From fbbe76dd86e373f48e75d2c8695d6bcbe9427d29 Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Mon, 5 Aug 2024 16:27:45 -0700 Subject: [PATCH] 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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+ [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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