From 7b7d5a6426c6615cd346594abc05f5ab66fcbe56 Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Thu, 1 Aug 2024 16:34:26 -0700 Subject: [PATCH 01/13] 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 02/13] 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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__glibc >=2.17,<3.0.a0 - cffi >=1.11 - - python >=3.12,<3.13.0a0 - - python_abi 3.12.* *_cp312 - - ucrt >=10.0.20348.0 - - vc >=14.2,<15 - - vc14_runtime >=14.29.30139 + - libgcc-ng >=12 + - python >=3.11,<3.12.0a0 + - python_abi 3.11.* *_cp311 - zstd >=1.5.6,<1.5.7.0a0 - zstd >=1.5.6,<1.6.0a0 license: BSD-3-Clause license_family: BSD - size: 324422 - timestamp: 1718867030240 + size: 416323 + timestamp: 1721044178290 - kind: conda name: zstd version: 1.5.6 diff --git a/pixi.toml b/pixi.toml index a23b176f..e4c49326 100644 --- a/pixi.toml +++ b/pixi.toml @@ -59,6 +59,9 @@ sphinx_rtd_theme = "*" sphinxcontrib-apidoc = "*" sphinx-autodoc-typehints = "*" myst-nb = "*" +econml = ">=0.15" +doubleml = "*" +statsmodels = "*" [feature.docs.tasks] docs = "cd docs && make html " From 246f7e8a25a732b86d7a51b353a3a19a55d0a517 Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Mon, 5 Aug 2024 16:27:45 -0700 Subject: [PATCH 03/13] address review comments --- CHANGELOG.rst | 10 +- docs/examples/example_estimating_ates.ipynb | 559 +- docs/examples/index.rst | 1 + metalearners/drlearner.py | 12 +- pixi.lock | 7922 ++++++++----------- tests/test_drlearner.py | 20 + 6 files changed, 3948 insertions(+), 4576 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 7f30b505..a89a0839 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -7,15 +7,6 @@ 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) ------------------ @@ -25,6 +16,7 @@ Changelog * Add :class:`metalearners.utils.FixedBinaryPropensity`. +* Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to compute AIPW point estimate and standard error for _average treatment effects (ATE)_ without requiring a full model fit (which is required for CATE estimation). A new notebook contains examples. 0.8.0 (2024-07-22) ------------------ diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index e6539188..4a2c93a9 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -12,7 +12,9 @@ "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", + "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. \n", + "\n", + "In this example, we simulate some data with a confounded binary treatment 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`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n", "\n", "Example\n", "-------" @@ -26,14 +28,16 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "import statsmodels.formula.api as smf" + "import statsmodels.formula.api as smf\n", + "\n", + "np.random.seed(42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# DGP with confounding" + "## Binary Treatment with confounding" ] }, { @@ -42,11 +46,7 @@ "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", + "def binary_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", " \"\"\"DGP for a confounded treatment assignment dgp\n", "\n", " Args:\n", @@ -55,9 +55,6 @@ " 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", @@ -73,10 +70,6 @@ " 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", @@ -90,7 +83,11 @@ "\n", "\n", "n, k = 10_000, 3\n", - "df = dgp(n, k, pscore_fn, tau_fn=lambda x: 1, outcome_fn=outcome_fn)\n", + "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", + "# simulate constant_effects\n", + "# tau_fn = lambda x: 1 + 2 * x[:, 0] + 3 * x[:, 1] + 4 * x[:, 2] + 5 * x[:, 3]\n", + "tau_fn = lambda x: 1\n", + "df = binary_dgp(n, k, pscore_fn, tau_fn=tau_fn, outcome_fn=outcome_fn)\n", "outcome_column, treatment_column = \"Y\", \"W\"\n", "feature_columns = [f\"X{i}\" for i in range(k + 1)]" ] @@ -99,7 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Linear Regression " + "### Linear Regression " ] }, { @@ -110,7 +107,7 @@ { "data": { "text/plain": [ - "(1.624943884109307, 0.04532725031682251)" + "(2.083595103597918, 0.06526671583747883)" ] }, "execution_count": 3, @@ -132,7 +129,7 @@ { "data": { "text/plain": [ - "(1.1326349969274776, 0.02972906033475406)" + "(2.1433722387308025, 0.06345124983351998)" ] }, "execution_count": 4, @@ -158,7 +155,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `metalearners`: `DRLearner`\n", + "### `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." ] @@ -215,7 +212,7 @@ { "data": { "text/plain": [ - "array([[1.01467296, 0.03699228]])" + "array([[1.02931589, 0.06679633]])" ] }, "execution_count": 6, @@ -239,7 +236,6 @@ " 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", @@ -264,7 +260,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "est: 1.0146729600419584, se: 0.036994131087587\n" + "est: 1.0293158917468608, se: 0.06679966900982737\n" ] } ], @@ -285,9 +281,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `doubleml`: `DoubleMLIRM`\n", + "### `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." + "The [`doubleML`](https://docs.doubleml.org/stable/index.html) 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." ] }, { @@ -324,7 +320,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.01366331 0.03880938]\n" + "[1.08356716 0.05786543]\n" ] } ], @@ -336,7 +332,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `econML`: `LinearDRLearner`" + "### `econML`: `LinearDRLearner`" ] }, { @@ -388,7 +384,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.069 0.059]\n" + "[1.001 0.109]\n" ] } ], @@ -400,9 +396,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## comparison\n", + "### comparison\n", "\n", - "All ml-based estimators yield comparable results." + "All ml-based estimators yield comparable results (both point estimate and standard error)." ] }, { @@ -441,28 +437,28 @@ " \n", " \n", " est\n", - " 1.624944\n", - " 1.132635\n", - " 1.014673\n", - " 1.013663\n", - " 1.069\n", + " 2.083595\n", + " 2.143372\n", + " 1.029316\n", + " 1.083567\n", + " 1.001\n", " \n", " \n", - " 0.036994\n", - " 0.045327\n", - " 0.029729\n", - " 0.036992\n", - " 0.038809\n", - " 0.059\n", + " se\n", + " 0.065267\n", + " 0.063451\n", + " 0.066796\n", + " 0.057865\n", + " 0.109\n", " \n", " \n", "\n", "" ], "text/plain": [ - " naive linreg metalearners doubleml econml\n", - "est 1.624944 1.132635 1.014673 1.013663 1.069\n", - "0.036994 0.045327 0.029729 0.036992 0.038809 0.059" + " naive linreg metalearners doubleml econml\n", + "est 2.083595 2.143372 1.029316 1.083567 1.001\n", + "se 0.065267 0.063451 0.066796 0.057865 0.109" ] }, "execution_count": 14, @@ -478,10 +474,483 @@ " metalearners_est.flatten(),\n", " doubleml_est,\n", " econml_est,\n", - "], index = ['est', se],\n", + "], index = ['est', 'se'],\n", "columns = ['naive', 'linreg', 'metalearners', 'doubleml', 'econml']\n", ")\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple Treatment Arms\n", + "\n", + "Next, we demonstrate the use of the `treatment_effect` method of the `DRLearner` class in a setting with multiple discrete treatments." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Y X0 X1 X2 X3 W\n", + "0 8.514662 -0.104441 0.833485 1.802766 1.0 2\n", + "1 0.355338 -0.690561 -0.180011 -1.715710 0.0 2\n", + "2 2.033627 0.685568 -0.004838 0.671343 0.0 1\n", + "3 2.791453 1.984499 -0.433412 0.921716 0.0 1\n", + "4 2.080426 0.189912 -0.769235 -0.450760 1.0 1" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(123)\n", + "\n", + "def multi_pscore_fn(x, num_treatments=3):\n", + " logits = np.zeros((x.shape[0], num_treatments))\n", + " logits[:, 0] = -x[:, 0] - x[:, 1] - x[:, 2] + x[:, 3]\n", + " logits[:, 1] = -x[:, 1] - x[:, 2] - x[:, 3] + x[:, 0]\n", + " logits[:, 2] = -x[:, 2] - x[:, 3] - x[:, 0] + x[:, 1]\n", + " # Apply softmax\n", + " exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))\n", + " pscores = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n", + " return pscores\n", + "\n", + "# And then modify the multi_treatment_dgp function to use these probabilities:\n", + "\n", + "def multi_treatment_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1, num_treatments=3):\n", + " \"\"\"DGP for a confounded multiple treatment assignment\"\"\"\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", + " Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n", + " X = np.c_[Xnum, Xcat]\n", + "\n", + " # Generate multiple treatments\n", + " pscores = pscore_fn(X, num_treatments)\n", + " W = np.zeros((n, num_treatments), dtype=int)\n", + " for i in range(n):\n", + " W[i, np.random.choice(num_treatments, p=pscores[i])] = 1\n", + " Y = outcome_fn(X, W, tau_fn)\n", + " df = pd.DataFrame(\n", + " np.c_[Y, X],\n", + " columns=[\"Y\"]\n", + " + [f\"X{i}\" for i in range(k + 1)],\n", + " ).assign(\n", + " W=np.argmax(W, axis=1)\n", + " ) # convert one-hot encoding to single column\n", + " return df\n", + "\n", + "\n", + "def multi_outcome_fn(x, w, taufn):\n", + " return (\n", + " np.sum(taufn(x) * w, axis=1) # sum over treatments\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", + "def multi_tau_fn(x):\n", + " return np.c_[ 0, 1, 2]\n", + "\n", + "n, k = 10_000, 3\n", + "\n", + "df_multi = multi_treatment_dgp(\n", + " n, k, multi_pscore_fn, tau_fn=multi_tau_fn, outcome_fn=multi_outcome_fn\n", + ")\n", + "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n", + "outcome_column, treatment_column = \"Y\", \"W\"\n", + "df_multi.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 0.645659\n", + "C(W)[T.1] 1.777798\n", + "C(W)[T.2] 2.387949\n", + "dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm_multi = smf.ols(\"Y ~ C(W)\", df_multi).fit()\n", + "lm_multi.params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These estimates are substantially biased, since the model is misspecified (the true outcome and propensity score contains quadratic terms)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.04071166, 0.14139908],\n", + " [2.25658364, 0.21346275]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metalearners_dr_2 = 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=3,\n", + " nuisance_model_params={\"verbose\": -1},\n", + " propensity_model_params={\"verbose\": -1},\n", + ")\n", + "\n", + "metalearners_dr_2.fit_all_nuisance(\n", + " X=df_multi[feature_columns],\n", + " y=df_multi[outcome_column],\n", + " w=df_multi[treatment_column],\n", + ")\n", + "metalearners_est2 = metalearners_dr_2.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + " X=df_multi[feature_columns],\n", + " y=df_multi[outcome_column],\n", + " w=df_multi[treatment_column],\n", + ")\n", + "metalearners_est2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These estimates are less biased than those produced by the linear model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Binary Outcome\n", + "\n", + "Finally, we consider a case where the outcome is binary, in which case the DRLearner class is initialized with `is_classification=True` and the nuisance model is a classifier." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ [f\"X{i}\" for i in range(k + 1)]\n", + " ).assign(Y = pd.to_numeric(Y, downcast='integer'))\n", + " return df\n", + "\n", + "\n", + "def classification_outcome_fn(x, w, taufn):\n", + " logits = (\n", + " taufn(x) * w\n", + " + x[:, 0]\n", + " + 2 * x[:, 1]\n", + " + 3 * x[:, 3] * x[:, 1]\n", + " + x[:, 2]\n", + " + x[:, 3]\n", + " )\n", + " return 1 / (1 + np.exp(-logits))\n", + "\n", + "\n", + "# Propensity score function\n", + "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", + "\n", + "# Treatment effect function - constant increase in probability of Y=1\n", + "classification_tau_fn = lambda x: 0.05\n", + "\n", + "n, k = 10_000, 3\n", + "df_class = classification_dgp(\n", + " n, k, pscore_fn, tau_fn=classification_tau_fn, outcome_fn=classification_outcome_fn\n", + ")\n", + "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n", + "outcome_column, treatment_column = \"Y\", \"W\"\n", + "\n", + "df_class.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle 0.081358650748229$" + ], + "text/plain": [ + "0.08135865074822901" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# naive TE\n", + "df_class.groupby(\"W\")[\"Y\"].mean().diff()[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.04373356, 0.04191263]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metalearners_dr_3 = DRLearner(\n", + " nuisance_model_factory=LGBMClassifier,\n", + " treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n", + " propensity_model_factory=LGBMClassifier,\n", + " is_classification=True,\n", + " n_variants=2,\n", + " nuisance_model_params={\"verbose\": -1},\n", + " propensity_model_params={\"verbose\": -1},\n", + ")\n", + "\n", + "metalearners_dr_3.fit_all_nuisance(\n", + " X=df_class[feature_columns],\n", + " y=df_class[outcome_column],\n", + " w=df_class[treatment_column],\n", + ")\n", + "metalearners_est_3 = metalearners_dr_3.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + " X=df_class[feature_columns],\n", + " y=df_class[outcome_column],\n", + " w=df_class[treatment_column],\n", + ")\n", + "metalearners_est_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we have lower bias than the naive comparison." + ] } ], "metadata": { diff --git a/docs/examples/index.rst b/docs/examples/index.rst index ad950311..2ef14398 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -6,6 +6,7 @@ Examples :maxdepth: 1 Estimating CATEs with MetaLearners + Estimating ATEs with DRLearner Reusing base models Explainability: Lime plots of MetaLearners Explainability: Feature importance and SHAP values diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index ae5f7c40..18df8af8 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -191,7 +191,7 @@ def fit_all_nuisance( ) self._assign_joblib_nuisance_results(results) - + self._nuisance_models_fit = True return self def fit_all_treatment( @@ -337,7 +337,11 @@ def treatment_effect( 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. + """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 + :meth:`~metalearners.drlearner.DRLearner.fit_all_nuisance` method. Args: X (Matrix): Covariate matrix. @@ -347,6 +351,10 @@ def treatment_effect( Returns: np.ndarray: Treatment effect and standard error for each treatment variant. """ + if not self._nuisance_models_fit: + raise ValueError( + "The nuisance models need to be fitted before computing the treatment effect." + ) 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( diff --git a/pixi.lock b/pixi.lock index 8baa1d0e..7227f074 100644 --- a/pixi.lock +++ b/pixi.lock @@ -11,11 +11,11 @@ environments: - 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/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/bzip2-1.0.8-hd590300_5.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/causalml-0.15.1-py310h2bcf4ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.6.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py310hd41b1e2_0.conda @@ -23,7 +23,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-0.29.34-py310heca2aa9_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dill-0.3.8-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dowhy-0.7.1-pyhd8ed1ab_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/econml-0.15.0-py310hcc13569_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/econml-0.15.1-py310h27b3328_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.15.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 @@ -33,7 +33,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.0-py310hc51659f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.1-py310h5b4e0ec_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/forestci-0.6-pyha21a80b_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.10-h36c2ea0_0.tar.bz2 @@ -45,10 +45,10 @@ environments: - 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kind: conda name: xgboost - version: 2.0.3 - build: cpu_pyhb8f9a19_4 - build_number: 4 + version: 2.1.1 + build: cpu_pyhb8f9a19_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/xgboost-2.0.3-cpu_pyhb8f9a19_4.conda - sha256: 353a299a6758a748e286c7beda4b8d6640bb25062031862f3ae0c5dde590f1cb - md5: 6d841f6e43095e98843e9ee49bd7c700 + url: https://conda.anaconda.org/conda-forge/noarch/xgboost-2.1.1-cpu_pyhb8f9a19_1.conda + sha256: 636d0edde8e6aa59817c7742a8c8317c11e20e7bcf7d7c5e35d3a9df9ad09f11 + md5: bc3d2545931539e258c58deeb59043ca depends: - - py-xgboost 2.0.3 cpu_pyh995e691_4 - - python + - py-xgboost 2.1.1 cpu_pyh995e691_1 + - python >=3.8 license: Apache-2.0 license_family: APACHE - size: 16171 - timestamp: 1713400029804 + size: 14298 + timestamp: 1722455224921 - kind: conda name: xorg-kbproto version: 1.0.7 diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index 2bf611ad..1e8d3c5d 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -1,6 +1,7 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +import numpy as np from sklearn.linear_model import LinearRegression, LogisticRegression from metalearners.drlearner import DRLearner @@ -18,3 +19,22 @@ def test_adaptive_clipping_smoke(dummy_dataset): adaptive_clipping=True, ) ml.fit(X, y, w) + + +def test_treatment_effect( + numerical_experiment_dataset_continuous_outcome_binary_treatment_linear_te, +): + X, _, W, Y, _, tau = ( + numerical_experiment_dataset_continuous_outcome_binary_treatment_linear_te + ) + ml = DRLearner( + False, + 2, + LinearRegression, + LinearRegression, + LogisticRegression, + n_folds=2, + ) + ml.fit_all_nuisance(X, Y, W) + est = ml.treatment_effect(X, Y, W) + np.testing.assert_almost_equal(est[:, 0], tau.mean(), decimal=1) From 3b2777c101eb382593164e2db8401c3acca75fd8 Mon Sep 17 00:00:00 2001 From: Kevin Klein <7267523+kklein@users.noreply.github.com> Date: Fri, 2 Aug 2024 10:35:12 +0200 Subject: [PATCH 04/13] Move fixed propensity model to utils. (#72) * Move fixed propensity model to utils. * Add changelog entry. * Add tests. * Fix typo. * Fix class reference. --- CHANGELOG.rst | 6 ++- docs/examples/example_propensity.ipynb | 66 ++++++++++---------------- metalearners/utils.py | 32 +++++++++++++ tests/test_utils.py | 61 +++++++++++++++++++++++- 4 files changed, 120 insertions(+), 45 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 53378c20..7f30b505 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -21,7 +21,9 @@ Changelog **New features** -* Added :meth:`metalearners.metalearner.MetaLearner.init_params`. +* Add :meth:`metalearners.metalearner.MetaLearner.init_params`. + +* Add :class:`metalearners.utils.FixedBinaryPropensity`. 0.8.0 (2024-07-22) @@ -29,7 +31,7 @@ Changelog **New features** -* Added :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and +* Add :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and :meth:`metalearners.metalearner.MetaLearner.fit_all_treatment`. * Add optional ``store_raw_results`` and ``store_results`` parameters to :class:`metalearners.grid_search.MetaLearnerGridSearch`. diff --git a/docs/examples/example_propensity.ipynb b/docs/examples/example_propensity.ipynb index 3a356351..7fc280f8 100644 --- a/docs/examples/example_propensity.ipynb +++ b/docs/examples/example_propensity.ipynb @@ -59,8 +59,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Creating our own estimator\n", - "--------------------------\n", + "Using a dummy estimator\n", + "-----------------------\n", "\n", "In this tutorial we will assume that we know that all observations were assigned to the\n", "treatment with a fixed probability of 0.3, which is close to the fraction of the observations\n", @@ -89,43 +89,10 @@ "dataset, we just use it for illustrational purposes.\n", "```\n", "\n", - "Now we can define our custom ``sklearn``-like classifier. We recommend inheriting from\n", - "the ``sklearn`` base classes and following the rules explained in the\n", - "[sklearn documentation](https://scikit-learn.org/stable/developers/develop.html) to avoid\n", - "having to define helper functions and ensure the correct functionality of the ``metalearners``\n", - "library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "outputs": [], - "source": [ - "from sklearn.base import BaseEstimator, ClassifierMixin\n", - "from typing import Any\n", - "from typing_extensions import Self\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "\n", - "class FixedPropensityModel(ClassifierMixin, BaseEstimator):\n", - " def __init__(self, propensity_score: float) -> None:\n", - " self.propensity_score = propensity_score\n", - "\n", - " def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:\n", - " self.classes_ = np.unique(y.to_numpy()) # sklearn requires this\n", - " return self\n", - "\n", - " def predict(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n", - " return np.argmax(self.predict_proba(X), axis=1)\n", - "\n", - " def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n", - " return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score])" + "Now we can use a custom ``sklearn``-like classifier: {class}`~metalearners.utils.FixedBinaryPropensity`.\n", + "The latter can be used like any ``sklearn`` classifier but will always return the same propensity,\n", + "independently of the observed covariates. This propensity has to be provided at initialization via the\n", + "``propensity_score`` parameter." ] }, { @@ -149,11 +116,12 @@ "outputs": [], "source": [ "from metalearners import RLearner\n", + "from metalearners.utils import FixedBinaryPropensity\n", "from lightgbm import LGBMRegressor\n", "\n", "rlearner = RLearner(\n", " nuisance_model_factory=LGBMRegressor,\n", - " propensity_model_factory=FixedPropensityModel,\n", + " propensity_model_factory=FixedBinaryPropensity,\n", " treatment_model_factory=LGBMRegressor,\n", " nuisance_model_params={\"verbose\": -1},\n", " propensity_model_params={\"propensity_score\": 0.3},\n", @@ -205,10 +173,24 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/metalearners/utils.py b/metalearners/utils.py index 765e6c1d..587bef67 100644 --- a/metalearners/utils.py +++ b/metalearners/utils.py @@ -1,8 +1,14 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +from typing import Any + import numpy as np +import pandas as pd +from sklearn.base import BaseEstimator, ClassifierMixin +from typing_extensions import Self +from metalearners._typing import Matrix, Vector from metalearners.drlearner import DRLearner from metalearners.metalearner import MetaLearner from metalearners.rlearner import RLearner @@ -73,3 +79,29 @@ def simplify_output(tensor: np.ndarray) -> np.ndarray: if n_outputs == 2: return tensor[:, :, 1].reshape(n_obs, n_variants) return tensor + + +class FixedBinaryPropensity(ClassifierMixin, BaseEstimator): + """Binary classifier propensity dummy model which outputs a fixed propensity, + independently of covariates.""" + + def __init__(self, propensity_score: float) -> None: + if not 0 <= propensity_score <= 1: + raise ValueError( + f"Expected a propensity score between 0 and 1 but got {propensity_score}." + ) + self.propensity_score = propensity_score + + def fit(self, X: Matrix, y: Vector) -> Self: + self.classes_ = np.unique(y) # sklearn requires this + if (n_classes := len(self.classes_)) > 2: + raise ValueError( + f"FixedBinaryPropensityModel only supports binary outcomes but {n_classes} were provided ." + ) + return self + + def predict(self, X: Matrix) -> np.ndarray[Any, Any]: + return np.argmax(self.predict_proba(X), axis=1) + + def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]: + return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score]) diff --git a/tests/test_utils.py b/tests/test_utils.py index c3f334ec..d88b5d14 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,11 +2,16 @@ # SPDX-License-Identifier: BSD-3-Clause import numpy as np +import pandas as pd import pytest from lightgbm import LGBMRegressor from metalearners.metalearner import MetaLearner -from metalearners.utils import metalearner_factory, simplify_output +from metalearners.utils import ( + FixedBinaryPropensity, + metalearner_factory, + simplify_output, +) @pytest.mark.parametrize("prefix", ["T"]) @@ -52,3 +57,57 @@ def test_simplify_output(input, expected): def test_simplify_output_raises(input): with pytest.raises(ValueError, match="needs to be 3-dimensional"): simplify_output(input) + + +@pytest.mark.parametrize("use_pd", [True, False]) +def test_fixed_binary_propensity(use_pd): + propensity_score = 0.3 + dominant_class = propensity_score >= 0.5 + + model = FixedBinaryPropensity(propensity_score=propensity_score) + + n_samples = 5 + X_train = np.ones((n_samples, 5)) + y_train = np.ones(n_samples) + if use_pd: + X_train = pd.DataFrame(X_train) + y_train = pd.Series(y_train) + + model.fit(X_train, y_train) + + n_test_samples = 3 + X_test = np.zeros(n_test_samples) + + class_predictions = model.predict(X_test) + assert np.array_equal( + class_predictions, np.array(np.ones(n_test_samples) * dominant_class) + ) + + probability_estimates = model.predict_proba(X_test) + assert np.array_equal( + probability_estimates, + np.column_stack( + ( + np.ones(n_test_samples) * (1 - propensity_score), + np.ones(n_test_samples) * propensity_score, + ) + ), + ) + + +@pytest.mark.parametrize("propensity_score", [-1, 100, 1.1]) +def test_fixed_binary_propensity_not_a_propbability(propensity_score): + with pytest.raises(ValueError, match="between 0 and 1 but got"): + FixedBinaryPropensity(propensity_score=propensity_score) + + +def test_fixed_binary_propensity_non_binary(): + propensity_score = 0.3 + + model = FixedBinaryPropensity(propensity_score=propensity_score) + + n_samples = 5 + X_train = np.ones((n_samples, 5)) + y_train = np.fromiter(range(n_samples), dtype=int) + with pytest.raises(ValueError, match="only supports binary outcomes"): + model.fit(X_train, y_train) From fbbe76dd86e373f48e75d2c8695d6bcbe9427d29 Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Mon, 5 Aug 2024 16:27:45 -0700 Subject: [PATCH 05/13] address review comments --- CHANGELOG.rst | 10 +- docs/examples/example_estimating_ates.ipynb | 559 +- docs/examples/index.rst | 1 + metalearners/drlearner.py | 12 +- pixi.lock | 7922 ++++++++----------- tests/test_drlearner.py | 20 + 6 files changed, 3948 insertions(+), 4576 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 7f30b505..a89a0839 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -7,15 +7,6 @@ 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) ------------------ @@ -25,6 +16,7 @@ Changelog * Add :class:`metalearners.utils.FixedBinaryPropensity`. +* Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to compute AIPW point estimate and standard error for _average treatment effects (ATE)_ without requiring a full model fit (which is required for CATE estimation). A new notebook contains examples. 0.8.0 (2024-07-22) ------------------ diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index e6539188..4a2c93a9 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -12,7 +12,9 @@ "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", + "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. \n", + "\n", + "In this example, we simulate some data with a confounded binary treatment 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`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n", "\n", "Example\n", "-------" @@ -26,14 +28,16 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "import statsmodels.formula.api as smf" + "import statsmodels.formula.api as smf\n", + "\n", + "np.random.seed(42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# DGP with confounding" + "## Binary Treatment with confounding" ] }, { @@ -42,11 +46,7 @@ "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", + "def binary_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", " \"\"\"DGP for a confounded treatment assignment dgp\n", "\n", " Args:\n", @@ -55,9 +55,6 @@ " 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", @@ -73,10 +70,6 @@ " 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", @@ -90,7 +83,11 @@ "\n", "\n", "n, k = 10_000, 3\n", - "df = dgp(n, k, pscore_fn, tau_fn=lambda x: 1, outcome_fn=outcome_fn)\n", + "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", + "# simulate constant_effects\n", + "# tau_fn = lambda x: 1 + 2 * x[:, 0] + 3 * x[:, 1] + 4 * x[:, 2] + 5 * x[:, 3]\n", + "tau_fn = lambda x: 1\n", + "df = binary_dgp(n, k, pscore_fn, tau_fn=tau_fn, outcome_fn=outcome_fn)\n", "outcome_column, treatment_column = \"Y\", \"W\"\n", "feature_columns = [f\"X{i}\" for i in range(k + 1)]" ] @@ -99,7 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Linear Regression " + "### Linear Regression " ] }, { @@ -110,7 +107,7 @@ { "data": { "text/plain": [ - "(1.624943884109307, 0.04532725031682251)" + "(2.083595103597918, 0.06526671583747883)" ] }, "execution_count": 3, @@ -132,7 +129,7 @@ { "data": { "text/plain": [ - "(1.1326349969274776, 0.02972906033475406)" + "(2.1433722387308025, 0.06345124983351998)" ] }, "execution_count": 4, @@ -158,7 +155,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `metalearners`: `DRLearner`\n", + "### `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." ] @@ -215,7 +212,7 @@ { "data": { "text/plain": [ - "array([[1.01467296, 0.03699228]])" + "array([[1.02931589, 0.06679633]])" ] }, "execution_count": 6, @@ -239,7 +236,6 @@ " 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", @@ -264,7 +260,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "est: 1.0146729600419584, se: 0.036994131087587\n" + "est: 1.0293158917468608, se: 0.06679966900982737\n" ] } ], @@ -285,9 +281,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `doubleml`: `DoubleMLIRM`\n", + "### `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." + "The [`doubleML`](https://docs.doubleml.org/stable/index.html) 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." ] }, { @@ -324,7 +320,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.01366331 0.03880938]\n" + "[1.08356716 0.05786543]\n" ] } ], @@ -336,7 +332,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `econML`: `LinearDRLearner`" + "### `econML`: `LinearDRLearner`" ] }, { @@ -388,7 +384,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.069 0.059]\n" + "[1.001 0.109]\n" ] } ], @@ -400,9 +396,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## comparison\n", + "### comparison\n", "\n", - "All ml-based estimators yield comparable results." + "All ml-based estimators yield comparable results (both point estimate and standard error)." ] }, { @@ -441,28 +437,28 @@ " \n", " \n", " est\n", - " 1.624944\n", - " 1.132635\n", - " 1.014673\n", - " 1.013663\n", - " 1.069\n", + " 2.083595\n", + " 2.143372\n", + " 1.029316\n", + " 1.083567\n", + " 1.001\n", " \n", " \n", - " 0.036994\n", - " 0.045327\n", - " 0.029729\n", - " 0.036992\n", - " 0.038809\n", - " 0.059\n", + " se\n", + " 0.065267\n", + " 0.063451\n", + " 0.066796\n", + " 0.057865\n", + " 0.109\n", " \n", " \n", "\n", "" ], "text/plain": [ - " naive linreg metalearners doubleml econml\n", - "est 1.624944 1.132635 1.014673 1.013663 1.069\n", - "0.036994 0.045327 0.029729 0.036992 0.038809 0.059" + " naive linreg metalearners doubleml econml\n", + "est 2.083595 2.143372 1.029316 1.083567 1.001\n", + "se 0.065267 0.063451 0.066796 0.057865 0.109" ] }, "execution_count": 14, @@ -478,10 +474,483 @@ " metalearners_est.flatten(),\n", " doubleml_est,\n", " econml_est,\n", - "], index = ['est', se],\n", + "], index = ['est', 'se'],\n", "columns = ['naive', 'linreg', 'metalearners', 'doubleml', 'econml']\n", ")\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple Treatment Arms\n", + "\n", + "Next, we demonstrate the use of the `treatment_effect` method of the `DRLearner` class in a setting with multiple discrete treatments." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Y X0 X1 X2 X3 W\n", + "0 8.514662 -0.104441 0.833485 1.802766 1.0 2\n", + "1 0.355338 -0.690561 -0.180011 -1.715710 0.0 2\n", + "2 2.033627 0.685568 -0.004838 0.671343 0.0 1\n", + "3 2.791453 1.984499 -0.433412 0.921716 0.0 1\n", + "4 2.080426 0.189912 -0.769235 -0.450760 1.0 1" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(123)\n", + "\n", + "def multi_pscore_fn(x, num_treatments=3):\n", + " logits = np.zeros((x.shape[0], num_treatments))\n", + " logits[:, 0] = -x[:, 0] - x[:, 1] - x[:, 2] + x[:, 3]\n", + " logits[:, 1] = -x[:, 1] - x[:, 2] - x[:, 3] + x[:, 0]\n", + " logits[:, 2] = -x[:, 2] - x[:, 3] - x[:, 0] + x[:, 1]\n", + " # Apply softmax\n", + " exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))\n", + " pscores = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n", + " return pscores\n", + "\n", + "# And then modify the multi_treatment_dgp function to use these probabilities:\n", + "\n", + "def multi_treatment_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1, num_treatments=3):\n", + " \"\"\"DGP for a confounded multiple treatment assignment\"\"\"\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", + " Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n", + " X = np.c_[Xnum, Xcat]\n", + "\n", + " # Generate multiple treatments\n", + " pscores = pscore_fn(X, num_treatments)\n", + " W = np.zeros((n, num_treatments), dtype=int)\n", + " for i in range(n):\n", + " W[i, np.random.choice(num_treatments, p=pscores[i])] = 1\n", + " Y = outcome_fn(X, W, tau_fn)\n", + " df = pd.DataFrame(\n", + " np.c_[Y, X],\n", + " columns=[\"Y\"]\n", + " + [f\"X{i}\" for i in range(k + 1)],\n", + " ).assign(\n", + " W=np.argmax(W, axis=1)\n", + " ) # convert one-hot encoding to single column\n", + " return df\n", + "\n", + "\n", + "def multi_outcome_fn(x, w, taufn):\n", + " return (\n", + " np.sum(taufn(x) * w, axis=1) # sum over treatments\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", + "def multi_tau_fn(x):\n", + " return np.c_[ 0, 1, 2]\n", + "\n", + "n, k = 10_000, 3\n", + "\n", + "df_multi = multi_treatment_dgp(\n", + " n, k, multi_pscore_fn, tau_fn=multi_tau_fn, outcome_fn=multi_outcome_fn\n", + ")\n", + "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n", + "outcome_column, treatment_column = \"Y\", \"W\"\n", + "df_multi.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 0.645659\n", + "C(W)[T.1] 1.777798\n", + "C(W)[T.2] 2.387949\n", + "dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm_multi = smf.ols(\"Y ~ C(W)\", df_multi).fit()\n", + "lm_multi.params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These estimates are substantially biased, since the model is misspecified (the true outcome and propensity score contains quadratic terms)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.04071166, 0.14139908],\n", + " [2.25658364, 0.21346275]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metalearners_dr_2 = 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=3,\n", + " nuisance_model_params={\"verbose\": -1},\n", + " propensity_model_params={\"verbose\": -1},\n", + ")\n", + "\n", + "metalearners_dr_2.fit_all_nuisance(\n", + " X=df_multi[feature_columns],\n", + " y=df_multi[outcome_column],\n", + " w=df_multi[treatment_column],\n", + ")\n", + "metalearners_est2 = metalearners_dr_2.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + " X=df_multi[feature_columns],\n", + " y=df_multi[outcome_column],\n", + " w=df_multi[treatment_column],\n", + ")\n", + "metalearners_est2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These estimates are less biased than those produced by the linear model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Binary Outcome\n", + "\n", + "Finally, we consider a case where the outcome is binary, in which case the DRLearner class is initialized with `is_classification=True` and the nuisance model is a classifier." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Y W X0 X1 X2 X3\n", + "0 0 1.0 1.138203 -1.439008 -1.981465 0.0\n", + "1 1 1.0 0.297749 -0.092953 1.455780 0.0\n", + "2 1 1.0 1.150090 0.545041 0.799303 0.0\n", + "3 1 0.0 0.832256 -0.591107 -1.076526 0.0\n", + "4 0 1.0 1.559507 -1.018031 -1.137247 0.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def classification_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", + " \"\"\"DGP for a confounded treatment assignment with binary outcome\"\"\"\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", + " 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_prob = outcome_fn(X, W, tau_fn)\n", + " Y = np.random.binomial(1, Y_prob).astype(int)\n", + " df = pd.DataFrame(\n", + " np.c_[Y, W, X], columns=[\"Y\", \"W\"] + [f\"X{i}\" for i in range(k + 1)]\n", + " ).assign(Y = pd.to_numeric(Y, downcast='integer'))\n", + " return df\n", + "\n", + "\n", + "def classification_outcome_fn(x, w, taufn):\n", + " logits = (\n", + " taufn(x) * w\n", + " + x[:, 0]\n", + " + 2 * x[:, 1]\n", + " + 3 * x[:, 3] * x[:, 1]\n", + " + x[:, 2]\n", + " + x[:, 3]\n", + " )\n", + " return 1 / (1 + np.exp(-logits))\n", + "\n", + "\n", + "# Propensity score function\n", + "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", + "\n", + "# Treatment effect function - constant increase in probability of Y=1\n", + "classification_tau_fn = lambda x: 0.05\n", + "\n", + "n, k = 10_000, 3\n", + "df_class = classification_dgp(\n", + " n, k, pscore_fn, tau_fn=classification_tau_fn, outcome_fn=classification_outcome_fn\n", + ")\n", + "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n", + "outcome_column, treatment_column = \"Y\", \"W\"\n", + "\n", + "df_class.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle 0.081358650748229$" + ], + "text/plain": [ + "0.08135865074822901" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# naive TE\n", + "df_class.groupby(\"W\")[\"Y\"].mean().diff()[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.04373356, 0.04191263]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metalearners_dr_3 = DRLearner(\n", + " nuisance_model_factory=LGBMClassifier,\n", + " treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n", + " propensity_model_factory=LGBMClassifier,\n", + " is_classification=True,\n", + " n_variants=2,\n", + " nuisance_model_params={\"verbose\": -1},\n", + " propensity_model_params={\"verbose\": -1},\n", + ")\n", + "\n", + "metalearners_dr_3.fit_all_nuisance(\n", + " X=df_class[feature_columns],\n", + " y=df_class[outcome_column],\n", + " w=df_class[treatment_column],\n", + ")\n", + "metalearners_est_3 = metalearners_dr_3.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + " X=df_class[feature_columns],\n", + " y=df_class[outcome_column],\n", + " w=df_class[treatment_column],\n", + ")\n", + "metalearners_est_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we have lower bias than the naive comparison." + ] } ], "metadata": { diff --git a/docs/examples/index.rst b/docs/examples/index.rst index ad950311..2ef14398 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -6,6 +6,7 @@ Examples :maxdepth: 1 Estimating CATEs with MetaLearners + Estimating ATEs with DRLearner Reusing base models Explainability: Lime plots of MetaLearners Explainability: Feature importance and SHAP values diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index ae5f7c40..18df8af8 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -191,7 +191,7 @@ def fit_all_nuisance( ) self._assign_joblib_nuisance_results(results) - + self._nuisance_models_fit = True return self def fit_all_treatment( @@ -337,7 +337,11 @@ def treatment_effect( 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. + """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 + :meth:`~metalearners.drlearner.DRLearner.fit_all_nuisance` method. Args: X (Matrix): Covariate matrix. @@ -347,6 +351,10 @@ def treatment_effect( Returns: np.ndarray: Treatment effect and standard error for each treatment variant. """ + if not self._nuisance_models_fit: + raise ValueError( + "The nuisance models need to be fitted before computing the treatment effect." + ) 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( diff --git a/pixi.lock b/pixi.lock index 8baa1d0e..7227f074 100644 --- a/pixi.lock +++ b/pixi.lock @@ -11,11 +11,11 @@ environments: - 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/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/bzip2-1.0.8-hd590300_5.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/causalml-0.15.1-py310h2bcf4ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.6.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py310hd41b1e2_0.conda @@ -23,7 +23,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-0.29.34-py310heca2aa9_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dill-0.3.8-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dowhy-0.7.1-pyhd8ed1ab_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/econml-0.15.0-py310hcc13569_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/econml-0.15.1-py310h27b3328_0.conda - 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__osx >=11.0 + - libzlib >=1.2.13,<2.0.0a0 + license: BSD-3-Clause + license_family: BSD + size: 405089 + timestamp: 1714723101397 From 62cf281a5c8a05e713e69d913a69078473d25ca4 Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 12:21:32 +0200 Subject: [PATCH 07/13] Apply pchs. --- tests/test_drlearner.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index e9d9e502..80c77029 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -2,7 +2,6 @@ # SPDX-License-Identifier: BSD-3-Clause from itertools import repeat -from sklearn.linear_model import LinearRegression, LogisticRegression import numpy as np import onnxruntime as rt From 9da63e96eabc90eeffe1f01ebe82f5f76e1760fb Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 12:24:28 +0200 Subject: [PATCH 08/13] Rename method from treatment_effects to average_treatment_effects. --- CHANGELOG.rst | 2 +- docs/examples/example_estimating_ates.ipynb | 8 ++++---- metalearners/drlearner.py | 2 +- tests/test_drlearner.py | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 50acafdd..6fef175b 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -22,7 +22,7 @@ Changelog * Added ``_necessary_onnx_models`` to :class:`metalearners.MetaLearner`. -* Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to +* Added :meth:`metalearners.metalearner.DRLearner.average_treatment_effect` to compute AIPW point estimate and standard error for _average treatment effects (ATE)_ without requiring a full model fit (which is required for CATE diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index 4a2c93a9..d0120496 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -14,7 +14,7 @@ "\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. \n", "\n", - "In this example, we simulate some data with a confounded binary treatment 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`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n", + "In this example, we simulate some data with a confounded binary treatment and demonstrate the `average_treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n", "\n", "Example\n", "-------" @@ -236,7 +236,7 @@ " y=df[outcome_column],\n", " w=df[treatment_column],\n", ")\n", - "metalearners_est = metalearners_dr.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + "metalearners_est = metalearners_dr.average_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", @@ -248,7 +248,7 @@ "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" + "Manual computation with pseudo outcome method produces the same estimate (`average_treatment_effect` does a generalisation of this under the hood) yields the same estimate" ] }, { @@ -713,7 +713,7 @@ " y=df_multi[outcome_column],\n", " w=df_multi[treatment_column],\n", ")\n", - "metalearners_est2 = metalearners_dr_2.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + "metalearners_est2 = metalearners_dr_2.average_treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", " X=df_multi[feature_columns],\n", " y=df_multi[outcome_column],\n", " w=df_multi[treatment_column],\n", diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index a65735e6..1db92a19 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -338,7 +338,7 @@ def evaluate( return variant_outcome_evaluation | propensity_evaluation | treatment_evaluation - def treatment_effect( + def average_treatment_effect( self, X: Matrix, y: Vector, diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index 80c77029..f5a942ac 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -150,5 +150,5 @@ def test_treatment_effect( n_folds=2, ) ml.fit_all_nuisance(X, Y, W) - est = ml.treatment_effect(X, Y, W) + est = ml.average_treatment_effect(X, Y, W) np.testing.assert_almost_equal(est[:, 0], tau.mean(), decimal=1) From 98d4408aca71b5def32975d4fbb3ab46afc91adc Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 12:29:16 +0200 Subject: [PATCH 09/13] Add indicator for oos data. --- docs/examples/example_estimating_ates.ipynb | 2 ++ metalearners/drlearner.py | 8 +++++--- tests/test_drlearner.py | 2 +- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index d0120496..9ba10c27 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -240,6 +240,7 @@ " X=df[feature_columns],\n", " w=df[treatment_column],\n", " y=df[outcome_column],\n", + " is_oos=False,\n", ")\n", "metalearners_est" ] @@ -717,6 +718,7 @@ " X=df_multi[feature_columns],\n", " y=df_multi[outcome_column],\n", " w=df_multi[treatment_column],\n", + " is_oos=False,\n", ")\n", "metalearners_est2" ] diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index 1db92a19..1bfc2ab5 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -343,6 +343,7 @@ def average_treatment_effect( X: Matrix, y: Vector, w: Vector, + is_oos: bool, ) -> 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- @@ -351,9 +352,10 @@ def average_treatment_effect( :meth:`~metalearners.drlearner.DRLearner.fit_all_nuisance` method. Args: - X (Matrix): Covariate matrix. - y (Vector): Outcome vector. + X (Matrix): Covariate matrix + y (Vector): Outcome vector w (Vector): Treatment vector + is_oos (bool): indicator whether data is out of sample Returns: np.ndarray: Treatment effect and standard error for each treatment variant. @@ -369,7 +371,7 @@ def average_treatment_effect( w=w, y=y, treatment_variant=treatment_variant, - is_oos=False, + is_oos=is_oos, ) treatment_effect = gamma_matrix.mean(axis=0) standard_error = gamma_matrix.std(axis=0) / np.sqrt(len(X)) diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index f5a942ac..14298e21 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -150,5 +150,5 @@ def test_treatment_effect( n_folds=2, ) ml.fit_all_nuisance(X, Y, W) - est = ml.average_treatment_effect(X, Y, W) + est = ml.average_treatment_effect(X, Y, W, is_oos=False) np.testing.assert_almost_equal(est[:, 0], tau.mean(), decimal=1) From 5ddf5533aebaedbceee53d53ef8fd9ca8b7d70ee Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Tue, 6 Aug 2024 10:33:07 -0700 Subject: [PATCH 10/13] Revert "Move fixed propensity model to utils. (#72)" This reverts commit 3b2777c101eb382593164e2db8401c3acca75fd8. --- CHANGELOG.rst | 6 +-- docs/examples/example_propensity.ipynb | 66 ++++++++++++++++---------- metalearners/utils.py | 32 ------------- tests/test_utils.py | 61 +----------------------- 4 files changed, 45 insertions(+), 120 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index a89a0839..c5b1a50e 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -12,9 +12,7 @@ Changelog **New features** -* Add :meth:`metalearners.metalearner.MetaLearner.init_params`. - -* Add :class:`metalearners.utils.FixedBinaryPropensity`. +* Added :meth:`metalearners.metalearner.MetaLearner.init_params`. * Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to compute AIPW point estimate and standard error for _average treatment effects (ATE)_ without requiring a full model fit (which is required for CATE estimation). A new notebook contains examples. @@ -23,7 +21,7 @@ Changelog **New features** -* Add :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and +* Added :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and :meth:`metalearners.metalearner.MetaLearner.fit_all_treatment`. * Add optional ``store_raw_results`` and ``store_results`` parameters to :class:`metalearners.grid_search.MetaLearnerGridSearch`. diff --git a/docs/examples/example_propensity.ipynb b/docs/examples/example_propensity.ipynb index 7fc280f8..3a356351 100644 --- a/docs/examples/example_propensity.ipynb +++ b/docs/examples/example_propensity.ipynb @@ -59,8 +59,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using a dummy estimator\n", - "-----------------------\n", + "Creating our own estimator\n", + "--------------------------\n", "\n", "In this tutorial we will assume that we know that all observations were assigned to the\n", "treatment with a fixed probability of 0.3, which is close to the fraction of the observations\n", @@ -89,10 +89,43 @@ "dataset, we just use it for illustrational purposes.\n", "```\n", "\n", - "Now we can use a custom ``sklearn``-like classifier: {class}`~metalearners.utils.FixedBinaryPropensity`.\n", - "The latter can be used like any ``sklearn`` classifier but will always return the same propensity,\n", - "independently of the observed covariates. This propensity has to be provided at initialization via the\n", - "``propensity_score`` parameter." + "Now we can define our custom ``sklearn``-like classifier. We recommend inheriting from\n", + "the ``sklearn`` base classes and following the rules explained in the\n", + "[sklearn documentation](https://scikit-learn.org/stable/developers/develop.html) to avoid\n", + "having to define helper functions and ensure the correct functionality of the ``metalearners``\n", + "library." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "from sklearn.base import BaseEstimator, ClassifierMixin\n", + "from typing import Any\n", + "from typing_extensions import Self\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "class FixedPropensityModel(ClassifierMixin, BaseEstimator):\n", + " def __init__(self, propensity_score: float) -> None:\n", + " self.propensity_score = propensity_score\n", + "\n", + " def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:\n", + " self.classes_ = np.unique(y.to_numpy()) # sklearn requires this\n", + " return self\n", + "\n", + " def predict(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n", + " return np.argmax(self.predict_proba(X), axis=1)\n", + "\n", + " def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n", + " return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score])" ] }, { @@ -116,12 +149,11 @@ "outputs": [], "source": [ "from metalearners import RLearner\n", - "from metalearners.utils import FixedBinaryPropensity\n", "from lightgbm import LGBMRegressor\n", "\n", "rlearner = RLearner(\n", " nuisance_model_factory=LGBMRegressor,\n", - " propensity_model_factory=FixedBinaryPropensity,\n", + " propensity_model_factory=FixedPropensityModel,\n", " treatment_model_factory=LGBMRegressor,\n", " nuisance_model_params={\"verbose\": -1},\n", " propensity_model_params={\"propensity_score\": 0.3},\n", @@ -173,24 +205,10 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" + "name": "python" } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 2 } diff --git a/metalearners/utils.py b/metalearners/utils.py index 587bef67..765e6c1d 100644 --- a/metalearners/utils.py +++ b/metalearners/utils.py @@ -1,14 +1,8 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause -from typing import Any - import numpy as np -import pandas as pd -from sklearn.base import BaseEstimator, ClassifierMixin -from typing_extensions import Self -from metalearners._typing import Matrix, Vector from metalearners.drlearner import DRLearner from metalearners.metalearner import MetaLearner from metalearners.rlearner import RLearner @@ -79,29 +73,3 @@ def simplify_output(tensor: np.ndarray) -> np.ndarray: if n_outputs == 2: return tensor[:, :, 1].reshape(n_obs, n_variants) return tensor - - -class FixedBinaryPropensity(ClassifierMixin, BaseEstimator): - """Binary classifier propensity dummy model which outputs a fixed propensity, - independently of covariates.""" - - def __init__(self, propensity_score: float) -> None: - if not 0 <= propensity_score <= 1: - raise ValueError( - f"Expected a propensity score between 0 and 1 but got {propensity_score}." - ) - self.propensity_score = propensity_score - - def fit(self, X: Matrix, y: Vector) -> Self: - self.classes_ = np.unique(y) # sklearn requires this - if (n_classes := len(self.classes_)) > 2: - raise ValueError( - f"FixedBinaryPropensityModel only supports binary outcomes but {n_classes} were provided ." - ) - return self - - def predict(self, X: Matrix) -> np.ndarray[Any, Any]: - return np.argmax(self.predict_proba(X), axis=1) - - def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]: - return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score]) diff --git a/tests/test_utils.py b/tests/test_utils.py index d88b5d14..c3f334ec 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,16 +2,11 @@ # SPDX-License-Identifier: BSD-3-Clause import numpy as np -import pandas as pd import pytest from lightgbm import LGBMRegressor from metalearners.metalearner import MetaLearner -from metalearners.utils import ( - FixedBinaryPropensity, - metalearner_factory, - simplify_output, -) +from metalearners.utils import metalearner_factory, simplify_output @pytest.mark.parametrize("prefix", ["T"]) @@ -57,57 +52,3 @@ def test_simplify_output(input, expected): def test_simplify_output_raises(input): with pytest.raises(ValueError, match="needs to be 3-dimensional"): simplify_output(input) - - -@pytest.mark.parametrize("use_pd", [True, False]) -def test_fixed_binary_propensity(use_pd): - propensity_score = 0.3 - dominant_class = propensity_score >= 0.5 - - model = FixedBinaryPropensity(propensity_score=propensity_score) - - n_samples = 5 - X_train = np.ones((n_samples, 5)) - y_train = np.ones(n_samples) - if use_pd: - X_train = pd.DataFrame(X_train) - y_train = pd.Series(y_train) - - model.fit(X_train, y_train) - - n_test_samples = 3 - X_test = np.zeros(n_test_samples) - - class_predictions = model.predict(X_test) - assert np.array_equal( - class_predictions, np.array(np.ones(n_test_samples) * dominant_class) - ) - - probability_estimates = model.predict_proba(X_test) - assert np.array_equal( - probability_estimates, - np.column_stack( - ( - np.ones(n_test_samples) * (1 - propensity_score), - np.ones(n_test_samples) * propensity_score, - ) - ), - ) - - -@pytest.mark.parametrize("propensity_score", [-1, 100, 1.1]) -def test_fixed_binary_propensity_not_a_propbability(propensity_score): - with pytest.raises(ValueError, match="between 0 and 1 but got"): - FixedBinaryPropensity(propensity_score=propensity_score) - - -def test_fixed_binary_propensity_non_binary(): - propensity_score = 0.3 - - model = FixedBinaryPropensity(propensity_score=propensity_score) - - n_samples = 5 - X_train = np.ones((n_samples, 5)) - y_train = np.fromiter(range(n_samples), dtype=int) - with pytest.raises(ValueError, match="only supports binary outcomes"): - model.fit(X_train, y_train) From 0acba125a534242b5b28117b81f4a0bfa1dbf6b1 Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 22:18:35 +0200 Subject: [PATCH 11/13] Update pixi lock. --- pixi.lock | 30329 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 30329 insertions(+) create mode 100644 pixi.lock diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 00000000..cb9aa727 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,30329 @@ +version: 5 +environments: + benchmark: + channels: + - 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__osx >=11.0 + - libzlib >=1.2.13,<2.0.0a0 + license: BSD-3-Clause + license_family: BSD + size: 405089 + timestamp: 1714723101397 From dec01bc79421b1d90628afb63435f7b6cdde2b9d Mon Sep 17 00:00:00 2001 From: kklein Date: Wed, 7 Aug 2024 10:06:03 +0200 Subject: [PATCH 12/13] Fix notebook. --- docs/examples/example_estimating_ates.ipynb | 509 +++----------------- 1 file changed, 56 insertions(+), 453 deletions(-) diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index 9ba10c27..84d14d04 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -4,9 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "(example-ate)=\n", - "\n", - " Example: Estimating Average Treatment Effects\n", + "Example: Estimating Average Treatment Effects\n", "=============================\n", "\n", "Motivation\n", @@ -22,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -101,20 +99,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.083595103597918, 0.06526671583747883)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "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", @@ -123,20 +110,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.1433722387308025, 0.06345124983351998)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "covaradjust_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}+{'+'.join(feature_columns)}\",\n", " df) .fit(cov_type=\"HC1\")\n", @@ -162,42 +138,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from metalearners import DRLearner\n", "from lightgbm import LGBMRegressor, LGBMClassifier\n", @@ -206,20 +149,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.02931589, 0.06679633]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "metalearners_dr = DRLearner(\n", " nuisance_model_factory=LGBMRegressor,\n", @@ -254,17 +186,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "est: 1.0293158917468608, se: 0.06679966900982737\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "gamma_i = metalearners_dr._pseudo_outcome(\n", " X=df[feature_columns],\n", @@ -289,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -314,17 +238,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.08356716 0.05786543]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(doubleml_est := aipw_mod.summary.values[0, :2])" ] @@ -338,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -348,17 +264,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Y ~ 0 + X0+X1+X2+X3\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(ff := f\"{outcome_column} ~ 0 + {'+'.join(feature_columns)}\")\n", "y, X = fm.Formula(ff).get_model_matrix(df, output=\"numpy\")\n", @@ -367,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -378,17 +286,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.001 0.109]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(econml_est := econml_dr.intercept__inference(1).summary_frame().iloc[0, :2].values)" ] @@ -404,69 +304,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Y W X0 X1 X2 X3\n", - "0 0 1.0 1.138203 -1.439008 -1.981465 0.0\n", - "1 1 1.0 0.297749 -0.092953 1.455780 0.0\n", - "2 1 1.0 1.150090 0.545041 0.799303 0.0\n", - "3 1 0.0 0.832256 -0.591107 -1.076526 0.0\n", - "4 0 1.0 1.559507 -1.018031 -1.137247 0.0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def classification_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", " \"\"\"DGP for a confounded treatment assignment with binary outcome\"\"\"\n", @@ -884,24 +512,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/latex": [ - "$\\displaystyle 0.081358650748229$" - ], - "text/plain": [ - "0.08135865074822901" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# naive TE\n", "df_class.groupby(\"W\")[\"Y\"].mean().diff()[1]" @@ -909,20 +522,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.04373356, 0.04191263]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "metalearners_dr_3 = DRLearner(\n", " nuisance_model_factory=LGBMClassifier,\n", @@ -939,10 +541,11 @@ " y=df_class[outcome_column],\n", " w=df_class[treatment_column],\n", ")\n", - "metalearners_est_3 = metalearners_dr_3.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + "metalearners_est_3 = metalearners_dr_3.average_treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", " X=df_class[feature_columns],\n", " y=df_class[outcome_column],\n", " w=df_class[treatment_column],\n", + " is_oos=False,\n", ")\n", "metalearners_est_3" ] @@ -957,7 +560,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -971,9 +574,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.9" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 7941bbd76b5b920505e6156777da52b2a3ff826e Mon Sep 17 00:00:00 2001 From: kklein Date: Wed, 7 Aug 2024 10:15:37 +0200 Subject: [PATCH 13/13] Remove jupyter magic. --- docs/examples/example_estimating_ates.ipynb | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index 84d14d04..72011e58 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -217,7 +217,6 @@ "metadata": {}, "outputs": [], "source": [ - "%%capture\n", "from doubleml import DoubleMLIRM, DoubleMLData\n", "dml_data = DoubleMLData(\n", " df,\n", @@ -279,7 +278,6 @@ "metadata": {}, "outputs": [], "source": [ - "%%capture\n", "econml_dr = LinearDRLearner(model_regression=LGBMRegressor(), model_propensity=LGBMClassifier())\n", "econml_dr.fit(y, T=W, W=X)" ]