{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LaLonde Dataset\n", "\n", "Economists have long-hypothesized that training programs could improve the labor market prospects of participants.\n", "In an attempt to test (or demonstrate) this, the National Supported Work (NSW) Demonstration was initiated using combined private and federal funding. This program was implemented between 1975 and 1979 in 15 locations across the US.\n", "The program provided 6-18 month training for individuals who had faced economic and social problems (such as women receiving Aid to Families with Dependent Children, former drug addicts, ex-convicts, and former juvenile delinquents, etc.).\n", "\n", "Participants were randomly assigned into experimental group (Support Work Programs) and control groups.\n", "However, due to the long duration of the study, participants joining the program at the beginning had different characteristics than people joining later. \n", "Therefore, this covariate shift should be adjusted for in order to estimate the true causal effect of the job-program on future employment.\n", "\n", "Furthermore, we add some observational data that was obtained from the Population Survey of Income Dynamics and the Current Population Survey. These did not receive any training and are considered controls.\n", "\n", "This dataset had become a common benchmark for causal analysis over the years.\n", "Original analysis of the study was done by [Robert LaLonde](https://en.wikipedia.org/wiki/Robert_LaLonde) and published in his 1986 [Evaluating the Econometric Evaluations of Training Programs with Experimental Data](http://people.hbs.edu/nashraf/LaLonde_1986.pdf) paper. \n", "The analysis here is based on results from a later, propensity-based, analysis made by Dehejia and Wahba in their 1999 [Causal Effects in Non-Experimental Studies: Reevaluating the Evaluation of Training Programs](https://users.nber.org/~rdehejia/papers/dehejia_wahba_jasa.pdf).\n", "\n", "We follow the procedure described in the LaLonde example to load the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Data\n", "First, let's download the dataset from [Rajeev Dehejia's webpage](https://users.nber.org/~rdehejia/nswdata2.html)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(22106, 10)\n" ] }, { "data": { "text/html": [ "
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trainingageeducationblackhispanicmarriedno_degreere74re75re78
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54120.027.012.00.00.01.00.016297.18013429.2100019562.14000
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" ], "text/plain": [ " training age education black hispanic married no_degree \\\n", "16827 0.0 26.0 13.0 0.0 0.0 0.0 0.0 \n", "5412 0.0 27.0 12.0 0.0 0.0 1.0 0.0 \n", "15399 0.0 26.0 12.0 0.0 0.0 0.0 0.0 \n", "13077 0.0 38.0 16.0 0.0 0.0 1.0 0.0 \n", "2189 0.0 55.0 8.0 0.0 0.0 1.0 1.0 \n", "\n", " re74 re75 re78 \n", "16827 58.778 50.12903 31.03226 \n", "5412 16297.180 13429.21000 19562.14000 \n", "15399 5217.527 3174.24200 25564.67000 \n", "13077 23713.010 9178.98400 18814.41000 \n", "2189 0.000 0.00000 0.00000 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "columns = [\"training\", # Treatment assignment indicator\n", " \"age\", # Age of participant\n", " \"education\", # Years of education\n", " \"black\", # Indicate whether individual is black\n", " \"hispanic\", # Indicate whether individual is hispanic\n", " \"married\", # Indicate whether individual is married\n", " \"no_degree\", # Indicate if individual has no high-school diploma\n", " \"re74\", # Real earnings in 1974, prior to study participation\n", " \"re75\", # Real earnings in 1975, prior to study participation\n", " \"re78\"] # Real earnings in 1978, after study end\n", "\n", "# treated = pd.read_csv(\"http://www.nber.org/~rdehejia/data/nswre74_treated.txt\",\n", "# delim_whitespace=True, header=None, names=columns)\n", "# control = pd.read_csv(\"http://www.nber.org/~rdehejia/data/nswre74_control.txt\",\n", "# delim_whitespace=True, header=None, names=columns)\n", "file_names = [\"http://www.nber.org/~rdehejia/data/nswre74_treated.txt\",\n", " \"http://www.nber.org/~rdehejia/data/nswre74_control.txt\",\n", " \"http://www.nber.org/~rdehejia/data/psid_controls.txt\",\n", " \"http://www.nber.org/~rdehejia/data/psid2_controls.txt\",\n", " \"http://www.nber.org/~rdehejia/data/psid3_controls.txt\",\n", " \"http://www.nber.org/~rdehejia/data/cps_controls.txt\",\n", " \"http://www.nber.org/~rdehejia/data/cps2_controls.txt\",\n", " \"http://www.nber.org/~rdehejia/data/cps3_controls.txt\"]\n", "files = [pd.read_csv(file_name, delim_whitespace=True,\n", " header=None, names=columns) for file_name in file_names]\n", "lalonde = pd.concat(files, ignore_index=True)\n", "lalonde = lalonde.sample(frac=1.0, random_state=42) # Shuffle\n", "\n", "print(lalonde.shape)\n", "lalonde.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The dataset contains 22106 people, out of which 185 received training\n" ] } ], "source": [ "print(f'The dataset contains {lalonde.shape[0]} people, out of which {lalonde[\"training\"].sum():.0f} received training')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Design matrix\n", "#### Earning indications\n", "Following the analysis performed by Gelman et al. on their [arm](https://cran.r-project.org/web/packages/arm/index.html) R library, we will create two indicator variables indicating no earnings in 1974 and 1975." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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trainingageeducationblackhispanicmarriedno_degreere74re75re78re74=0re75=0
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54120.027.012.00.00.01.00.016297.18013429.2100019562.1400000
153990.026.012.00.00.00.00.05217.5273174.2420025564.6700000
130770.038.016.00.00.01.00.023713.0109178.9840018814.4100000
21890.055.08.00.00.01.01.00.0000.000000.0000011
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" ], "text/plain": [ " training age education black hispanic married no_degree \\\n", "16827 0.0 26.0 13.0 0.0 0.0 0.0 0.0 \n", "5412 0.0 27.0 12.0 0.0 0.0 1.0 0.0 \n", "15399 0.0 26.0 12.0 0.0 0.0 0.0 0.0 \n", "13077 0.0 38.0 16.0 0.0 0.0 1.0 0.0 \n", "2189 0.0 55.0 8.0 0.0 0.0 1.0 1.0 \n", "\n", " re74 re75 re78 re74=0 re75=0 \n", "16827 58.778 50.12903 31.03226 0 0 \n", "5412 16297.180 13429.21000 19562.14000 0 0 \n", "15399 5217.527 3174.24200 25564.67000 0 0 \n", "13077 23713.010 9178.98400 18814.41000 0 0 \n", "2189 0.000 0.00000 0.00000 1 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lalonde = lalonde.join((lalonde[[\"re74\", \"re75\"]] == 0).astype(int), rsuffix=(\"=0\"))\n", "lalonde.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(22106, 12)\n" ] }, { "data": { "text/html": [ "
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trainingageeducationblackhispanicmarriedno_degreere74re75re78re74=0re75=0
168270.026.013.00.00.00.00.058.77850.1290331.0322600
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21890.055.08.00.00.01.01.00.0000.000000.0000011
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" ], "text/plain": [ " training age education black hispanic married no_degree \\\n", "16827 0.0 26.0 13.0 0.0 0.0 0.0 0.0 \n", "5412 0.0 27.0 12.0 0.0 0.0 1.0 0.0 \n", "15399 0.0 26.0 12.0 0.0 0.0 0.0 0.0 \n", "13077 0.0 38.0 16.0 0.0 0.0 1.0 0.0 \n", "2189 0.0 55.0 8.0 0.0 0.0 1.0 1.0 \n", "\n", " re74 re75 re78 re74=0 re75=0 \n", "16827 58.778 50.12903 31.03226 0 0 \n", "5412 16297.180 13429.21000 19562.14000 0 0 \n", "15399 5217.527 3174.24200 25564.67000 0 0 \n", "13077 23713.010 9178.98400 18814.41000 0 0 \n", "2189 0.000 0.00000 0.00000 1 1 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(lalonde.shape)\n", "lalonde.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variables selection\n", "Lastly, we extract the covariates, treatment and outcome variables" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((22106, 10), (22106,), (22106,))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = lalonde.pop(\"training\")\n", "y = lalonde.pop(\"re78\")\n", "X = lalonde\n", "X.shape, a.shape, y.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Matching to Estimate the Outcome and to Prepare the Data for IPW\n", "We have some concerns here that the data may be too imbalanced between treatment and control to allow a reliable inference. Even though propensity weighting can, in principle, correct for covariate imbalances, we see here that this data has some pretty severe positivity violations. When we condition the data by matching it before using inverse propensity weighting, we find that the IPW is more effective as judged by the weighted covariate imbalance. Most interestingly, we see that the sign of the effect changes once the covariates are balanced.\n", "\n", "We'll start by looking at the results of a simple matching on propensity with one neighbor and with replacement. We use the `PropensityTransformer` object to calculate the propensity and add it to the covariates to be used for matching." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from causallib.estimation import IPW, Matching\n", "from causallib.preprocessing.transformers import PropensityTransformer\n", "from sklearn.linear_model import LogisticRegression\n", "import pandas as pd\n", "\n", "\n", "def learner(): return LogisticRegression(solver=\"liblinear\",\n", " max_iter=5000,\n", " class_weight=\"balanced\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we show in the Faiss notebook, matching is much faster using the faiss backend. This requires `faiss-gpu` or `faiss-cpu` to be installed. We will automatically select it if it is available, falling back on \"sklearn\" if not." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "try:\n", " from causallib.contrib.faissknn import FaissNearestNeighbors\n", " knn_backend = FaissNearestNeighbors\n", "except ImportError:\n", " knn_backend = \"sklearn\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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............
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44212 rows × 2 columns

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" ], "text/plain": [ " distances matches\n", "match_to_treatment sample_id \n", "0.0 16827 [0] [16827]\n", " 5412 [0] [5412]\n", " 15399 [0] [15399]\n", " 13077 [0] [13077]\n", " 2189 [0] [2189]\n", "... ... ...\n", "1.0 11964 [0.029483559] [178]\n", " 21575 [0.0] [30]\n", " 5390 [0.01956479] [171]\n", " 860 [0.058751065] [178]\n", " 15795 [0.005777422] [41]\n", "\n", "[44212 rows x 2 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "propensity_transform = PropensityTransformer(\n", " include_covariates=False, learner=learner())\n", "matcher = Matching(propensity_transform=propensity_transform,\n", " with_replacement=True, n_neighbors=1, knn_backend=knn_backend)\n", "matcher.fit(X, a, y)\n", "matcher.match(X, a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One way to understand better how close our samples our is to examine the covariates for the matches that we have discovered. We can do that with the `get_covariates_of_matches` function." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "best_control_matches = matcher.get_covariates_of_matches(1, 0, X)\n", "best_treatment_matches = matcher.get_covariates_of_matches(0, 1, X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the worst matches and see which covariates are not matched. The `get_covariates_of_matches` DataFrame includes the covariates of the matches, and details on the matches. We will focus here on the `\"delta\"` columns:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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ageeducationblackhispanicmarriedno_degreere74re75re74=0re75=0
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" ], "text/plain": [ " age education black hispanic married no_degree re74 \\\n", "2274 23.0 0.0 0.0 0.0 1.0 0.0 130388.686 \n", "18914 33.0 5.0 0.0 0.0 1.0 0.0 81407.012 \n", "12877 27.0 4.0 0.0 0.0 1.0 0.0 87284.813 \n", "3310 20.0 4.0 0.0 0.0 1.0 0.0 97081.146 \n", "253 15.0 5.0 0.0 0.0 1.0 0.0 75529.212 \n", "17098 6.0 4.0 0.0 0.0 1.0 0.0 91203.346 \n", "4605 25.0 4.0 0.0 0.0 1.0 0.0 71610.678 \n", "11638 33.0 4.0 0.0 0.0 1.0 0.0 42221.676 \n", "6670 11.0 4.0 0.0 1.0 1.0 0.0 28506.808 \n", "21105 16.0 1.0 0.0 0.0 1.0 0.0 44180.943 \n", "\n", " re75 re74=0 re75=0 \n", "2274 148197.726 0.0 0.0 \n", "18914 90012.238 0.0 0.0 \n", "12877 90012.238 0.0 0.0 \n", "3310 84641.270 0.0 0.0 \n", "253 76584.819 0.0 0.0 \n", "17098 81060.625 0.0 0.0 \n", "4605 70318.690 0.0 0.0 \n", "11638 63157.399 0.0 0.0 \n", "6670 77479.980 0.0 0.0 \n", "21105 64947.722 0.0 0.0 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_control_matches.sort_values((\"match\", \"distance\"), ascending=False)[\"delta\"].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see pretty bad matching on income in 1974 and 1975 (\"re74\" and \"re75\") as well as age, even as the other covariates seem pretty well matched. We can do the same thing looking at the best matches each treatment sample found. We find much better agreement, both in terms of propensity scores distances and in terms of the covariates. Here, the income delta is about an order of magnitude smaller than we find in the other direction." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age education black hispanic married no_degree re74 \\\n", "142 23.0 -4.0 1.0 -1.0 -1.0 1.0 3165.6580 \n", "93 8.0 1.0 0.0 0.0 1.0 -1.0 -11917.0550 \n", "103 30.0 4.0 0.0 0.0 0.0 -1.0 -2870.3260 \n", "115 0.0 2.0 0.0 0.0 0.0 0.0 -4829.4348 \n", "130 8.0 -1.0 0.0 0.0 0.0 1.0 -2849.6230 \n", "127 21.0 -1.0 0.0 0.0 0.0 1.0 0.0000 \n", "184 5.0 -10.0 0.0 0.0 0.0 1.0 4552.0330 \n", "11 29.0 -8.0 0.0 0.0 0.0 1.0 0.0000 \n", "76 -9.0 -2.0 0.0 0.0 0.0 1.0 -2163.3790 \n", "83 2.0 -2.0 0.0 0.0 0.0 1.0 -4392.7160 \n", "\n", " re75 re74=0 re75=0 \n", "142 2378.0940 -1.0 0.0 \n", "93 -10407.8857 0.0 0.0 \n", "103 -785.9516 1.0 1.0 \n", "115 -3580.6452 0.0 1.0 \n", "130 -1241.8860 0.0 0.0 \n", "127 -6640.3060 0.0 1.0 \n", "184 3405.5497 0.0 0.0 \n", "11 -6640.3060 0.0 1.0 \n", "76 3800.9480 0.0 0.0 \n", "83 583.5100 0.0 0.0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_treatment_matches.sort_values((\"match\", \"distance\"), ascending=False)[\"delta\"].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tells us that for every treated individual (ie, an individual who received employment training) there is an untreated individual that is fairly similar, but there are a great many control individuals who have covariates, particularly income levels, that are vastly different from their nearest match in the treated group." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can pursue this observation further by examining the distribution of propensity distances for matching control samples and matching treatment samples." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "f, axes = plt.subplots(1, 2, figsize=(16, 5))\n", "best_control_matches.match.distance.hist(ax=axes[0], bins=40)\n", "best_treatment_matches.match.distance.hist(ax=axes[1], bins=40)\n", "axes[0].set_xlabel(\"$\\Delta$ Propensity of closest match\")\n", "axes[1].set_xlabel(\"$\\Delta$ Propensity of closest match\")\n", "axes[0].set_ylabel(\"Count\")\n", "axes[1].set_ylabel(\"Count\")\n", "axes[0].set_title(\"Control\")\n", "axes[1].set_title(\"Treatment\")\n", "axes[0].axvline(x=best_treatment_matches.match.distance.max(\n", "), color=\"red\", label=\"max $\\Delta$ propensity of treatment-control\")\n", "axes[0].legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow! The largest distance of a nearest neighbor that we find when searching for neighbors of the treated is in the first couple percentiles of distances of nearest neighbors when searching from control to treated. Actually it's just inside the 6th percentile:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.07171205693170932" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum((best_control_matches.match.distance <=\n", " best_treatment_matches.match.distance.max())) / len(best_control_matches)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How can matching help us in this case? For one we can do a simple, no replacement match and compare the `n_treated` pairs to estimate the outcome:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0 5093.492942\n", "1.0 6349.143530\n", "dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matcher.with_replacement = False\n", "matcher.match(X, a)\n", "matcher.estimate_population_outcome(X, a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That gives us an effect of $1752 for those who received the treatment. That's pretty dramatic and totally different from the naive estimate, which was negative. But we are only using 370 samples out of the original dataset of ~21,000. It would be nice to utilize more of the data that we have. One thing we can do is utilize a caliper and use matching to estimate the potential outcomes:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "matcher.n_neighbors = 3\n", "results, sample_count = [[], []]\n", "cvec = np.logspace(-7, -0.5, 10)\n", "for caliper in cvec:\n", " matcher.caliper = caliper\n", " matcher.with_replacement = True\n", " matcher.match(X, a)\n", " results.append(matcher.estimate_population_outcome(X, a))\n", " sample_count.append(matcher.samples_used_)\n", "cresults = pd.DataFrame(data=results, index=cvec)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, axes = plt.subplots(1, 2, figsize=(16, 7))\n", "\n", "axes[0].loglog(cvec, sample_count)\n", "axes[0].legend([\"control samples\", \"treatment samples\"])\n", "axes[0].axhline(y=sum(a == 0), ls=\":\", color=\"C0\")\n", "axes[0].axhline(y=sum(a == 1), ls=\":\", color=\"C1\")\n", "axes[0].set_xlabel(\"caliper\")\n", "axes[0].set_ylabel(\"sample count\")\n", "axes[0].axis(\"tight\")\n", "\n", "axes[1].semilogx(cvec, results)\n", "axes[1].legend([\"control prediction\", \"treatment prediction\"])\n", "axes[1].set_xlabel(\"caliper\")\n", "axes[1].set_ylabel(\"Expected Income\")\n", "\n", "plt.tight_layout();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's pretty interesting. We see that in this way we can use a sliding scale of how strict we want to be on the matching and we see a dramatic shift in the effect estimation as we include the distant matches, namely the effect changes signs!\n", "\n", "What about inverse propensity weighting? Propensity is a balancing weight, so as long as we clean up the positivity problems, we should be able to get a pretty robust estimate in this way. We can even keep tabs on the covariate balancing to see how well we are doing at correcting positivity problems. Because if there are positivity problems the covariates will not be balancable by the inverse propensity weights. To give the IPW model more to work with, we can expand to three neighbors (caliper permitting)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from causallib.preprocessing.transformers import MatchingTransformer\n", "from causallib.evaluation.weight_evaluator import calculate_covariate_balance\n", "caliper_vec = np.logspace(-5.5, -1, 10)\n", "covbal = []\n", "n_neighbors = 3\n", "\n", "\n", "def match_then_ipw_weight(caliper):\n", " mt = MatchingTransformer(\n", " propensity_transform=PropensityTransformer(\n", " include_covariates=False, learner=learner()),\n", " caliper=caliper,\n", " n_neighbors=n_neighbors)\n", " mt.fit(X, a, y)\n", " Xm, am, ym = mt.transform(X, a, y)\n", " ipw = IPW(learner=learner())\n", " ipw.fit(Xm, am,)\n", " ipw_weights = ipw.compute_weights(Xm, am)\n", " ipw_outcome = ipw.estimate_population_outcome(Xm, am, ym)\n", " matched_treated = sum(am == 1)\n", " matched_control = sum(am == 0)\n", " covbalance = calculate_covariate_balance(Xm, am, ipw_weights)\n", " return {\"caliper\": caliper, \"n_treated\": matched_treated, \"n_control\": matched_control,\n", " \"ipw_weights\": ipw_weights, \"ipw_outcome\": ipw_outcome,\n", " \"covariate_balance\": covbalance.drop(columns=\"unweighted\")}\n", "\n", "\n", "results = [match_then_ipw_weight(c) for c in caliper_vec]\n", "\n", "covbal_df = pd.concat([i[\"covariate_balance\"] for i in results], axis=1)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn as sb\n", "import matplotlib.pyplot as plt\n", "covbal_df.columns = [\"%.6f\" % i for i in caliper_vec]\n", "covbal_noedu_df = covbal_df.drop(\n", " labels=[i for i in covbal_df.index if \"education\" in i])\n", "f, ax = plt.subplots(2, 2, figsize=(13, 12))\n", "\n", "ax[0, 0].errorbar(caliper_vec, covbal_noedu_df.mean(), yerr=covbal_noedu_df.std())\n", "ax[0, 0].set_xscale(\"log\")\n", "ax[0, 0].set_xlabel(\"caliper\")\n", "ax[0, 0].set_ylabel(\"mean covariate balance\")\n", "\n", "ax[0, 1].semilogx(caliper_vec, [i[\"n_control\"] for i in results], label=\"control\")\n", "ax[0, 1].semilogx(caliper_vec, [i[\"n_treated\"] for i in results], label=\"treatment\")\n", "ax[0, 1].set_ylabel(\"sample count\")\n", "ax[0, 1].set_xlabel(\"caliper\")\n", "ax[0, 1].set_xscale(\"log\")\n", "ax[0, 1].set_yscale(\"log\")\n", "ax[0, 1].legend()\n", "\n", "ax[1, 0].semilogx(caliper_vec, [i[\"ipw_outcome\"][0] for i in results], label=\"control\")\n", "ax[1, 0].semilogx(caliper_vec, [i[\"ipw_outcome\"][1] for i in results], label=\"treatment\")\n", "ax[1, 0].set_ylabel(\"outcome\")\n", "ax[1, 0].set_xlabel(\"caliper\")\n", "ax[1, 0].set_xscale(\"log\")\n", "ax[1, 0].legend()\n", "\n", "sb.heatmap(data=covbal_noedu_df, ax=ax[1, 1])\n", "ax[1, 1].set_xlabel(\"caliper\")\n", "\n", "plt.tight_layout();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "We see that the prediction remains fairly robust as we expand the data to include non-exact matches, up to a point. With no data filtering, the IPW gets tripped up on the positivity violations and does not successfully balance the covariates, especially previous income (and hispanic).\n", "\n", "Thus, despite its power, the IPW method cannot on its own detect positivity violations and can be improved by filtering using matching or a similar procedure.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }