{ "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 5.490488e+04 6.352250e+04 1.115297e+05 10.344505 24774.289062 \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, "id": "683857f3", "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, verbose=-1, n_jobs=1)\n", "reg_learner = LGBMRegressor(n_estimators=300, learning_rate=0.05, num_leaves=10, verbose=-1, n_jobs=1)" ] }, { "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, "id": "0392bf0b", "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[0]\n", " PQ_1[idx_tau] = dml_PQ_1.coef[0]\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", "\n", "------------------ Score & Algorithm ------------------\n", "Score function: PQ\n", "\n", "------------------ Machine Learner ------------------\n", "Learner ml_g: LGBMClassifier(learning_rate=0.05, n_estimators=300, n_jobs=1, num_leaves=10,\n", " verbose=-1)\n", "Learner ml_m: LGBMClassifier(learning_rate=0.05, n_estimators=300, n_jobs=1, num_leaves=10,\n", " verbose=-1)\n", "Out-of-sample Performance:\n", "Classification:\n", "Learner ml_g Log Loss: [[0.33168943]]\n", "Learner ml_m Log Loss: [[0.57751232]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", "No. repeated sample splits: 1\n", "\n", "------------------ Fit Summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", "e401 63499.0 1888.230821 33.628834 6.358690e-248 59798.135596 \n", "\n", " 97.5 % \n", "e401 67199.864404 \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, "id": "2c712b87", "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 -5.320000e+03 -3.978000e+03 -5708.039154 -4931.960846 \n", "1 0.15 -3.200000e+03 -2.000000e+03 -3422.338855 -2977.661145 \n", "2 0.20 -2.000000e+03 -7.270000e+02 -2167.843296 -1832.156704 \n", "3 0.25 -1.000000e+03 -1.077527e-12 -1144.513658 -855.486342 \n", "4 0.30 -3.480000e+02 2.800000e+02 -485.671633 -210.328367 \n", "5 0.35 -1.063428e-12 1.000000e+03 -140.524215 140.524215 \n", "6 0.40 1.512657e-12 1.425000e+03 -141.848578 141.848578 \n", "7 0.45 1.000000e+02 3.648000e+03 -43.070797 243.070797 \n", "8 0.50 5.000000e+02 4.450000e+03 354.370165 645.629835 \n", "9 0.55 1.200000e+03 6.450000e+03 1040.412406 1359.587594 \n", "10 0.60 2.398000e+03 9.500000e+03 2175.942629 2620.057371 \n", "11 0.65 4.100000e+03 1.370000e+04 3712.348338 4487.651662 \n", "12 0.70 6.499000e+03 1.790000e+04 5804.239845 7193.760155 \n", "13 0.75 1.074700e+04 2.330000e+04 9773.014721 11720.985279 \n", "14 0.80 1.600000e+04 3.320000e+04 14530.490898 17469.509102 \n", "15 0.85 2.564800e+04 4.496300e+04 23368.042583 27927.957417 \n", "16 0.90 4.190000e+04 6.349900e+04 38348.193833 45451.806167 \n", "\n", " DML Y(1) lower DML Y(1) upper \n", "0 -4584.205245 -3371.794755 \n", "1 -2445.618722 -1554.381278 \n", "2 -1275.916921 -178.083079 \n", "3 -412.409390 412.409390 \n", "4 -179.647750 739.647750 \n", "5 413.167035 1586.832965 \n", "6 843.522605 2006.477395 \n", "7 2760.562866 4535.437134 \n", "8 3598.463208 5301.536792 \n", "9 5494.106116 7405.893884 \n", "10 8208.841712 10791.158288 \n", "11 11850.483855 15549.516145 \n", "12 16249.111783 19550.888217 \n", "13 20956.028868 25643.971132 \n", "14 30186.784841 36213.215159 \n", "15 42143.373235 47782.626765 \n", "16 59798.135596 67199.864404 \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, "id": "eb27251f", "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, "id": "34ee8d9a", "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 1211.0 252.052811 4.804549 1.551010e-06 716.985569 \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 3.715179e-10 1378.490941 \n", "0.45 3329.0 427.336461 7.790115 6.661338e-15 2491.435927 \n", "0.50 4601.0 448.109454 10.267581 0.000000e+00 3722.721609 \n", "0.55 6000.0 590.561309 10.159826 0.000000e+00 4842.521104 \n", "0.60 7040.0 605.739720 11.622153 0.000000e+00 5852.771965 \n", "0.65 9243.0 809.469379 11.418591 0.000000e+00 7656.469170 \n", "0.70 10928.0 859.705581 12.711328 0.000000e+00 9243.008023 \n", "0.75 12410.0 1018.114834 12.189195 0.000000e+00 10414.531594 \n", "0.80 16590.0 1589.396531 10.437924 0.000000e+00 13474.840041 \n", "0.85 19382.0 1622.701413 11.944280 0.000000e+00 16201.563673 \n", "0.90 21550.0 2279.055439 9.455672 0.000000e+00 17083.133421 \n", "\n", " 97.5 % \n", "0.10 2163.402077 \n", "0.15 1746.937586 \n", "0.20 1705.014431 \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 7157.478896 \n", "0.60 8227.228035 \n", "0.65 10829.530830 \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, "id": "fc1adee3", "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 -164.794119 2584.794119\n", "0.15 0.15 1230.0 484.582331 1975.417669\n", "0.20 0.20 1211.0 498.637240 1923.362760\n", "0.25 0.25 1000.0 308.017185 1691.982815\n", "0.30 0.30 622.0 -99.404824 1343.404824\n", "0.35 0.35 1031.0 254.309464 1807.690536\n", "0.40 0.40 2006.0 1101.139622 2910.860378\n", "0.45 0.45 3329.0 2121.242864 4536.757136\n", "0.50 0.50 4601.0 3334.533316 5867.466684\n", "0.55 0.55 6000.0 4330.929607 7669.070393\n", "0.60 0.60 7040.0 5328.031712 8751.968288\n", "0.65 0.65 9243.0 6955.241973 11530.758027\n", "0.70 0.70 10928.0 8498.262204 13357.737796\n", "0.75 0.75 12410.0 9532.558996 15287.441004\n", "0.80 0.80 16590.0 12097.977489 21082.022511\n", "0.85 0.85 19382.0 14795.849766 23968.150234\n", "0.90 0.90 21550.0 15108.833096 27991.166904\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, "id": "9af19bbc", "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(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 9045.771876 1297.433473 6.972051 3.123501e-12 6502.848997 \n", "0.15 10126.150435 1371.682315 7.382285 1.556533e-13 7437.702500 \n", "0.20 14588.483642 1485.887212 9.818029 0.000000e+00 11676.198221 \n", "0.25 16910.113005 1582.022950 10.688918 0.000000e+00 13809.405000 \n", "0.30 14744.693345 1676.606733 8.794366 0.000000e+00 11458.604532 \n", "0.35 16241.220211 1812.325046 8.961538 0.000000e+00 12689.128393 \n", "0.40 18666.064164 1970.604110 9.472255 0.000000e+00 14803.751081 \n", "0.45 12861.546438 2086.920695 6.162930 7.141101e-10 8771.257037 \n", "0.50 13642.272643 2295.693359 5.942550 2.806220e-09 9142.796340 \n", "0.55 14772.076119 2543.121297 5.808640 6.298235e-09 9787.649969 \n", "0.60 15556.470365 2849.994864 5.458420 4.803889e-08 9970.583076 \n", "0.65 16618.635757 3235.158198 5.136885 2.793297e-07 10277.842205 \n", "0.70 17576.744211 3745.384883 4.692907 2.693495e-06 10235.924732 \n", "0.75 18773.183814 4438.392472 4.229726 2.339762e-05 10074.094420 \n", "0.80 19798.478399 5475.067639 3.616116 2.990567e-04 9067.543014 \n", "0.85 19824.886611 7155.563760 2.770556 5.596076e-03 5800.239352 \n", "0.90 20055.810916 10406.538282 1.927232 5.395076e-02 -340.629319 \n", "\n", " 97.5 % \n", "0.10 11588.694755 \n", "0.15 12814.598371 \n", "0.20 17500.769063 \n", "0.25 20010.821009 \n", "0.30 18030.782159 \n", "0.35 19793.312030 \n", "0.40 22528.377246 \n", "0.45 16951.835839 \n", "0.50 18141.748945 \n", "0.55 19756.502268 \n", "0.60 21142.357654 \n", "0.65 22959.429309 \n", "0.70 24917.563690 \n", "0.75 27472.273208 \n", "0.80 30529.413784 \n", "0.85 33849.533871 \n", "0.90 40452.251152 \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 9045.771876 6241.250083 11850.293669\n", "0.15 0.15 10126.150435 7161.132941 13091.167930\n", "0.20 0.20 14588.483642 11376.601757 17800.365527\n", "0.25 0.25 16910.113005 13490.424879 20329.801130\n", "0.30 0.30 14744.693345 11120.553672 18368.833018\n", "0.35 0.35 16241.220211 12323.712921 20158.727501\n", "0.40 0.40 18666.064164 14406.422119 22925.706208\n", "0.45 0.45 12861.546438 8350.475395 17372.617481\n", "0.50 0.50 13642.272643 8679.920283 18604.625002\n", "0.55 0.55 14772.076119 9274.885513 20269.266724\n", "0.60 0.60 15556.470365 9395.944317 21716.996413\n", "0.65 0.65 16618.635757 9625.543734 23611.727780\n", "0.70 0.70 17576.744211 9480.750275 25672.738147\n", "0.75 0.75 18773.183814 9179.190231 28367.177397\n", "0.80 0.80 19798.478399 7963.616086 31633.340712\n", "0.85 0.85 19824.886611 4357.477354 35292.295868\n", "0.90 0.90 20055.810916 -2438.878802 42550.500635\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 2610.0 487.820993 5.350323 8.779727e-08 1653.888423 \n", "0.15 1773.0 357.148835 4.964317 6.894329e-07 1073.001145 \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 2.420308e-11 2815.440320 \n", "0.45 5175.0 739.887778 6.994304 2.665868e-12 3724.846602 \n", "0.50 7239.0 775.751013 9.331602 0.000000e+00 5718.555954 \n", "0.55 9500.0 1109.046788 8.565915 0.000000e+00 7326.308239 \n", "0.60 11750.0 1295.711518 9.068377 0.000000e+00 9210.452091 \n", "0.65 14625.0 1443.080854 10.134567 0.000000e+00 11796.613498 \n", "0.70 16984.0 1576.564577 10.772791 0.000000e+00 13893.990210 \n", "0.75 19758.0 2865.426736 6.895308 5.374812e-12 14141.866798 \n", "0.80 23856.0 2281.099670 10.458114 0.000000e+00 19385.126802 \n", "0.85 27066.0 2920.063312 9.268977 0.000000e+00 21342.781076 \n", "0.90 30645.0 4634.200110 6.612792 3.771383e-11 21562.134687 \n", "\n", " 97.5 % \n", "0.10 3566.111577 \n", "0.15 2472.998855 \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 6625.153398 \n", "0.50 8759.444046 \n", "0.55 11673.691761 \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 32789.218924 \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 1258.618753 3961.381247\n", "0.15 0.15 1773.0 783.611995 2762.388005\n", "0.20 0.20 1398.0 327.228725 2468.771275\n", "0.25 0.25 1435.0 368.577884 2501.422116\n", "0.30 0.30 1400.0 189.468001 2610.531999\n", "0.35 0.35 2500.0 1151.233415 3848.766585\n", "0.40 0.40 3985.0 2331.928269 5638.071731\n", "0.45 0.45 5175.0 3125.333250 7224.666750\n", "0.50 0.50 7239.0 5089.983482 9388.016518\n", "0.55 0.55 9500.0 6427.674181 12572.325819\n", "0.60 0.60 11750.0 8160.568071 15339.431929\n", "0.65 0.65 14625.0 10627.319634 18622.680366\n", "0.70 0.70 16984.0 12616.537681 21351.462319\n", "0.75 0.75 19758.0 11820.080121 27695.919879\n", "0.80 0.80 23856.0 17536.806356 30175.193644\n", "0.85 0.85 27066.0 18976.723689 35155.276311\n", "0.90 0.90 30645.0 17807.153293 43482.846707\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_dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.8" } }, "nbformat": 4, "nbformat_minor": 5 }