{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "2b2a0fd8", "metadata": {}, "source": [ "# Python: Impact of 401(k) on Financial Wealth (Quantile Effects)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "a967ed5f", "metadata": {}, "source": [ "In this real-data example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate the effect of 401(k) eligibility and participation on accumulated assets. The 401(k) data set has been analyzed in several studies, among others [Chernozhukov et al. (2018)](https://arxiv.org/abs/1608.00060), see [Kallus et al. (2019)](https://arxiv.org/abs/1912.12945) for quantile effects.\n", "\n", "**Remark:**\n", "This notebook focuses on the evaluation of the treatment effect at different quantiles. For a basic introduction to the [DoubleML](https://docs.doubleml.org/stable/index.html) package and a detailed example of the average treatment effect estimation for the 401(k) data set, we refer to the notebook [Python: Impact of 401(k) on Financial Wealth](https://docs.doubleml.org/stable/examples/py_double_ml_pension.html). The Data sections of both notebooks coincide.\n", "\n", "401(k) plans are pension accounts sponsored by employers. The key problem in determining the effect of participation in 401(k) plans on accumulated assets is saver heterogeneity coupled with the fact that the decision to enroll in a 401(k) is non-random. It is generally recognized that some people have a higher preference for saving than others. It also seems likely that those individuals with high unobserved preference for saving would be most likely to choose to participate in tax-advantaged retirement savings plans and would tend to have otherwise high amounts of accumulated assets. The presence of unobserved savings preferences with these properties then implies that conventional estimates that do not account for saver heterogeneity and endogeneity of participation will be biased upward, tending to overstate the savings effects of 401(k) participation.\n", "\n", "One can argue that eligibility for enrolling in a 401(k) plan in this data can be taken as exogenous after conditioning on a few observables of which the most important for their argument is income. The basic idea is that, at least around the time 401(k)’s initially became available, people were unlikely to be basing their employment decisions on whether an employer offered a 401(k) but would instead focus on income and other aspects of the job." ] }, { "attachments": {}, "cell_type": "markdown", "id": "40b41785", "metadata": {}, "source": [ "## Data\n", "\n", "The preprocessed data can be fetched by calling [fetch_401K()](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.fetch_401K.html#doubleml.datasets.fetch_401K). Note that an internet connection is required for loading the data." ] }, { "cell_type": "code", "execution_count": 1, "id": "c06e87b2", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import doubleml as dml\n", "import multiprocessing\n", "from doubleml.datasets import fetch_401K\n", "\n", "from sklearn.base import clone\n", "\n", "from lightgbm import LGBMClassifier, LGBMRegressor\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "28347df3", "metadata": {}, "outputs": [], "source": [ "sns.set()\n", "colors = sns.color_palette()" ] }, { "cell_type": "code", "execution_count": 3, "id": "0ad5caf0", "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = 10., 7.5\n", "sns.set(font_scale=1.5)\n", "sns.set_style('whitegrid', {'axes.spines.top': False,\n", " 'axes.spines.bottom': False,\n", " 'axes.spines.left': False,\n", " 'axes.spines.right': False})" ] }, { "cell_type": "code", "execution_count": 4, "id": "11763be2", "metadata": {}, "outputs": [], "source": [ "data = fetch_401K(return_type='DataFrame')" ] }, { "cell_type": "code", "execution_count": 5, "id": "c1d9ce03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " nifa net_tfa tw age inc \\\n", "count 9.915000e+03 9.915000e+03 9.915000e+03 9915.000000 9915.000000 \n", "mean 1.392864e+04 1.805153e+04 6.381685e+04 41.060212 37200.621094 \n", "std 5.490504e+04 6.352259e+04 1.115296e+05 10.344505 24774.251953 \n", "min 0.000000e+00 -5.023020e+05 -5.023020e+05 25.000000 -2652.000000 \n", "25% 2.000000e+02 -5.000000e+02 3.291500e+03 32.000000 19413.000000 \n", "50% 1.635000e+03 1.499000e+03 2.510000e+04 40.000000 31476.000000 \n", "75% 8.765500e+03 1.652450e+04 8.148750e+04 48.000000 48583.500000 \n", "max 1.430298e+06 1.536798e+06 2.029910e+06 64.000000 242124.000000 \n", "\n", " fsize educ db marr twoearn \\\n", "count 9915.000000 9915.000000 9915.000000 9915.000000 9915.000000 \n", "mean 2.865860 13.206253 0.271004 0.604841 0.380837 \n", "std 1.538937 2.810382 0.444500 0.488909 0.485617 \n", "min 1.000000 1.000000 0.000000 0.000000 0.000000 \n", "25% 2.000000 12.000000 0.000000 0.000000 0.000000 \n", "50% 3.000000 12.000000 0.000000 1.000000 0.000000 \n", "75% 4.000000 16.000000 1.000000 1.000000 1.000000 \n", "max 13.000000 18.000000 1.000000 1.000000 1.000000 \n", "\n", " e401 p401 pira hown \n", "count 9915.000000 9915.000000 9915.000000 9915.000000 \n", "mean 0.371357 0.261624 0.242158 0.635199 \n", "std 0.483192 0.439541 0.428411 0.481399 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 1.000000 \n", "75% 1.000000 1.000000 0.000000 1.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 \n" ] } ], "source": [ "print(data.describe())" ] }, { "attachments": {}, "cell_type": "markdown", "id": "3ea8c00f", "metadata": {}, "source": [ "The data consist of 9,915 observations at the household level drawn from the 1991 Survey of Income and Program Participation (SIPP). All the variables are referred to 1990. We use net financial assets (*net\\_tfa*) as the outcome variable, $Y$, in our analysis. The net financial assets are computed as the sum of IRA balances, 401(k) balances, checking accounts, saving bonds, other interest-earning accounts, other interest-earning assets, stocks, and mutual funds less non mortgage debts. " ] }, { "attachments": {}, "cell_type": "markdown", "id": "1a4f9dc4", "metadata": {}, "source": [ "Among the $9915$ individuals, $3682$ are eligible to participate in the program. The variable *e401* indicates eligibility and *p401* indicates participation, respectively.\n", "\n", "At first consider eligibility as the treatment and define the following data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Set up basic model: Specify variables for data-backend\n", "features_base = ['age', 'inc', 'educ', 'fsize', 'marr',\n", " 'twoearn', 'db', 'pira', 'hown']\n", "\n", "\n", "# Initialize DoubleMLData (data-backend of DoubleML)\n", "data_dml_base = dml.DoubleMLData(data,\n", " y_col='net_tfa',\n", " d_cols='e401',\n", " x_cols=features_base)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "6a22a2ed", "metadata": {}, "source": [ "## Estimating Potential Quantiles and Quantile Treatment Effects\n", "\n", "We will use the [DoubleML](https://docs.doubleml.org/stable/index.html) package to estimate quantile treatment effects of 401(k) eligibility, i.e. `e401`.\n", "As it is more interesting to take a look at a range of quantiles instead of a single one, we will first define a discretisized grid of quanitles `tau_vec`, which will range from the 10%-quantile to the 90%-quantile.\n", "Further, we need a machine learning algorithm to estimate the nuisance elements of our model. In this example, we will use a basic `LGBMClassifier`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tau_vec = np.arange(0.1,0.95,0.05)\n", "n_folds = 5\n", "\n", "# Learners\n", "class_learner = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10)\n", "reg_learner = LGBMRegressor(n_estimators=300, learning_rate=0.05, num_leaves=10)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "d56cd2b7", "metadata": {}, "source": [ "Next, we will apply create an `DoubleMLPQ` object for each quantile to fit a quantile model. Here, we have to specifiy, whether we would like to estimate a potential quantile for the treatment group `treatment=1` or control `treatment=0`. Further basic options are trimming and normalization of the propensity scores (`trimming_rule=\"truncate\"`, `trimming_threshold=0.01` and `normalize_ipw=True`). " ] }, { "cell_type": "code", "execution_count": 8, "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": [ "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(data_dml_base, \n", " ml_g=clone(class_learner),\n", " ml_m=clone(class_learner),\n", " score=\"PQ\",\n", " treatment=0,\n", " quantile=tau,\n", " n_folds=n_folds,\n", " normalize_ipw=True,\n", " trimming_rule=\"truncate\",\n", " trimming_threshold=1e-2)\n", " dml_PQ_1 = dml.DoubleMLPQ(data_dml_base,\n", " ml_g=clone(class_learner),\n", " ml_m=clone(class_learner),\n", " score=\"PQ\",\n", " treatment=1,\n", " quantile=tau,\n", " n_folds=n_folds,\n", " normalize_ipw=True,\n", " trimming_rule=\"truncate\",\n", " trimming_threshold=1e-2)\n", "\n", " dml_PQ_0.fit()\n", " dml_PQ_1.fit()\n", "\n", " PQ_0[idx_tau] = dml_PQ_0.coef\n", " PQ_1[idx_tau] = dml_PQ_1.coef\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()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "3ba94f1d", "metadata": {}, "source": [ "Additionally, each `DoubleMLPQ` object has a (hopefully) helpful summary, which indicates also the evaluation of the nuisance elements with cross-validated estimation. See e.g. `dml_PQ_1'" ] }, { "cell_type": "code", "execution_count": 9, "id": "611263dd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLPQ Object ==================\n", "\n", "------------------ Data summary ------------------\n", "Outcome variable: net_tfa\n", "Treatment variable(s): ['e401']\n", "Covariates: ['age', 'inc', 'educ', 'fsize', 'marr', 'twoearn', 'db', 'pira', 'hown']\n", "Instrument variable(s): None\n", "No. Observations: 9915\n", "\n", "------------------ Score & algorithm ------------------\n", "Score function: PQ\n", "DML algorithm: dml2\n", "\n", "------------------ Machine learner ------------------\n", "Learner ml_g: LGBMClassifier(learning_rate=0.05, n_estimators=300, num_leaves=10)\n", "Learner ml_m: LGBMClassifier(learning_rate=0.05, n_estimators=300, num_leaves=10)\n", "Out-of-sample Performance:\n", "Learner ml_g RMSE: [[0.31337878]]\n", "Learner ml_m RMSE: [[0.4449272]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", "No. repeated sample splits: 1\n", "Apply cross-fitting: True\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", "e401 63499.0 1855.668337 34.218938 1.264274e-256 59861.956892 \n", "\n", " 97.5 % \n", "e401 67136.043108 \n" ] } ], "source": [ "print(dml_PQ_1)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "3011ec6c", "metadata": {}, "source": [ "Finally, let us take a look at the estimated potential quantiles" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile DML Y(0) DML Y(1) DML Y(0) lower DML Y(0) upper \\\n", "0 0.10 -5.150000e+03 -4200.0 -5518.552508 -4781.447492 \n", "1 0.15 -3.197000e+03 -2000.0 -3420.763691 -2973.236309 \n", "2 0.20 -1.900000e+03 -733.0 -2061.872222 -1738.127778 \n", "3 0.25 -9.910000e+02 -6.0 -1129.758391 -852.241609 \n", "4 0.30 -3.310000e+02 201.0 -467.454081 -194.545919 \n", "5 0.35 -9.880808e-13 1000.0 -140.411447 140.411447 \n", "6 0.40 8.262423e-13 1586.0 -141.518446 141.518446 \n", "7 0.45 1.490000e+02 2927.0 6.781233 291.218767 \n", "8 0.50 5.000000e+02 5250.0 354.965774 645.034226 \n", "9 0.55 1.200000e+03 6530.0 1038.506687 1361.493313 \n", "10 0.60 2.318000e+03 10000.0 2103.647002 2532.352998 \n", "11 0.65 4.100000e+03 13300.0 3710.041459 4489.958541 \n", "12 0.70 6.750000e+03 18500.0 6029.711024 7470.288976 \n", "13 0.75 1.052000e+04 24199.0 9551.134146 11488.865854 \n", "14 0.80 1.650000e+04 33500.0 14984.664147 18015.335853 \n", "15 0.85 2.600000e+04 45500.0 23748.752283 28251.247717 \n", "16 0.90 4.144500e+04 63499.0 37939.488460 44950.511540 \n", "\n", " DML Y(1) lower DML Y(1) upper \n", "0 -4835.847966 -3564.152034 \n", "1 -2439.318552 -1560.681448 \n", "2 -1238.098317 -227.901683 \n", "3 -472.478032 460.478032 \n", "4 -343.083750 745.083750 \n", "5 552.392400 1447.607600 \n", "6 1107.286593 2064.713407 \n", "7 2036.542333 3817.457667 \n", "8 4389.402902 6110.597098 \n", "9 5702.335176 7357.664824 \n", "10 8440.555150 11559.444850 \n", "11 11932.311253 14667.688747 \n", "12 16725.272296 20274.727704 \n", "13 22222.986383 26175.013617 \n", "14 30383.148802 36616.851198 \n", "15 42338.762748 48661.237252 \n", "16 59861.956892 67136.043108 \n" ] } ], "source": [ "data_pq = {\"Quantile\": tau_vec,\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_pq = pd.DataFrame(data_pq)\n", "print(df_pq)" ] }, { "cell_type": "code", "execution_count": 11, "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(visible=True); ax2.grid(visible=True)\n", "\n", "ax1.plot(df_pq['Quantile'],df_pq['DML Y(0)'], color='violet', label='Estimated Quantile Y(0)')\n", "ax1.fill_between(df_pq['Quantile'], df_pq['DML Y(0) lower'], df_pq['DML Y(0) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax1.legend()\n", "\n", "ax2.plot(df_pq['Quantile'],df_pq['DML Y(1)'], color='violet', label='Estimated Quantile Y(1)')\n", "ax2.fill_between(df_pq['Quantile'], df_pq['DML Y(1) lower'], df_pq['DML Y(1) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax2.legend()\n", "\n", "\n", "fig.suptitle('Potential Quantiles', fontsize=16)\n", "fig.supxlabel('Quantile')\n", "_ = fig.supylabel('Potential Quantile and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "0a78b537", "metadata": {}, "source": [ "As we are interested in the QTE, we can use the `DoubleMLQTE` object, which internally fits two `DoubleMLPQ` objects for the treatment and control group. The main advantage is to apply this to a list of quantiles and construct uniformly valid confidence intervals for the range of treatment effects." ] }, { "cell_type": "code", "execution_count": 12, "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 % \\\n", "0.10 1210.0 486.438569 2.487467 1.286563e-02 256.597923 \n", "0.15 1230.0 263.748513 4.663533 3.108257e-06 713.062414 \n", "0.20 1211.0 251.948868 4.806531 1.535718e-06 717.189293 \n", "0.25 1000.0 244.841847 4.084269 4.421576e-05 520.118799 \n", "0.30 622.0 255.252133 2.436806 1.481761e-02 121.715013 \n", "0.35 1031.0 274.813682 3.751633 1.756867e-04 492.375081 \n", "0.40 2006.0 320.163566 6.265547 3.715180e-10 1378.490941 \n", "0.45 3329.0 427.336461 7.790115 6.694845e-15 2491.435927 \n", "0.50 4601.0 448.109454 10.267581 9.864741e-25 3722.721609 \n", "0.55 6000.0 588.816752 10.189927 2.199282e-24 4845.940373 \n", "0.60 7040.0 605.739720 11.622153 3.180176e-31 5852.771965 \n", "0.65 9223.0 804.541821 11.463668 2.008266e-30 7646.127006 \n", "0.70 10928.0 859.705581 12.711328 5.115792e-37 9243.008023 \n", "0.75 12410.0 1018.114834 12.189195 3.549109e-34 10414.531594 \n", "0.80 16590.0 1589.396531 10.437924 1.664103e-25 13474.840041 \n", "0.85 19382.0 1622.701413 11.944280 6.955005e-33 16201.563673 \n", "0.90 21550.0 2279.055439 9.455672 3.209546e-21 17083.133421 \n", "\n", " 97.5 % \n", "0.10 2163.402077 \n", "0.15 1746.937586 \n", "0.20 1704.810707 \n", "0.25 1479.881201 \n", "0.30 1122.284987 \n", "0.35 1569.624919 \n", "0.40 2633.509059 \n", "0.45 4166.564073 \n", "0.50 5479.278391 \n", "0.55 7154.059627 \n", "0.60 8227.228035 \n", "0.65 10799.872994 \n", "0.70 12612.991977 \n", "0.75 14405.468406 \n", "0.80 19705.159959 \n", "0.85 22562.436327 \n", "0.90 26016.866579 \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", "np.random.seed(42)\n", "dml_QTE = dml.DoubleMLQTE(data_dml_base,\n", " ml_g=clone(class_learner),\n", " ml_m=clone(class_learner),\n", " quantiles=tau_vec,\n", " score='PQ',\n", " n_folds=n_folds,\n", " normalize_ipw=True,\n", " trimming_rule=\"truncate\",\n", " trimming_threshold=1e-2)\n", "dml_QTE.fit(n_jobs_models=cores_used)\n", "print(dml_QTE)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "91202581", "metadata": {}, "source": [ "For uniformly valid confidence intervals, we still need to apply a bootstrap first. \n", "Let's take a quick look at the QTEs combinded with a confidence interval." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile DML QTE DML QTE lower DML QTE upper\n", "0.10 0.10 1210.0 -163.857765 2583.857765\n", "0.15 0.15 1230.0 485.090025 1974.909975\n", "0.20 0.20 1211.0 499.415988 1922.584012\n", "0.25 0.25 1000.0 308.488485 1691.511515\n", "0.30 0.30 622.0 -98.913485 1342.913485\n", "0.35 0.35 1031.0 254.838457 1807.161543\n", "0.40 0.40 2006.0 1101.755910 2910.244090\n", "0.45 0.45 3329.0 2122.065451 4535.934549\n", "0.50 0.50 4601.0 3335.395889 5866.604111\n", "0.55 0.55 6000.0 4336.993575 7663.006425\n", "0.60 0.60 7040.0 5329.197711 8750.802289\n", "0.65 0.65 9223.0 6950.717130 11495.282870\n", "0.70 0.70 10928.0 8499.917066 13356.082934\n", "0.75 0.75 12410.0 9534.518782 15285.481218\n", "0.80 0.80 16590.0 12101.036945 21078.963055\n", "0.85 0.85 19382.0 14798.973331 23965.026669\n", "0.90 0.90 21550.0 15113.220088 27986.779912\n" ] } ], "source": [ "dml_QTE.bootstrap(n_rep_boot=2000)\n", "ci_QTE = dml_QTE.confint(level=0.95, joint=True)\n", "\n", "data_qte = {\"Quantile\": tau_vec, \"DML QTE\": dml_QTE.coef,\n", " \"DML QTE lower\": ci_QTE[\"2.5 %\"], \"DML QTE upper\": ci_QTE[\"97.5 %\"]}\n", "df_qte = pd.DataFrame(data_qte)\n", "print(df_qte)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "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(visible=True)\n", "\n", "\n", "ax.plot(df_qte['Quantile'],df_qte['DML QTE'], color='violet', label='Estimated QTE')\n", "ax.fill_between(df_qte['Quantile'], df_qte['DML QTE lower'], df_qte['DML QTE upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "\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", "id": "1d0c8d17", "metadata": {}, "source": [ "## Estimating the treatment effect on the Conditional Value a Risk (CVaR)\n", "\n", "Similar to the evaluation of the estimation of quantile treatment effects (QTEs), we can estimate the conditional value at risk ([CVaR](https://de.wikipedia.org/wiki/Conditional_Value_at_Risk)) for given quantiles. Here, we will only focus on treatment effect estimation, but the DoubleML package also allows for estimation of potential CVaRs.\n", "\n", "The estimation of treatment effects can be easily done by adjusting the score in the `DoubleMLQTE` object to `score=\"CVaR\"`, as the estimation is based on the same nuisance elements as QTEs." ] }, { "cell_type": "code", "execution_count": 15, "id": "2147887e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLQTE Object ==================\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", "0.10 9073.195547 1298.264884 6.988709 2.774271e-12 6528.643133 \n", "0.15 10126.150334 1371.682269 7.382286 1.555949e-13 7437.702489 \n", "0.20 14587.388871 1485.887345 9.817291 9.485812e-23 11675.103189 \n", "0.25 16910.113415 1582.022969 10.688918 1.147015e-26 13809.405374 \n", "0.30 14744.693690 1676.606759 8.794366 1.438578e-18 11458.604825 \n", "0.35 16241.221419 1812.325090 8.961539 3.201788e-19 12689.129514 \n", "0.40 18666.064161 1970.604016 9.472255 2.738659e-21 14803.751261 \n", "0.45 12861.546294 2086.920645 6.162930 7.141098e-10 8771.256992 \n", "0.50 13642.272662 2295.693316 5.942550 2.806218e-09 9142.796444 \n", "0.55 14772.077161 2543.121399 5.808640 6.298228e-09 9787.650810 \n", "0.60 15556.468919 2849.994851 5.458420 4.803902e-08 9970.581655 \n", "0.65 16597.988780 3234.712082 5.131211 2.878847e-07 10258.069600 \n", "0.70 17576.743247 3745.384777 4.692907 2.693497e-06 10235.923977 \n", "0.75 18789.942489 4437.655422 4.234205 2.293617e-05 10092.297687 \n", "0.80 19794.747646 5476.213026 3.614678 3.007210e-04 9061.567343 \n", "0.85 19824.888804 7155.563528 2.770556 5.596069e-03 5800.242000 \n", "0.90 20055.810363 10406.538013 1.927232 5.395076e-02 -340.629346 \n", "\n", " 97.5 % \n", "0.10 11617.747961 \n", "0.15 12814.598178 \n", "0.20 17499.674552 \n", "0.25 20010.821457 \n", "0.30 18030.782555 \n", "0.35 19793.313324 \n", "0.40 22528.377060 \n", "0.45 16951.835596 \n", "0.50 18141.748880 \n", "0.55 19756.503511 \n", "0.60 21142.356183 \n", "0.65 22937.907961 \n", "0.70 24917.562518 \n", "0.75 27487.587292 \n", "0.80 30527.927950 \n", "0.85 33849.535609 \n", "0.90 40452.250073 \n" ] } ], "source": [ "\n", "np.random.seed(42)\n", "dml_CVAR = dml.DoubleMLQTE(data_dml_base,\n", " ml_g=clone(reg_learner),\n", " ml_m=clone(class_learner),\n", " quantiles=tau_vec,\n", " score=\"CVaR\",\n", " n_folds=n_folds,\n", " normalize_ipw=True,\n", " trimming_rule=\"truncate\",\n", " trimming_threshold=1e-2)\n", "dml_CVAR.fit(n_jobs_models=cores_used)\n", "print(dml_CVAR)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "f380651e", "metadata": {}, "source": [ "Estimation of the corresponding (uniformly) valid confidence intervals can be done analogously to the quantile treatment effects." ] }, { "cell_type": "code", "execution_count": 16, "id": "e50b4b99", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile DML CVAR DML CVAR lower DML CVAR upper\n", "0.10 0.10 9073.195547 6266.876549 11879.514545\n", "0.15 0.15 10126.150334 7161.132903 13091.167765\n", "0.20 0.20 14587.388871 11375.506659 17799.271083\n", "0.25 0.25 16910.113415 13490.425208 20329.801623\n", "0.30 0.30 14744.693690 11120.553916 18368.833464\n", "0.35 0.35 16241.221419 12323.713986 20158.728852\n", "0.40 0.40 18666.064161 14406.422266 22925.706056\n", "0.45 0.45 12861.546294 8350.475304 17372.617283\n", "0.50 0.50 13642.272662 8679.920335 18604.624988\n", "0.55 0.55 14772.077161 9274.886266 20269.268055\n", "0.60 0.60 15556.468919 9395.942823 21716.995015\n", "0.65 0.65 16597.988780 9605.860992 23590.116569\n", "0.70 0.70 17576.743247 9480.749443 25672.737052\n", "0.75 0.75 18789.942489 9197.541990 28382.342989\n", "0.80 0.80 19794.747646 7957.409328 31632.085965\n", "0.85 0.85 19824.888804 4357.479860 35292.297749\n", "0.90 0.90 20055.810363 -2438.879049 42550.499776\n" ] } ], "source": [ "dml_CVAR.bootstrap(n_rep_boot=2000)\n", "ci_CVAR = dml_CVAR.confint(level=0.95, joint=True)\n", "\n", "data_cvar = {\"Quantile\": tau_vec, \"DML CVAR\": dml_CVAR.coef,\n", " \"DML CVAR lower\": ci_CVAR[\"2.5 %\"], \"DML CVAR upper\": ci_CVAR[\"97.5 %\"]}\n", "df_cvar = pd.DataFrame(data_cvar)\n", "print(df_cvar)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "741d477f", "metadata": {}, "source": [ "Finally, let us take a look at the estimated treatment effects on the CVaR." ] }, { "cell_type": "code", "execution_count": 17, "id": "5b3d590a", "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(visible=True)\n", "\n", "ax.plot(df_cvar['Quantile'],df_cvar['DML CVAR'], color='violet', label='Estimated CVaR Effect')\n", "ax.fill_between(df_cvar['Quantile'], df_cvar['DML CVAR lower'], df_cvar['DML CVAR upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "\n", "plt.legend()\n", "plt.title('Conditional Value at Risk', fontsize=16)\n", "plt.xlabel('Quantile')\n", "_ = plt.ylabel('CVaR Effect and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "7b39b3f4", "metadata": {}, "source": [ "## Estimating local quantile treatment effects (LQTEs)\n", "\n", "If we have an `IIVM` model with a given instrumental variable, we are still able to identify the local quantile treatment effect (LQTE), the quantile treatment effect on compliers. For the 401(k) pension data we can use `e401` as an instrument for participation `p401`. \n", "To fit an `DoubleML` model with an instrument, we have to change the data backend and specify the instrument." ] }, { "cell_type": "code", "execution_count": 18, "id": "3d0d5a8c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLData Object ==================\n", "\n", "------------------ Data summary ------------------\n", "Outcome variable: net_tfa\n", "Treatment variable(s): ['p401']\n", "Covariates: ['age', 'inc', 'educ', 'fsize', 'marr', 'twoearn', 'db', 'pira', 'hown']\n", "Instrument variable(s): ['e401']\n", "No. Observations: 9915\n", "\n", "------------------ DataFrame info ------------------\n", "\n", "RangeIndex: 9915 entries, 0 to 9914\n", "Columns: 14 entries, nifa to hown\n", "dtypes: float32(4), int8(10)\n", "memory usage: 251.9 KB\n", "\n" ] } ], "source": [ "# Initialize DoubleMLData with an instrument\n", "\n", "# Basic model\n", "data_dml_base_iv = dml.DoubleMLData(data,\n", " y_col='net_tfa',\n", " d_cols='p401',\n", " z_cols='e401',\n", " x_cols=features_base)\n", "\n", "print(data_dml_base_iv)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "797ab98e", "metadata": {}, "source": [ "The estimation of local treatment effects can be easily done by adjusting the score in the `DoubleMLQTE` object to `score=\"LPQ\"`." ] }, { "cell_type": "code", "execution_count": 19, "id": "bf543cbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLQTE Object ==================\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", "0.10 2610.0 487.701966 5.351629 8.716595e-08 1654.121711 \n", "0.15 1773.0 357.148790 4.964318 6.894307e-07 1073.001234 \n", "0.20 1398.0 386.526532 3.616828 2.982353e-04 640.421919 \n", "0.25 1435.0 384.956574 3.727693 1.932404e-04 680.498979 \n", "0.30 1400.0 436.977295 3.203828 1.356136e-03 543.540240 \n", "0.35 2500.0 486.877153 5.134765 2.824961e-07 1545.738315 \n", "0.40 3985.0 596.725087 6.678117 2.420316e-11 2815.440320 \n", "0.45 5175.0 739.897240 6.994214 2.667492e-12 3724.828058 \n", "0.50 7239.0 775.751013 9.331602 1.042822e-20 5718.555954 \n", "0.55 9500.0 1109.023955 8.566091 1.070574e-17 7326.352990 \n", "0.60 11750.0 1295.711518 9.068377 1.208034e-19 9210.452091 \n", "0.65 14625.0 1443.080854 10.134567 3.880880e-24 11796.613498 \n", "0.70 16984.0 1576.564577 10.772791 4.627588e-27 13893.990210 \n", "0.75 19758.0 2865.426736 6.895308 5.374821e-12 14141.866798 \n", "0.80 23856.0 2281.099670 10.458114 1.345065e-25 19385.126802 \n", "0.85 27751.0 3151.771741 8.804889 1.309823e-18 21573.640900 \n", "0.90 30645.0 4634.200110 6.612792 3.771390e-11 21562.134687 \n", "\n", " 97.5 % \n", "0.10 3565.878289 \n", "0.15 2472.998766 \n", "0.20 2155.578081 \n", "0.25 2189.501021 \n", "0.30 2256.459760 \n", "0.35 3454.261685 \n", "0.40 5154.559680 \n", "0.45 6625.171942 \n", "0.50 8759.444046 \n", "0.55 11673.647010 \n", "0.60 14289.547909 \n", "0.65 17453.386502 \n", "0.70 20074.009790 \n", "0.75 25374.133202 \n", "0.80 28326.873198 \n", "0.85 33928.359100 \n", "0.90 39727.865313 \n" ] } ], "source": [ "np.random.seed(42)\n", "dml_LQTE = dml.DoubleMLQTE(data_dml_base_iv,\n", " ml_g=clone(class_learner),\n", " ml_m=clone(class_learner),\n", " quantiles=tau_vec,\n", " score=\"LPQ\",\n", " n_folds=n_folds,\n", " normalize_ipw=True,\n", " trimming_rule=\"truncate\",\n", " trimming_threshold=1e-2)\n", "dml_LQTE.fit(n_jobs_models=cores_used)\n", "print(dml_LQTE)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "62bd2981", "metadata": {}, "source": [ "Estimation of the corresponding (uniformly) valid confidence intervals can be done analogously to the quantile treatment effects." ] }, { "cell_type": "code", "execution_count": 20, "id": "f92451b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile DML LQTE DML LQTE lower DML LQTE upper\n", "0.10 0.10 2610.0 1255.980026 3964.019974\n", "0.15 0.15 1773.0 781.438289 2764.561711\n", "0.20 0.20 1398.0 324.876083 2471.123917\n", "0.25 0.25 1435.0 366.234798 2503.765202\n", "0.30 0.30 1400.0 186.808284 2613.191716\n", "0.35 0.35 2500.0 1148.269977 3851.730023\n", "0.40 0.40 3985.0 2328.296228 5641.703772\n", "0.45 0.45 5175.0 3120.803563 7229.196437\n", "0.50 0.50 7239.0 5085.261777 9392.738223\n", "0.55 0.55 9500.0 6420.987220 12579.012780\n", "0.60 0.60 11750.0 8152.681562 15347.318438\n", "0.65 0.65 14625.0 10618.536143 18631.463857\n", "0.70 0.70 16984.0 12606.941724 21361.058276\n", "0.75 0.75 19758.0 11802.639345 27713.360655\n", "0.80 0.80 23856.0 17522.922160 30189.077840\n", "0.85 0.85 27751.0 19000.652071 36501.347929\n", "0.90 0.90 30645.0 17778.946658 43511.053342\n" ] } ], "source": [ "dml_LQTE.bootstrap(n_rep_boot=2000)\n", "ci_LQTE = dml_LQTE.confint(level=0.95, joint=True)\n", "\n", "data_lqte = {\"Quantile\": tau_vec, \"DML LQTE\": dml_LQTE.coef,\n", " \"DML LQTE lower\": ci_LQTE[\"2.5 %\"], \"DML LQTE upper\": ci_LQTE[\"97.5 %\"]}\n", "df_lqte = pd.DataFrame(data_lqte)\n", "print(df_lqte)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "5f26ac09", "metadata": {}, "source": [ "Finally, let us take a look at the estimated local quantile treatment effects." ] }, { "cell_type": "code", "execution_count": 21, "id": "727c5bf0", "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(visible=True)\n", "\n", "ax.plot(df_lqte['Quantile'],df_lqte['DML LQTE'], color='violet', label='Estimated LQTE')\n", "ax.fill_between(df_lqte['Quantile'], df_lqte['DML LQTE lower'], df_lqte['DML LQTE upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "\n", "plt.legend()\n", "plt.title('Local Quantile Treatment Effect', 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": "3.10.8" }, "vscode": { "interpreter": { "hash": "04413e1efd13b5e2aaedccc5facec5686292d28983ef24fa021a12c73bd6369e" } } }, "nbformat": 4, "nbformat_minor": 5 }