\n", " 0.038809\n", " 0.059\n", @@ -512,7 +462,7 @@ "text/plain": [ " naive linreg metalearners doubleml econml\n", "est 1.624944 1.132635 1.014673 1.013663 1.069\n", - "0.036994 0.046886 0.029739 0.036992 0.038809 0.059" + "0.036994 0.045327 0.029729 0.036992 0.038809 0.059" ] }, "execution_count": 14, @@ -550,7 +500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/pixi.lock b/pixi.lock index 63a6f428..8baa1d0e 100644 --- a/pixi.lock +++ b/pixi.lock @@ -2315,6 +2315,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/astor-0.8.1-pyh9f0ad1d_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-2.4.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-23.2.0-pyh71513ae_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/autograd-1.6.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/autograd-gamma-0.5.0-pyh9f0ad1d_0.tar.bz2 @@ -2324,52 +2325,73 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-hef167b5_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h30efb56_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py311hb755f60_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.0-h6d6b9e4_1.conda - 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