{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Python: Potential Quantiles and Quantile Treatment Effects\n", "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate (local) potential quantiles and (local) quantile treatment effects. The estimation is based on [Kallus et al. (2019)](https://arxiv.org/abs/1912.12945)." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Potential Quantiles (PQs)\n", "\n", "At first, we will start with the estimation of the quantiles of the potential outcomes." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Data\n", "We define a data generating process to create synthetic data to compare the estimates to the true effect." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import doubleml as dml\n", "import multiprocessing\n", "\n", "from lightgbm import LGBMClassifier" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "The data is generated as a location-scale model with\n", "\n", "$$Y_i = \\text{loc}(D_i,X_i) + \\text{scale}(D_i,X_i)\\cdot\\varepsilon_i,$$\n", "\n", "where $X_i\\sim\\mathcal{U}[-1,1]^{p}$ and $\\varepsilon_i \\sim \\mathcal{N}(0,1)$.\n", "Further, the location and scale are determined according to the following functions\n", "\n", "$$\\begin{aligned}\n", "\\text{loc}(d,x) &:= 0.5d + 2dx_5 + 2\\cdot 1\\{x_2 > 0.1\\} - 1.7\\cdot 1\\{x_1x_3 > 0\\} - 3x_4 \\\\\n", "\\text{scale}(d,x) &:= \\sqrt{0.5d + 0.3dx_1 + 2},\n", "\\end{aligned}$$\n", "\n", "and the treatment takes the following form\n", "\n", "$$D_i = 1_{\\{(X_2 - X_4 + 1.5\\cdot 1\\{x_1 > 0\\} + \\epsilon_i > 0)\\}}$$\n", "\n", "with $\\epsilon_i \\sim \\mathcal{N}(0,1)$.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f_loc(D, X):\n", " loc = 0.5*D + 2*D*X[:,4] + 2.0*(X[:,1] > 0.1) - 1.7*(X[:,0] * X[:,2] > 0) - 3*X[:,3]\n", " return loc\n", "\n", "def f_scale(D, X):\n", " scale = np.sqrt(0.5*D + 0.3*D*X[:,1] + 2)\n", " return scale\n", "\n", "def dgp(n=200, p=5):\n", " X = np.random.uniform(-1,1,size=[n,p])\n", " D = ((X[:,1 ] - X[:,3] + 1.5*(X[:,0] > 0) + np.random.normal(size=n)) > 0)*1.0\n", " epsilon = np.random.normal(size=n)\n", "\n", " Y = f_loc(D, X) + f_scale(D, X)*epsilon\n", "\n", " return Y, X, D, epsilon" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "We can calculate the true potential quantile analytically or through simulations. Here, we will just approximate the true potential quantile for a range of quantiles." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Potential Quantile Y(0): [-3.33014346 -2.71465114 -2.21155656 -1.77348822 -1.37436439 -1.00000591\n", " -0.64197957 -0.29548121 0.04653976 0.38866808 0.73608412 1.09347419\n", " 1.46811985 1.8685788 2.30982972 2.81568484 3.43597565]\n", "Potential Quantile Y(1): [-3.23789633 -2.53947541 -1.97276281 -1.48208358 -1.03698487 -0.62131806\n", " -0.22522221 0.15891559 0.53724023 0.91724807 1.30361321 1.7018663\n", " 2.12046836 2.56965663 3.06724028 3.64154727 4.35341202]\n" ] } ], "source": [ "tau_vec = np.arange(0.1,0.95,0.05)\n", "n_true = int(10e+6)\n", "\n", "_, X_true, _, epsilon_true = dgp(n=n_true)\n", "D1 = np.ones(n_true)\n", "D0 = np.zeros(n_true)\n", "\n", "Y1 = f_loc(D1, X_true) + f_scale(D1, X_true)*epsilon_true\n", "Y0 = f_loc(D0, X_true) + f_scale(D0, X_true)*epsilon_true\n", "\n", "Y1_quant = np.quantile(Y1, q=tau_vec)\n", "Y0_quant = np.quantile(Y0, q=tau_vec)\n", "\n", "print(f'Potential Quantile Y(0): {Y0_quant}')\n", "print(f'Potential Quantile Y(1): {Y1_quant}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Let us generate $n=5000$ observations and convert them to a [DoubleMLData](https://docs.doubleml.org/stable/api/generated/doubleml.DoubleMLData.html) object." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n = 5000\n", "np.random.seed(42)\n", "Y, X, D, _ = dgp(n=n)\n", "obj_dml_data = dml.DoubleMLData.from_arrays(X, Y, D)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Potential Quantile Estimation\n", "Next, we can initialize our two machine learning algorithms to train the different nuisance elements. Then we can initialize the `DoubleMLPQ` objects and call `fit()` to estimate the relevant parameters. To obtain confidence intervals, we can use the `confint()` method." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quantile: 0.1\n", "Quantile: 0.15000000000000002\n", "Quantile: 0.20000000000000004\n", "Quantile: 0.25000000000000006\n", "Quantile: 0.30000000000000004\n", "Quantile: 0.3500000000000001\n", "Quantile: 0.40000000000000013\n", "Quantile: 0.45000000000000007\n", "Quantile: 0.5000000000000001\n", "Quantile: 0.5500000000000002\n", "Quantile: 0.6000000000000002\n", "Quantile: 0.6500000000000001\n", "Quantile: 0.7000000000000002\n", "Quantile: 0.7500000000000002\n", "Quantile: 0.8000000000000002\n", "Quantile: 0.8500000000000002\n", "Quantile: 0.9000000000000002\n" ] } ], "source": [ "ml_m = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10)\n", "ml_g = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10)\n", "\n", "PQ_0 = np.full((len(tau_vec)), np.nan)\n", "PQ_1 = np.full((len(tau_vec)), np.nan)\n", "\n", "ci_PQ_0 = np.full((len(tau_vec),2), np.nan)\n", "ci_PQ_1 = np.full((len(tau_vec),2), np.nan)\n", "\n", "for idx_tau, tau in enumerate(tau_vec):\n", " print(f'Quantile: {tau}')\n", " dml_PQ_0 = dml.DoubleMLPQ(obj_dml_data,\n", " ml_g, ml_m,\n", " quantile=tau,\n", " treatment=0,\n", " n_folds=5)\n", " dml_PQ_1 = dml.DoubleMLPQ(obj_dml_data,\n", " ml_g, ml_m,\n", " quantile=tau,\n", " treatment=1,\n", " n_folds=5)\n", "\n", " dml_PQ_0.fit()\n", " dml_PQ_1.fit()\n", "\n", " ci_PQ_0[idx_tau, :] = dml_PQ_0.confint(level=0.95).to_numpy()\n", " ci_PQ_1[idx_tau, :] = dml_PQ_1.confint(level=0.95).to_numpy()\n", "\n", " PQ_0[idx_tau] = dml_PQ_0.coef.squeeze()\n", " PQ_1[idx_tau] = dml_PQ_1.coef.squeeze()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Finally, let us take a look at the estimated quantiles." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile Y(0) Y(1) DML Y(0) DML Y(1) DML Y(0) lower \\\n", "0 0.10 -3.330143 -3.237896 -3.408565 -3.128312 -3.813293 \n", "1 0.15 -2.714651 -2.539475 -2.855780 -2.495752 -3.245512 \n", "2 0.20 -2.211557 -1.972763 -2.345903 -1.978977 -2.638264 \n", "3 0.25 -1.773488 -1.482084 -1.924002 -1.533900 -2.189737 \n", "4 0.30 -1.374364 -1.036985 -1.482483 -1.148161 -1.683942 \n", "5 0.35 -1.000006 -0.621318 -1.246879 -0.700102 -1.509196 \n", "6 0.40 -0.641980 -0.225222 -0.932973 -0.291406 -1.244455 \n", "7 0.45 -0.295481 0.158916 -0.665264 0.145245 -0.949456 \n", "8 0.50 0.046540 0.537240 -0.077319 0.496551 -0.411582 \n", "9 0.55 0.388668 0.917248 0.378834 0.760104 0.070020 \n", "10 0.60 0.736084 1.303613 0.479928 1.216344 0.168614 \n", "11 0.65 1.093474 1.701866 1.059384 1.655284 0.677614 \n", "12 0.70 1.468120 2.120468 1.544097 2.036147 1.215342 \n", "13 0.75 1.868579 2.569657 1.700015 2.493219 1.400823 \n", "14 0.80 2.309830 3.067240 2.187690 2.988463 1.872768 \n", "15 0.85 2.815685 3.641547 2.631333 3.542647 2.226524 \n", "16 0.90 3.435976 4.353412 3.113207 4.243246 2.753523 \n", "\n", " DML Y(0) upper DML Y(1) lower DML Y(1) upper \n", "0 -3.003836 -3.448745 -2.807879 \n", "1 -2.466047 -2.687345 -2.304159 \n", "2 -2.053541 -2.177496 -1.780458 \n", "3 -1.658267 -1.684502 -1.383297 \n", "4 -1.281024 -1.319759 -0.976562 \n", "5 -0.984562 -0.844707 -0.555498 \n", "6 -0.621490 -0.428255 -0.154557 \n", "7 -0.381072 0.015698 0.274793 \n", "8 0.256944 0.367625 0.625477 \n", "9 0.687647 0.627560 0.892648 \n", "10 0.791241 1.088048 1.344640 \n", "11 1.441153 1.524657 1.785911 \n", "12 1.872852 1.907115 2.165178 \n", "13 1.999207 2.360004 2.626433 \n", "14 2.502612 2.857161 3.119766 \n", "15 3.036143 3.408539 3.676756 \n", "16 3.472891 4.098712 4.387780 \n" ] } ], "source": [ "data = {\"Quantile\": tau_vec, \"Y(0)\": Y0_quant, \"Y(1)\": Y1_quant,\n", " \"DML Y(0)\": PQ_0, \"DML Y(1)\": PQ_1,\n", " \"DML Y(0) lower\": ci_PQ_0[:, 0], \"DML Y(0) upper\": ci_PQ_0[:, 1],\n", " \"DML Y(1) lower\": ci_PQ_1[:, 0], \"DML Y(1) upper\": ci_PQ_1[:, 1]}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, (ax1, ax2) = plt.subplots(1 ,2)\n", "ax1.grid(); ax2.grid()\n", "\n", "ax1.plot(df['Quantile'],df['DML Y(0)'], color='violet', label='Estimated Quantile Y(0)')\n", "ax1.plot(df['Quantile'],df['Y(0)'], color='green', label='True Quantile Y(0)')\n", "ax1.fill_between(df['Quantile'], df['DML Y(0) lower'], df['DML Y(0) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax1.legend()\n", "ax1.set_ylim(-3, 4)\n", "\n", "ax2.plot(df['Quantile'],df['DML Y(1)'], color='violet', label='Estimated Quantile Y(1)')\n", "ax2.plot(df['Quantile'],df['Y(1)'], color='green', label='True Quantile Y(1)')\n", "ax2.fill_between(df['Quantile'], df['DML Y(1) lower'], df['DML Y(1) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax2.legend()\n", "ax2.set_ylim(-3, 4)\n", "\n", "fig.suptitle('Potential Quantiles', fontsize=16)\n", "fig.supxlabel('Quantile')\n", "_ = fig.supylabel('Potential Quantile and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Quantile Treatment Effects (QTEs)\n", "In most cases, we want to evaluate the quantile treatment effect as the difference between potential quantiles.\n", "Here, different quantiles can be estimated in parallel with `n_jobs_models`.\n", "\n", "To estimate the quantile treatment effect, we can use" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Cores used: 5\n", "================== DoubleMLQTE Object ==================\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", "0.10 0.267950 0.231986 1.155025 2.480800e-01 -0.186735 0.722634\n", "0.15 0.293218 0.190982 1.535318 1.247057e-01 -0.081100 0.667536\n", "0.20 0.388071 0.167547 2.316193 2.054771e-02 0.059685 0.716456\n", "0.25 0.406285 0.161243 2.519710 1.174516e-02 0.090255 0.722316\n", "0.30 0.466756 0.151819 3.074426 2.109079e-03 0.169196 0.764315\n", "0.35 0.606129 0.149285 4.060201 4.903056e-05 0.313535 0.898722\n", "0.40 0.708459 0.158007 4.483711 7.335609e-06 0.398770 1.018148\n", "0.45 0.725166 0.156021 4.647873 3.353748e-06 0.419371 1.030962\n", "0.50 0.509461 0.164608 3.094999 1.968134e-03 0.186836 0.832086\n", "0.55 0.488811 0.161236 3.031639 2.432300e-03 0.172793 0.804828\n", "0.60 0.542989 0.162153 3.348617 8.121584e-04 0.225175 0.860804\n", "0.65 0.699035 0.190809 3.663529 2.487641e-04 0.325056 1.073013\n", "0.70 0.516528 0.195377 2.643752 8.199281e-03 0.133596 0.899460\n", "0.75 0.685107 0.245062 2.795647 5.179588e-03 0.204794 1.165419\n", "0.80 0.893851 0.222843 4.011131 6.042844e-05 0.457088 1.330615\n", "0.85 0.896023 0.209894 4.268942 1.964025e-05 0.484640 1.307407\n", "0.90 1.229443 0.177995 6.907176 4.943949e-12 0.880579 1.578307\n" ] } ], "source": [ "n_cores = multiprocessing.cpu_count()\n", "cores_used = np.min([5, n_cores - 1])\n", "print(f\"Number of Cores used: {cores_used}\")\n", "\n", "dml_QTE = dml.DoubleMLQTE(obj_dml_data,\n", " ml_g,\n", " ml_m,\n", " quantiles=tau_vec,\n", " n_folds=5)\n", "dml_QTE.fit(n_jobs_models=cores_used)\n", "print(dml_QTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "As for other ``dml`` objects, we can use ``bootstrap()`` and ``confint()`` methods to generate jointly valid confidence intervals." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.5 % 97.5 %\n", "0.10 -0.399692 0.935591\n", "0.15 -0.256416 0.842853\n", "0.20 -0.094118 0.870260\n", "0.25 -0.057762 0.870332\n", "0.30 0.029831 0.903681\n", "0.35 0.176495 1.035762\n", "0.40 0.253724 1.163194\n", "0.45 0.276148 1.174185\n", "0.50 0.035730 0.983192\n", "0.55 0.024782 0.952839\n", "0.60 0.076322 1.009656\n", "0.65 0.149898 1.248171\n", "0.70 -0.045754 1.078810\n", "0.75 -0.020166 1.390379\n", "0.80 0.252524 1.535179\n", "0.85 0.291963 1.500084\n", "0.90 0.717185 1.741702\n" ] } ], "source": [ "ci_QTE = dml_QTE.confint(level=0.95, joint=False)\n", "\n", "dml_QTE.bootstrap(n_rep_boot=2000)\n", "ci_joint_QTE = dml_QTE.confint(level=0.95, joint=True)\n", "print(ci_joint_QTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "As before, let us take a look at the estimated effects and confidence intervals." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile QTE DML QTE DML QTE pointwise lower \\\n", "0.10 0.10 0.092247 0.267950 -0.186735 \n", "0.15 0.15 0.175176 0.293218 -0.081100 \n", "0.20 0.20 0.238794 0.388071 0.059685 \n", "0.25 0.25 0.291405 0.406285 0.090255 \n", "0.30 0.30 0.337380 0.466756 0.169196 \n", "0.35 0.35 0.378688 0.606129 0.313535 \n", "0.40 0.40 0.416757 0.708459 0.398770 \n", "0.45 0.45 0.454397 0.725166 0.419371 \n", "0.50 0.50 0.490700 0.509461 0.186836 \n", "0.55 0.55 0.528580 0.488811 0.172793 \n", "0.60 0.60 0.567529 0.542989 0.225175 \n", "0.65 0.65 0.608392 0.699035 0.325056 \n", "0.70 0.70 0.652349 0.516528 0.133596 \n", "0.75 0.75 0.701078 0.685107 0.204794 \n", "0.80 0.80 0.757411 0.893851 0.457088 \n", "0.85 0.85 0.825862 0.896023 0.484640 \n", "0.90 0.90 0.917436 1.229443 0.880579 \n", "\n", " DML QTE pointwise upper DML QTE joint lower DML QTE joint upper \n", "0.10 0.722634 -0.399692 0.935591 \n", "0.15 0.667536 -0.256416 0.842853 \n", "0.20 0.716456 -0.094118 0.870260 \n", "0.25 0.722316 -0.057762 0.870332 \n", "0.30 0.764315 0.029831 0.903681 \n", "0.35 0.898722 0.176495 1.035762 \n", "0.40 1.018148 0.253724 1.163194 \n", "0.45 1.030962 0.276148 1.174185 \n", "0.50 0.832086 0.035730 0.983192 \n", "0.55 0.804828 0.024782 0.952839 \n", "0.60 0.860804 0.076322 1.009656 \n", "0.65 1.073013 0.149898 1.248171 \n", "0.70 0.899460 -0.045754 1.078810 \n", "0.75 1.165419 -0.020166 1.390379 \n", "0.80 1.330615 0.252524 1.535179 \n", "0.85 1.307407 0.291963 1.500084 \n", "0.90 1.578307 0.717185 1.741702 \n" ] } ], "source": [ "QTE = Y1_quant - Y0_quant\n", "data = {\"Quantile\": tau_vec, \"QTE\": QTE, \"DML QTE\": dml_QTE.coef,\n", " \"DML QTE pointwise lower\": ci_QTE['2.5 %'], \"DML QTE pointwise upper\": ci_QTE['97.5 %'],\n", " \"DML QTE joint lower\": ci_joint_QTE['2.5 %'], \"DML QTE joint upper\": ci_joint_QTE['97.5 %']}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, ax = plt.subplots()\n", "ax.grid()\n", "\n", "ax.plot(df['Quantile'],df['DML QTE'], color='violet', label='Estimated QTE')\n", "ax.plot(df['Quantile'],df['QTE'], color='green', label='True QTE')\n", "ax.fill_between(df['Quantile'], df['DML QTE pointwise lower'], df['DML QTE pointwise upper'], color='violet', alpha=.3, label='Pointwise Confidence Interval')\n", "ax.fill_between(df['Quantile'], df['DML QTE joint lower'], df['DML QTE joint upper'], color='violet', alpha=.2, label='Joint Confidence Interval')\n", "\n", "plt.legend()\n", "plt.title('Quantile Treatment Effects', fontsize=16)\n", "plt.xlabel('Quantile')\n", "_ = plt.ylabel('QTE and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Local Potential Quantiles (LPQs)\n", "\n", "Next, we will consider local potential quantiles and the corresponding local quantile treatment effects." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Data\n", "We add a counfounder and an instrument to our data generating process." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import doubleml as dml\n", "import multiprocessing\n", "\n", "from lightgbm import LGBMClassifier" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The data is generated as a location-scale model with confounding of the treatment $D_i$ and instrument $Z_i$\n", "\n", "$$\\begin{aligned}\n", "Y_i &= \\text{loc}\\left(D_i,X_i, X^{(conf)}_i\\right) + \\text{scale}\\left(D_i,X_i, X^{(conf)}_i\\right)\\cdot\\varepsilon_i,\\\\\n", "D_i &=1\\{0.5Z_i -0.3X_{i,1}+0.7X^{(conf)}_i + \\eta_i > 0\\},\n", "\\end{aligned}$$\n", "\n", "where $X_i\\sim\\mathcal{U}[-1,1]^{p}$, $X^{(conf)}_i \\sim\\mathcal{U}[-1,1]$, $Z_i\\sim \\mathcal{B}(1,1/2)$, $\\varepsilon_i \\sim \\mathcal{N}(0,1)$ and $\\eta_i \\sim \\mathcal{N}(0,1)$.\n", "Further, the location and scale are determined according to the following functions\n", "\n", "$$\\begin{aligned}\n", "\\text{loc}(d,x) &:= 0.5d + 2dx_5 + 2\\cdot 1\\{x_2 > 0.1\\} - 1.7\\cdot 1\\{x_1x_3 > 0\\} - 3x_4 - 2x^{(conf)}\\\\\n", "\\text{scale}(d,x, x^{(conf)}) &:= \\sqrt{0.5d + 3dx_1 + 0.4x^{(conf)} + 2}\n", "\\end{aligned}$$" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def f_loc(D, X, X_conf):\n", " loc = 0.5*D + 2*D*X[:,4] + 2.0*(X[:,1] > 0.1) - 1.7*(X[:,0] * X[:,2] > 0) - 3*X[:,3] - 2*X_conf[:, 0]\n", " return loc\n", "\n", "def f_scale(D, X, X_conf):\n", " scale = np.sqrt(0.5*D + 3*D*X[:, 0] + 0.4*X_conf[:, 0] + 2)\n", " return scale\n", "\n", "def generate_treatment(Z, X, X_conf):\n", " eta = np.random.normal(size=len(Z))\n", " d = ((0.5*Z -0.3*X[:, 0] + 0.7*X_conf[:, 0] + eta) > 0)*1.0\n", " return d\n", "\n", "def dgp(n=200, p=5):\n", " X = np.random.uniform(0, 1, size=[n,p])\n", " X_conf = np.random.uniform(-1, 1, size=[n,1])\n", " Z = np.random.binomial(1, p=0.5, size=n)\n", " D = generate_treatment(Z, X, X_conf)\n", " epsilon = np.random.normal(size=n)\n", "\n", " Y = f_loc(D, X, X_conf) + f_scale(D, X, X_conf)*epsilon\n", "\n", " return Y, X, D, Z" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will just simulate the true quantile for a range of quantiles." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "tau_vec = np.arange(0.1,0.95,0.05)\n", "n_true = int(10e+6)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compliance probability: 0.3245837\n" ] } ], "source": [ "p=5\n", "X_true = np.random.uniform(0, 1, size=[n_true,p])\n", "X_conf_true = np.random.uniform(-1, 1, size=[n_true,1])\n", "Z_true = np.random.binomial(1, p=0.5, size=n_true)\n", "eta_true = np.random.normal(size=n_true)\n", "D1_true = generate_treatment(np.ones_like(Z_true), X_true, X_conf_true)\n", "D0_true = generate_treatment(np.zeros_like(Z_true), X_true, X_conf_true)\n", "epsilon_true = np.random.normal(size=n_true)\n", "\n", "compliers = (D1_true == 1) * (D0_true == 0)\n", "print(f'Compliance probability: {str(compliers.mean())}')\n", "n_compliers = compliers.sum()\n", "Y1 = f_loc(np.ones(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) + f_scale(np.ones(n_compliers), X_true[compliers, :], X_conf_true[compliers, :])*epsilon_true[compliers]\n", "Y0 = f_loc(np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) + f_scale(np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :])*epsilon_true[compliers]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local Potential Quantile Y(0): [-4.07538443 -3.53606675 -3.10878571 -2.74402577 -2.41805621 -2.11724226\n", " -1.83287529 -1.56018481 -1.29299726 -1.02900983 -0.76228406 -0.4895498\n", " -0.20400735 0.10089588 0.43453524 0.81827267 1.29107127]\n", "Local Potential Quantile Y(1): [-3.19031969 -2.53273833 -2.01574297 -1.57245066 -1.17700723 -0.81190107\n", " -0.46807543 -0.13585644 0.1881752 0.51214922 0.84030318 1.17655394\n", " 1.53094017 1.91102953 2.33175566 2.81828926 3.4284675 ]\n" ] } ], "source": [ "Y0_quant = np.quantile(Y0, q=tau_vec)\n", "Y1_quant = np.quantile(Y1, q=tau_vec)\n", "\n", "print(f'Local Potential Quantile Y(0): {Y0_quant}')\n", "print(f'Local Potential Quantile Y(1): {Y1_quant}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Let us generate $n=10000$ observations and convert them to a [DoubleMLData](https://docs.doubleml.org/stable/api/generated/doubleml.DoubleMLData.html) object." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "n = 10000\n", "np.random.seed(42)\n", "Y, X, D, Z = dgp(n=n,p=p)\n", "obj_dml_data = dml.DoubleMLData.from_arrays(X, Y, D, Z)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Local Potential Quantile Estimation\n", "Next, we can initialize our two machine learning algorithms to train the different nuisance elements. As above, we can initialize the `DoubleMLLPQ` objects and call `fit()` to estimate the relevant parameters. To obtain confidence intervals, we can use the `confint()` method." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quantile: 0.1\n", "Quantile: 0.15000000000000002\n", "Quantile: 0.20000000000000004\n", "Quantile: 0.25000000000000006\n", "Quantile: 0.30000000000000004\n", "Quantile: 0.3500000000000001\n", "Quantile: 0.40000000000000013\n", "Quantile: 0.45000000000000007\n", "Quantile: 0.5000000000000001\n", "Quantile: 0.5500000000000002\n", "Quantile: 0.6000000000000002\n", "Quantile: 0.6500000000000001\n", "Quantile: 0.7000000000000002\n", "Quantile: 0.7500000000000002\n", "Quantile: 0.8000000000000002\n", "Quantile: 0.8500000000000002\n", "Quantile: 0.9000000000000002\n" ] } ], "source": [ "ml_m = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10)\n", "ml_g = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10)\n", "\n", "LPQ_0 = np.full((len(tau_vec)), np.nan)\n", "LPQ_1 = np.full((len(tau_vec)), np.nan)\n", "\n", "ci_LPQ_0 = np.full((len(tau_vec),2), np.nan)\n", "ci_LPQ_1 = np.full((len(tau_vec),2), np.nan)\n", "\n", "for idx_tau, tau in enumerate(tau_vec):\n", " print(f'Quantile: {tau}')\n", " dml_LPQ_0 = dml.DoubleMLLPQ(obj_dml_data,\n", " ml_g,\n", " ml_m,\n", " score=\"LPQ\",\n", " treatment=0,\n", " quantile=tau,\n", " n_folds=5,\n", " trimming_threshold=0.05)\n", " dml_LPQ_1 = dml.DoubleMLLPQ(obj_dml_data,\n", " ml_g,\n", " ml_m,\n", " score=\"LPQ\",\n", " treatment=1,\n", " quantile=tau,\n", " n_folds=5,\n", " trimming_threshold=0.05)\n", "\n", " dml_LPQ_0.fit()\n", " dml_LPQ_1.fit()\n", "\n", " LPQ_0[idx_tau] = dml_LPQ_0.coef.squeeze()\n", " LPQ_1[idx_tau] = dml_LPQ_1.coef.squeeze()\n", "\n", " ci_LPQ_0[idx_tau, :] = dml_LPQ_0.confint(level=0.95).to_numpy()\n", " ci_LPQ_1[idx_tau, :] = dml_LPQ_1.confint(level=0.95).to_numpy()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let us take a look at the estimated local quantiles." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile Y(0) Y(1) DML Y(0) DML Y(1) DML Y(0) lower \\\n", "0 0.10 -4.075384 -3.190320 -4.231310 -3.306963 -4.976088 \n", "1 0.15 -3.536067 -2.532738 -3.188223 -2.826492 -4.009122 \n", "2 0.20 -3.108786 -2.015743 -2.641528 -1.935730 -3.164801 \n", "3 0.25 -2.744026 -1.572451 -2.387426 -1.590813 -2.838114 \n", "4 0.30 -2.418056 -1.177007 -1.927074 -0.878281 -2.301371 \n", "5 0.35 -2.117242 -0.811901 -1.799403 -0.583534 -2.155120 \n", "6 0.40 -1.832875 -0.468075 -1.564045 -0.134211 -1.923607 \n", "7 0.45 -1.560185 -0.135856 -1.412127 0.023256 -1.765792 \n", "8 0.50 -1.292997 0.188175 -1.144669 0.317487 -1.566024 \n", "9 0.55 -1.029010 0.512149 -0.970065 0.516255 -1.441209 \n", "10 0.60 -0.762284 0.840303 -0.649158 0.793570 -1.180951 \n", "11 0.65 -0.489550 1.176554 -0.355209 1.556792 -0.919432 \n", "12 0.70 -0.204007 1.530940 -0.077883 1.742907 -0.742128 \n", "13 0.75 0.100896 1.911030 0.354371 1.995248 -0.379614 \n", "14 0.80 0.434535 2.331756 0.692725 2.562013 0.009986 \n", "15 0.85 0.818273 2.818289 0.842746 2.720571 0.146037 \n", "16 0.90 1.291071 3.428467 1.196189 3.946433 0.145625 \n", "\n", " DML Y(0) upper DML Y(1) lower DML Y(1) upper \n", "0 -3.486532 -3.867565 -2.746361 \n", "1 -2.367323 -3.883622 -1.769361 \n", "2 -2.118255 -2.724338 -1.147121 \n", "3 -1.936739 -2.377311 -0.804316 \n", "4 -1.552776 -1.563503 -0.193060 \n", "5 -1.443686 -1.182633 0.015565 \n", "6 -1.204482 -0.728710 0.460289 \n", "7 -1.058463 -0.559522 0.606034 \n", "8 -0.723314 -0.260360 0.895333 \n", "9 -0.498921 -0.098319 1.130829 \n", "10 -0.117366 0.070884 1.516256 \n", "11 0.209014 0.793735 2.319850 \n", "12 0.586362 0.973241 2.512572 \n", "13 1.088357 1.168931 2.821566 \n", "14 1.375465 1.685807 3.438219 \n", "15 1.539455 1.827735 3.613408 \n", "16 2.246753 1.889733 6.003134 \n" ] } ], "source": [ "data = {\"Quantile\": tau_vec, \"Y(0)\": Y0_quant, \"Y(1)\": Y1_quant,\n", " \"DML Y(0)\": LPQ_0, \"DML Y(1)\": LPQ_1,\n", " \"DML Y(0) lower\": ci_LPQ_0[:, 0], \"DML Y(0) upper\": ci_LPQ_0[:, 1],\n", " \"DML Y(1) lower\": ci_LPQ_1[:, 0], \"DML Y(1) upper\": ci_LPQ_1[:, 1]}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, (ax1, ax2) = plt.subplots(1 ,2)\n", "ax1.grid(); ax2.grid()\n", "\n", "ax1.plot(df['Quantile'],df['DML Y(0)'], color='violet', label='Estimated Local Quantile Y(0)')\n", "ax1.plot(df['Quantile'],df['Y(0)'], color='green', label='True Local Quantile Y(0)')\n", "ax1.fill_between(df['Quantile'], df['DML Y(0) lower'], df['DML Y(0) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax1.legend()\n", "ax1.set_ylim(-3, 4)\n", "\n", "ax2.plot(df['Quantile'],df['DML Y(1)'], color='violet', label='Estimated Local Quantile Y(1)')\n", "ax2.plot(df['Quantile'],df['Y(1)'], color='green', label='True Local Quantile Y(1)')\n", "ax2.fill_between(df['Quantile'], df['DML Y(1) lower'], df['DML Y(1) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax2.legend()\n", "ax2.set_ylim(-3, 4)\n", "\n", "fig.suptitle('Local Potential Quantiles', fontsize=16)\n", "fig.supxlabel('Quantile')\n", "_ = fig.supylabel('Local Potential Quantile and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Local Quantile Treatment Effects (LQTEs)\n", "As for quantile treatment effects, we often want to evaluate the (local) treatment effect.\n", "To estimate local quantile treatment effects, we can use the `DoubleMLQTE` object and specify `LPQ` as the score. \n", "\n", "As for quantile treatment effects, different quantiles can be estimated in parallel with `n_jobs_models`." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Cores used: 5\n", "================== DoubleMLQTE Object ==================\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", "0.10 1.103497 0.463325 2.381689 1.723345e-02 0.195396 2.011598\n", "0.15 1.052502 0.590736 1.781681 7.480133e-02 -0.105318 2.210323\n", "0.20 0.707868 0.411295 1.721071 8.523794e-02 -0.098256 1.513992\n", "0.25 0.931479 0.427725 2.177751 2.942460e-02 0.093153 1.769805\n", "0.30 1.182849 0.326740 3.620156 2.944253e-04 0.542451 1.823247\n", "0.35 1.220772 0.297682 4.100923 4.115060e-05 0.637326 1.804219\n", "0.40 1.369796 0.292105 4.689392 2.740180e-06 0.797280 1.942312\n", "0.45 1.403425 0.287041 4.889293 1.011988e-06 0.840836 1.966015\n", "0.50 1.500517 0.283974 5.283994 1.263974e-07 0.943938 2.057095\n", "0.55 1.642016 0.304130 5.399056 6.699259e-08 1.045932 2.238101\n", "0.60 1.502995 0.350518 4.287926 1.803492e-05 0.815993 2.189998\n", "0.65 1.901148 0.335846 5.660776 1.506900e-08 1.242902 2.559394\n", "0.70 1.827381 0.331521 5.512108 3.545605e-08 1.177611 2.477150\n", "0.75 1.844308 0.339570 5.431306 5.594316e-08 1.178763 2.509853\n", "0.80 1.916914 0.305775 6.269043 3.632747e-10 1.317607 2.516222\n", "0.85 2.087947 0.346206 6.030934 1.630150e-09 1.409395 2.766499\n", "0.90 2.250354 0.536746 4.192587 2.757917e-05 1.198351 3.302357\n" ] } ], "source": [ "n_cores = multiprocessing.cpu_count()\n", "cores_used = np.min([5, n_cores - 1])\n", "print(f\"Number of Cores used: {cores_used}\")\n", "\n", "dml_LQTE = dml.DoubleMLQTE(obj_dml_data, \n", " ml_g=ml_g,\n", " ml_m=ml_m, \n", " quantiles=tau_vec, \n", " score='LPQ', \n", " n_folds=5)\n", "dml_LQTE.fit(n_jobs_models=cores_used)\n", "print(dml_LQTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "As for other ``dml`` objects, we can use ``bootstrap()`` and ``confint()`` methods to generate jointly valid confidence intervals." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.5 % 97.5 %\n", "0.10 -0.206614 2.413608\n", "0.15 -0.617877 2.722881\n", "0.20 -0.455120 1.870857\n", "0.25 -0.277968 2.140926\n", "0.30 0.258951 2.106746\n", "0.35 0.379038 2.062507\n", "0.40 0.543832 2.195761\n", "0.45 0.591782 2.215069\n", "0.50 0.697545 2.303489\n", "0.55 0.782050 2.501983\n", "0.60 0.511862 2.494129\n", "0.65 0.951502 2.850794\n", "0.70 0.889963 2.764798\n", "0.75 0.884132 2.804484\n", "0.80 1.052298 2.781530\n", "0.85 1.109005 3.066889\n", "0.90 0.732638 3.768071\n" ] } ], "source": [ "ci_LQTE = dml_LQTE.confint(level=0.95, joint=False)\n", "\n", "dml_LQTE.bootstrap(n_rep_boot=2000)\n", "ci_joint_LQTE = dml_LQTE.confint(level=0.95, joint=True)\n", "print(ci_joint_LQTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "As before, let us take a look at the estimated effects and confidence intervals." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile LQTE DML LQTE DML LQTE pointwise lower \\\n", "0.10 0.10 0.885065 1.103497 0.195396 \n", "0.15 0.15 1.003328 1.052502 -0.105318 \n", "0.20 0.20 1.093043 0.707868 -0.098256 \n", "0.25 0.25 1.171575 0.931479 0.093153 \n", "0.30 0.30 1.241049 1.182849 0.542451 \n", "0.35 0.35 1.305341 1.220772 0.637326 \n", "0.40 0.40 1.364800 1.369796 0.797280 \n", "0.45 0.45 1.424328 1.403425 0.840836 \n", "0.50 0.50 1.481172 1.500517 0.943938 \n", "0.55 0.55 1.541159 1.642016 1.045932 \n", "0.60 0.60 1.602587 1.502995 0.815993 \n", "0.65 0.65 1.666104 1.901148 1.242902 \n", "0.70 0.70 1.734948 1.827381 1.177611 \n", "0.75 0.75 1.810134 1.844308 1.178763 \n", "0.80 0.80 1.897220 1.916914 1.317607 \n", "0.85 0.85 2.000017 2.087947 1.409395 \n", "0.90 0.90 2.137396 2.250354 1.198351 \n", "\n", " DML LQTE pointwise upper DML LQTE joint lower DML LQTE joint upper \n", "0.10 2.011598 -0.206614 2.413608 \n", "0.15 2.210323 -0.617877 2.722881 \n", "0.20 1.513992 -0.455120 1.870857 \n", "0.25 1.769805 -0.277968 2.140926 \n", "0.30 1.823247 0.258951 2.106746 \n", "0.35 1.804219 0.379038 2.062507 \n", "0.40 1.942312 0.543832 2.195761 \n", "0.45 1.966015 0.591782 2.215069 \n", "0.50 2.057095 0.697545 2.303489 \n", "0.55 2.238101 0.782050 2.501983 \n", "0.60 2.189998 0.511862 2.494129 \n", "0.65 2.559394 0.951502 2.850794 \n", "0.70 2.477150 0.889963 2.764798 \n", "0.75 2.509853 0.884132 2.804484 \n", "0.80 2.516222 1.052298 2.781530 \n", "0.85 2.766499 1.109005 3.066889 \n", "0.90 3.302357 0.732638 3.768071 \n" ] } ], "source": [ "LQTE = Y1_quant - Y0_quant\n", "data = {\"Quantile\": tau_vec, \"LQTE\": LQTE, \"DML LQTE\": dml_LQTE.coef,\n", " \"DML LQTE pointwise lower\": ci_LQTE['2.5 %'], \"DML LQTE pointwise upper\": ci_LQTE['97.5 %'],\n", " \"DML LQTE joint lower\": ci_joint_LQTE['2.5 %'], \"DML LQTE joint upper\": ci_joint_LQTE['97.5 %']}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, ax = plt.subplots()\n", "ax.grid()\n", "\n", "ax.plot(df['Quantile'],df['DML LQTE'], color='violet', label='Estimated LQTE')\n", "ax.plot(df['Quantile'],df['LQTE'], color='green', label='True LQTE')\n", "ax.fill_between(df['Quantile'], df['DML LQTE pointwise lower'], df['DML LQTE pointwise upper'], color='violet', alpha=.3, label='Pointwise Confidence Interval')\n", "ax.fill_between(df['Quantile'], df['DML LQTE joint lower'], df['DML LQTE joint upper'], color='violet', alpha=.2, label='Joint Confidence Interval')\n", "\n", "plt.legend()\n", "plt.title('Local Quantile Treatment Effects', fontsize=16)\n", "plt.xlabel('Quantile')\n", "_ = plt.ylabel('LQTE and 95%-CI')" ] } ], "metadata": { "kernelspec": { "display_name": "dml_base", "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": "3.11.2" }, "vscode": { "interpreter": { "hash": "04413e1efd13b5e2aaedccc5facec5686292d28983ef24fa021a12c73bd6369e" } } }, "nbformat": 4, "nbformat_minor": 0 }