{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise set 9: Double machine learning\n", "\n", "In this exercise set we will once again be working with estimation of conditional average treatment effects assuming selection on observables. However, this time the focus will be more broadly on double machine learning in `econml`, learning how to utilize these methods to get estimates, both assuming a partially linear model (`LinearDML`) and non-parametrically using causal forests (`CausalForestDML`).\n", "\n", "As an example we will examine the age-old question of orange juice price-elasticity, which, as we all know, have haunted economists for millenia. To answer this question we will use a subset of [Dominick's dataset](\n", "https://www.chicagobooth.edu/research/kilts/datasets/dominicks) from the James M. Kilts Center, University of Chicago Booth School of Business. The data is a repeated cross sectional from stores (which we pool), where our main variables of interest are the amount of units sold (outcome) and the price of orange juice (treatment) and median income in the neighborhood (treatment effect heterogeneity). A description of the dataset can be seen [here](https://rdrr.io/cran/bayesm/man/orangeJuice.html). Throughout, we assume selection on observables. This exercise was in part inspired by the `econml` notebooks on causal model selection and double machine learning." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np \n", "import pandas as pd \n", "import os\n", "import urllib.request\n", "\n", "## Ignore warnings\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "# Set seed and plot style\n", "np.random.seed(73)\n", "plt.style.use('seaborn-whitegrid')\n", "\n", "%matplotlib inline" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Getting started\n", "\n", "In this first part of the exercise we will be digging straight into estimating treatment effects using double machine learning. As such, we will be postponing training of models for predicting `Y` and `T` for just a moment, although this is an essential part of double machine learning and should not be neglected." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.1**\n", ">\n", "> Load the data using the following code and verify that you have correctly loaded the `DataFrame` by printing the first 5 rows.\n", ">\n", "> NOTE: The following code will download the file which might take a few seconds dependent on your internet.\n", ">\n", ">>*Hints:*\n", ">> \n", ">> `DataFrame`'s have a method called .head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Import the data\n", "file_name = \"oj_large.csv\"\n", "\n", "if not os.path.isfile(file_name):\n", " print(\"Download file\")\n", " urllib.request.urlretrieve(\"https://msalicedatapublic.blob.core.windows.net/datasets/OrangeJuice/oj_large.csv\", file_name)\n", " \n", "oj_data = pd.read_csv(file_name)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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storebrandweeklogmovefeatpriceAGE60EDUCETHNICINCOMEHHLARGEWORKWOMHVAL150SSTRDISTSSTRVOLCPDIST5CPWVOL5
02tropicana409.01869503.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
12tropicana468.72323103.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
22tropicana478.25322803.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
32tropicana488.98719703.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
42tropicana509.09335703.870.2328650.2489350.1142810.5532050.1039530.3035850.4638872.1101221.1428571.927280.376927
\n", "
" ], "text/plain": [ " store brand week logmove feat price AGE60 EDUC ETHNIC \\\n", "0 2 tropicana 40 9.018695 0 3.87 0.232865 0.248935 0.11428 \n", "1 2 tropicana 46 8.723231 0 3.87 0.232865 0.248935 0.11428 \n", "2 2 tropicana 47 8.253228 0 3.87 0.232865 0.248935 0.11428 \n", "3 2 tropicana 48 8.987197 0 3.87 0.232865 0.248935 0.11428 \n", "4 2 tropicana 50 9.093357 0 3.87 0.232865 0.248935 0.11428 \n", "\n", " INCOME HHLARGE WORKWOM HVAL150 SSTRDIST SSTRVOL CPDIST5 \\\n", "0 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", "1 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", "2 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", "3 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", "4 10.553205 0.103953 0.303585 0.463887 2.110122 1.142857 1.92728 \n", "\n", " CPWVOL5 \n", "0 0.376927 \n", "1 0.376927 \n", "2 0.376927 \n", "3 0.376927 \n", "4 0.376927 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "oj_data.head()\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.2**\n", ">\n", "> The following code subsets the data into different parts corresponding to `X`, `Y`, `W` and `T`, but have been named temporary names. Which is which, and why?\n", ">\n", ">>*Hints:*\n", ">> \n", ">> What's just confounders and what drives heterogeneity?" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [], "source": [ "# Prepare data\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "\n", "scaler = StandardScaler()\n", "\n", "non_categorical_cols = ['feat', 'AGE60', 'EDUC', 'ETHNIC', 'HHLARGE', 'WORKWOM', 'HVAL150', 'SSTRDIST', 'SSTRVOL', 'CPDIST5', 'CPWVOL5']\n", "categorical_cols = ['brand']\n", "\n", "temp_1 = scaler.fit_transform(oj_data[non_categorical_cols].values)\n", "temp_2 = scaler.fit_transform(pd.get_dummies(oj_data[categorical_cols]).values)\n", "# Stacks categorical and non categorical variables together\n", "temp_3 = np.hstack([temp_1, temp_2]) \n", "temp_4 = np.log(oj_data[\"price\"]).values\n", "temp_5 = oj_data['logmove'].values\n", "temp_6 = scaler.fit_transform(oj_data[['INCOME']].values)\n", "\n", "X = # FILL IN\n", "W = # FILL IN\n", "Y = # FILL IN\n", "T = # FILL IN\n", "\n", "XW = np.hstack([X, W])\n", "\n", "Y_train, Y_val, T_train, T_val, X_train, X_val, W_train, W_val, XW_train, XW_val = train_test_split(Y, T, X, W, XW, test_size=.2)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Prepare data\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "\n", "scaler = StandardScaler()\n", "\n", "non_categorical_cols = ['feat', 'AGE60', 'EDUC', 'ETHNIC', 'HHLARGE', 'WORKWOM', 'HVAL150', 'SSTRDIST', 'SSTRVOL', 'CPDIST5', 'CPWVOL5']\n", "categorical_cols = ['brand']\n", "\n", "temp_1 = scaler.fit_transform(oj_data[non_categorical_cols].values)\n", "temp_2 = scaler.fit_transform(pd.get_dummies(oj_data[categorical_cols]).values)\n", "temp_3 = np.hstack([temp_1, temp_2])\n", "\n", "temp_4 = oj_data['logmove'].values\n", "temp_5 = np.log(oj_data[\"price\"]).values\n", "\n", "temp_6 = scaler.fit_transform(oj_data[['INCOME']].values)\n", "\n", "X = temp_6\n", "W = temp_3\n", "Y = temp_4\n", "T = temp_5\n", "\n", "XW = np.hstack([X, W])\n", "\n", "Y_train, Y_val, T_train, T_val, X_train, X_val, W_train, W_val, XW_train, XW_val = train_test_split(Y, T, X, W, XW, test_size=.2)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.3**\n", ">\n", "> Create an instance of a `LinearDML` and fit it to the training data using default input parameters. \n", ">\n", ">>*Hints:*\n", ">> \n", ">> There's an example on [this page](https://econml.azurewebsites.net/spec/estimation/dml.html#when-should-you-use-it)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "from econml.dml import LinearDML\n", "\n", "linear_est = LinearDML()\n", "linear_est.fit(Y_train, T_train, X=X_train, W=W_train)\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.4**\n", ">\n", "> Look at the documentation for `LinearDML`, which can be found [here](https://econml.azurewebsites.net/_autosummary/econml.dml.LinearDML.html#econml.dml.LinearDML). \n", ">\n", "> How are the models for `Y` and `T` created? Does this explain why the data was scaled?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your answer" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "\n", "# cross validated LASSO -- sensitive to scaling, hence standardscaling!\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.5**\n", ">\n", "> Get an estimate of the treatment effect heterogeneity using the `summary` method. Is the sign as expected?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 0.155 0.027 5.821 0.0 0.102 0.207
\n", "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept -2.601 0.026 -98.624 0.0 -2.652 -2.549


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = X' coef_{ij} + cate\\_intercept_{ij}$
Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.
" ], "text/plain": [ "\n", "\"\"\"\n", " Coefficient Results \n", "=======================================================\n", " point_estimate stderr zstat pvalue ci_lower ci_upper\n", "-------------------------------------------------------\n", "X0 0.155 0.027 5.821 0.0 0.102 0.207\n", " CATE Intercept Results \n", "=====================================================================\n", " point_estimate stderr zstat pvalue ci_lower ci_upper\n", "---------------------------------------------------------------------\n", "cate_intercept -2.601 0.026 -98.624 0.0 -2.652 -2.549\n", "---------------------------------------------------------------------\n", "\n", "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", "$\\Theta_{ij}(X) = X' coef_{ij} + cate\\_intercept_{ij}$\n", "Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", "\"\"\"" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "linear_est.summary()\n", "\n", "# Positive slope -- less price sensitive as income increases. Makes sense!\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.6**\n", ">\n", "> Estimate and plot the conditional average treatment effect and the the 95\\% confidence interval with the `LinearDML` model on the following `X_test` data, which generates counterfactual income levels ranging from -1 to 1.\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> There documentation for `LinearDML` can be found on [this page](https://econml.azurewebsites.net/_autosummary/econml.dml.LinearDML.html#econml.dml.LinearDML). \n", ">>\n", ">> Try looking for methods that start with `effect`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (1647766146.py, line 9)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"C:\\Users\\wkg579\\AppData\\Local\\Temp\\ipykernel_17504\\1647766146.py\"\u001b[1;36m, line \u001b[1;32m9\u001b[0m\n\u001b[1;33m te_pred_linear = # FILL IN\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "## Generate test data\n", "min_income = -1\n", "max_income = 1\n", "delta = (1 - (-1)) / 100\n", "X_test = np.arange(min_income, max_income + delta - 0.001, delta).reshape(-1,1)\n", "\n", "# Calculate treatment effect and interval\n", "\n", "te_pred_linear = # FILL IN \n", "te_pred_interval_linear = # FILL IN \n", "\n", "# Plot Orange Juice elasticity as a function of income\n", "\n", "plt.figure(figsize=(10,6))\n", "plt.plot(X_test, te_pred_linear, label=\"Linear model\")\n", "plt.xlabel(r'Scale(Income)')\n", "plt.ylabel('Orange Juice Elasticity')\n", "plt.legend()\n", "plt.title(\"Orange Juice Elasticity vs Income\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "# Generate test data\n", "min_income = -1\n", "max_income = 1\n", "delta = (1 - (-1)) / 100\n", "X_test = np.arange(min_income, max_income + delta - 0.001, delta).reshape(-1,1)\n", "\n", "# Calculate treatment effects\n", "\n", "te_pred_linear = linear_est.effect(X_test)\n", "te_pred_interval_linear = linear_est.effect_interval(X_test, alpha = 0.05)\n", "\n", "# Plot Orange Juice elasticity as a function of income\n", "\n", "plt.figure(figsize=(10,6))\n", "plt.plot(X_test, te_pred_linear, label=\"Linear model\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_linear[0], te_pred_interval_linear[1], alpha=.5)\n", "plt.xlabel(r'Scale(Income)')\n", "plt.ylabel('Orange Juice Elasticity')\n", "plt.legend()\n", "plt.title(\"Orange Juice Elasticity vs Income\")\n", "plt.show()\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.7**\n", ">\n", "> Create an instance of a `CausalForestDML` and fit it to the training data using default input parameters. \n", ">\n", ">>*Hints:*\n", ">> \n", ">> It follows exactly the same recipe as `LinearDML`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "from econml.dml import CausalForestDML\n", "\n", "cf_est = CausalForestDML()\n", "cf_est.fit(Y_train, T_train, X=X_train, W=W_train)\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.8**\n", ">\n", "> Estimate and plot the conditional average treatment effect and the the 95\\% confidence interval with the `CausalForestDML` model `X_test`.\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> It follows exactly the same recipe as `LinearDML`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "te_pred_cf = cf_est.effect(X_test)\n", "te_pred_interval_cf = cf_est.effect_interval(X_test, alpha=0.05)\n", "\n", "# Plot Orange Juice elasticity as a function of income\n", "plt.figure(figsize=(10,6))\n", "\n", "\n", "plt.plot(X_test, te_pred_cf, label=\"Causal forest\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_cf[0], te_pred_interval_cf[1], alpha=.5)\n", "plt.xlabel(r'Scale(Income)')\n", "plt.ylabel('Orange Juice Elasticity')\n", "plt.legend()\n", "plt.title(\"Orange Juice Elasticity vs Income\")\n", "plt.show()\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "As discussed during the lecture, we can use the R-loss to evaluate the fit of the conditional average treatment effect and use this to perform causal model selection.\n", "\n", "This could be done manually, but much like `grf` in R, `econml` also offers automatic tuning with the `tune` method, which we will utilize to tune the causal forest in `CausalForestDML`.\n", "\n", "> **Exercise 1.9**\n", ">\n", "> Create an instance of a `CausalForestDML`, tune it on the training data and then fit it to the training data, all using default input parameters. \n", ">\n", ">>*Hints:*\n", ">> \n", ">> The call to `tune` the model looks exactly like the call to `fit` the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "from econml.dml import CausalForestDML\n", "\n", "cf_tuned_est = CausalForestDML()\n", "cf_tuned_est.tune(Y_train, T_train, X=X_train, W=W_train)\n", "cf_tuned_est.fit(Y_train, T_train, X=X_train, W=W_train)\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.8**\n", ">\n", "> Estimate and plot the conditional average treatment effect and the the 95\\% confidence interval with the tuned `CausalForestDML` model on `X_test`.\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> It follows exactly the same recipe as `LinearDML` and an untuned `CausalForestDML`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "te_pred_tuned_cf = cf_tuned_est.effect(X_test)\n", "te_pred_interval_tuned_cf = cf_tuned_est.effect_interval(X_test, alpha=0.05)\n", "\n", "\n", "# Plot Orange Juice elasticity as a function of income\n", "plt.figure(figsize=(10,6))\n", "\n", "plt.plot(X_test, te_pred_tuned_cf, label=\"Tuned causal forest\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_tuned_cf[0], te_pred_interval_tuned_cf[1], alpha=.5)\n", "plt.xlabel(r'Scale(Income)')\n", "plt.ylabel('Orange Juice Elasticity')\n", "plt.legend()\n", "plt.title(\"Orange Juice Elasticity vs Income\")\n", "plt.show()\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.9**\n", ">\n", "> Plot the conditional average treatment effect and the the 95\\% confidence interval for all three models on `X_test`. Which do your prefer?\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> You can call `plot` and `fill_between` repeatedly before calling `xlabel`" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "# Plot Orange Juice elasticity as a function of income\n", "plt.figure(figsize=(10,6))\n", "\n", "# Linear\n", "plt.plot(X_test, te_pred_linear, label=\"Linear\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_linear[0], te_pred_interval_linear[1], alpha=.5)\n", "# Untuned CF\n", "plt.plot(X_test, te_pred_cf, label=\"Causal forest\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_cf[0], te_pred_interval_cf[1], alpha=.5)\n", "# Tuned CF\n", "plt.plot(X_test, te_pred_tuned_cf, label=\"Tuned causal forest\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_tuned_cf[0], te_pred_interval_tuned_cf[1], alpha=.5)\n", "# Make pretty\n", "plt.xlabel(r'Scale(Income)')\n", "plt.ylabel('Orange Juice Elasticity')\n", "plt.legend()\n", "plt.title(\"Orange Juice Elasticity vs Income\")\n", "plt.show()\n", "\n", "# No clear way to tell which is the correct model.\n", "# Given that the tuned causal forest is tuned using the R-loss, \n", "# I would atleast prefer that over the untuned causal forest\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.10**\n", ">\n", "> `econml` implements a scoring function using the R-loss, called the `Rscorer`. Fit the `Rscorer` to the appropriate data sample.\n", ">\n", "> NOTE: The `Rscorer` needs a model to create residuals. Here we input a `LassoCV`, which is also the default in the double machine learning models. As such we obtain similar residuals.\n", ">\n", ">>*Hints:*\n", ">> \n", ">> Should we use training data or held out data for causal model selection?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import model\n", "from sklearn.linear_model import LassoCV\n", "from econml.score import RScorer\n", "\n", "# Create scorer\n", "scorer = RScorer(model_y=LassoCV(), model_t=LassoCV())\n", "\n", "# FILL IN" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "# Import model\n", "from sklearn.linear_model import LassoCV\n", "from econml.score import RScorer\n", "\n", "# Create scorer\n", "scorer = RScorer(model_y=LassoCV(), model_t=LassoCV())\n", "\n", "scorer.fit(Y_val, T_val, X=X_val, W=W_val)\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 1.11**\n", ">\n", "> Score the models using the `Rscorer`'s `best_model` method. Which model is the preferred one? Is it preferred over a constant average treatment effect?\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> If you're in doubt as to which model the method has selected, you can return all the scores by setting `return_scores = True` and compare the best score to the list\n", ">>\n", ">> The `best_model` method accepts a list of fitted estimators, and the documentation can be seen [here](https://econml.azurewebsites.net/_autosummary/econml.score.RScorer.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "0.0011816363186031298\n", "[0.0009297028164834131, -0.001814529865579395, 0.0011816363186031298]\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "\n", "cate_models = [linear_est, cf_est, cf_tuned_est]\n", "best_model, best_score, score_list = scorer.best_model(cate_models, return_scores = True)\n", "\n", "print(best_model)\n", "print(best_score)\n", "print(score_list)\n", "\n", "# The tuned causal forest is the best model, \n", "# As it has an rscore of higher than 0, it is preferred over a constant average treatment effect. \n", "# Although we note that this is very close to zero\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting treatment and outcome\n", "\n", "Having now looked closer at how to code up estimation of heterogeneous treatment effects using double machine learning estimators, we will start with the task that is predicting both `Y` and `T`, from which we can learn the optimal hyperparameters to pass on to our double machine learning estimators.\n", "\n", "In practice, this is probably where you will be spending most of the time, optimizing features and models to accurately predict treatment and outcome, thus achieving better converge rates.\n", "\n", "We have covered this in both session 3 and 4, where we covered model selection and supervised learning, respectively." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.1**\n", ">\n", "> What covariates should we use to predict `Y` and `T`? Is this part of the train test split we made in exercise 1.1?\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> Look at the struqtural equations in the lecture slides. What enters in the nuisance functions?\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "\n", "# Both Y and T should be predicted using both X and W\n", "# If T was used in predicting Y, we should use a doubly robust estimator, which is not possible for continuous outcomes\n", "# We did perform a horizontal stacking of X and W, called XW, before making our train test split\n", "# We can use XW to create the models\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To make this go by slightly faster, I have pre-selected three models and their respective hyperparametergrids for which to search over. Furthermore, we utilize 2 fold cross validation (matching the default amount of folds in `econml`) and 10 random hyperparameter combinations using `RandomizedSearchCV`, which we covered in session 3. \n", "\n", "Each model should be using `negative_mean_squared_error`, and you should make note of the best performing hyper mean squared error, such that you can compare performance across estimators." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.2**\n", ">\n", "> Create a `Lasso` to predict the outcome, `Y`.\n", ">\n", "> Save the best hyperparameter combination.\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> The best score and best hyperparameter can be found using the methods `best_score_` and `best_param_` respectively.\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (3612000372.py, line 5)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"C:\\Users\\wkg579\\AppData\\Local\\Temp\\ipykernel_3844\\3612000372.py\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m from sklearn # FILL IN\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "from sklearn.pipeline import Pipeline\n", "from sklearn # FILL IN \n", "\n", "pipe_lasso = Pipeline([ \n", " # FILL IN \n", " ]\n", " )\n", "\n", "param_grid = {'lasso__alpha':np.logspace(-5, 3, 10)}\n", "\n", "\n", "rs = RandomizedSearchCV(\n", " # FILL IN\n", ")\n", "\n", "# Fit\n", "# FILL IN\n", "\n", "# Score\n", "# FILL IN" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'lasso__alpha': 0.0005994842503189409}\n", "-0.7015673056724774\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "\n", "from sklearn.model_selection import RandomizedSearchCV\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.linear_model import Lasso\n", "\n", "pipe_lasso = Pipeline([ \n", " ('lasso', Lasso(random_state=1))\n", " ]\n", " )\n", "\n", "param_grid = {'lasso__alpha':np.logspace(-5, 3, 10)}\n", "\n", "\n", "rs = RandomizedSearchCV(estimator=pipe_lasso, \n", " param_distributions=[param_grid], \n", " scoring='neg_mean_squared_error', \n", " cv=2,\n", " n_iter = 10,\n", " n_jobs=-1,\n", " random_state=73)\n", "\n", "\n", "# Fit\n", "rs.fit(XW_train, Y_train)\n", "\n", "# Score\n", "print(rs.best_params_)\n", "print(rs.best_score_)\n", "\n", "y_lasso_best_params = rs.best_params_\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.3**\n", ">\n", "> Create a `RandomForestRegressor` to predict the outcome, `Y`.\n", ">\n", "> Save the best hyperparameter combination.\n", ">\n", ">\n", ">>*Hints:*\n", ">> \n", ">> The best score and best hyperparameter can be found using the methods `best_score_` and `best_param_` respectively.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn # FILL IN\n", "\n", "pipe_forest = Pipeline([ \n", " # FILL IN\n", " ]\n", " )\n", "\n", "\n", "param_grid= [{\n", " 'forest__n_estimators': np.unique(np.logspace(0, 3, 25).astype(int)),\n", " 'forest__max_features': np.arange(0.1, 1.01, 0.1),\n", " 'forest__min_samples_split': np.arange(0.01, 0.2, 0.01),\n", " 'forest__min_samples_leaf': np.arange(2, 50, 2),\n", " 'forest__max_depth': np.unique(np.logspace(1, 4, 20).astype(int))\n", " }]\n", "\n", "rs = RandomizedSearchCV(\n", " # FILL IN\n", ")\n", "\n", "# Fit\n", "# FILL IN\n", "\n", "# Score\n", "# FILL IN" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'forest__n_estimators': 1000, 'forest__min_samples_split': 0.04, 'forest__min_samples_leaf': 46, 'forest__max_features': 0.4, 'forest__max_depth': 61}\n", "-0.6439130531996966\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "pipe_forest = Pipeline([ \n", " ('forest', RandomForestRegressor())\n", " ]\n", " )\n", "\n", "\n", "param_grid= [{\n", " 'forest__n_estimators': np.unique(np.logspace(0, 3, 25).astype(int)),\n", " 'forest__max_features': np.arange(0.1, 1.01, 0.1),\n", " 'forest__min_samples_split': np.arange(0.01, 0.2, 0.01),\n", " 'forest__min_samples_leaf': np.arange(2, 50, 2),\n", " 'forest__max_depth': np.unique(np.logspace(1, 4, 20).astype(int))\n", " }]\n", "\n", "rs = RandomizedSearchCV(estimator=pipe_forest, \n", " param_distributions=param_grid, \n", " scoring='neg_mean_squared_error', \n", " cv=2, \n", " n_iter = 10,\n", " n_jobs=-1,\n", " random_state=73)\n", "\n", "# Fit\n", "rs.fit(XW_train, Y_train)\n", "\n", "# Score\n", "print(rs.best_params_)\n", "print(rs.best_score_)\n", "\n", "\n", "y_forest_best_params = rs.best_params_\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.4**\n", ">\n", "> Create a `HistGradientBoostingRegressor` to predict the outcome, `Y`. \n", ">\n", "> Save the best hyperparameter combination.\n", ">\n", "> NOTE: The `HistGradientBoostingRegressor` is an efficient model which does gradient boosting.\n", ">\n", ">>*Hints:*\n", ">> \n", ">> The best score and best hyperparameter can be found using the methods `best_score_` and `best_param_` respectively.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code\n", "\n", "from sklearn # FILL IN\n", "\n", "pipe_booster = Pipeline([ \n", " # FILL IN\n", " ]\n", " )\n", "\n", "\n", "param_grid= [{\n", " 'booster__min_samples_leaf': np.arange(2, 50, 2),\n", " 'booster__max_depth': np.unique(np.logspace(1, 4, 20).astype(int)),\n", " 'booster__learning_rate':np.arange(0,1.001,0.1)\n", " }]\n", "\n", "rs = RandomizedSearchCV(\n", " # FILL IN\n", ")\n", "\n", "# Fit\n", "# FILL IN\n", "\n", "# Score\n", "# FILL IN\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'booster__min_samples_leaf': 46, 'booster__max_depth': 379, 'booster__learning_rate': 0.30000000000000004}\n", "-0.6227261601961838\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "\n", "from sklearn.ensemble import HistGradientBoostingRegressor\n", "\n", "pipe_booster = Pipeline([ \n", " ('booster', HistGradientBoostingRegressor())\n", " ]\n", " )\n", "\n", "\n", "param_grid= [{\n", " 'booster__min_samples_leaf': np.arange(2, 50, 2),\n", " 'booster__max_depth': np.unique(np.logspace(1, 4, 20).astype(int)),\n", " 'booster__learning_rate':np.arange(0,1.001,0.1)\n", " }]\n", "\n", "rs = RandomizedSearchCV(estimator=pipe_booster, \n", " param_distributions=param_grid, \n", " scoring='neg_mean_squared_error', \n", " cv=2, \n", " n_iter = 10,\n", " n_jobs=-1,\n", " random_state=73)\n", "\n", "# Fit\n", "rs.fit(XW_train, Y_train)\n", "\n", "# Score\n", "print(rs.best_params_)\n", "print(rs.best_score_)\n", "\n", "y_booster_best_params = rs.best_params_\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.5**\n", ">\n", "> Which model best predicts `Y`? Create a model of that type with the best hyperparameter combination" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "\n", "# It's the HistGradientBoostingRegressor (gradient boosted models are very powerful)\n", "# As is has the highest negative mean squared error\n", "\n", "# Remove 'booster__' prefix\n", "y_params = {k.split('__')[-1]:v for k,v in y_booster_best_params.items()}\n", "\n", "# Create model\n", "best_y = HistGradientBoostingRegressor(**y_params)\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.6**\n", "> \n", "> You have now found the model which best predicts `Y`, and now we repeat the same process for `T`\n", "> \n", "> To find the best model to predict `T`, repeat exercise 2.2 through 2.5 but predicting `T`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'lasso__alpha': 7.742636826811278e-05}\n", "-0.03612321520342247\n", "{'forest__n_estimators': 421, 'forest__min_samples_split': 0.060000000000000005, 'forest__min_samples_leaf': 26, 'forest__max_features': 0.7000000000000001, 'forest__max_depth': 20}\n", "-0.03577823168557294\n", "{'booster__min_samples_leaf': 46, 'booster__max_depth': 379, 'booster__learning_rate': 0.30000000000000004}\n", "-0.03669465435871566\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "\n", "pipe_lasso = Pipeline([ \n", " ('lasso', Lasso(random_state=1))\n", " ]\n", " )\n", "\n", "\n", "lambdas = np.logspace(-5, 3, 10)\n", "\n", "rs = RandomizedSearchCV(estimator=pipe_lasso, \n", " param_distributions=[{'lasso__alpha':lambdas}], \n", " scoring='neg_mean_squared_error', \n", " cv=2,\n", " n_iter = 10,\n", " n_jobs=-1,\n", " random_state=73)\n", "\n", "rs.fit(XW_train, T_train)\n", "print(rs.best_params_)\n", "print(rs.best_score_)\n", "\n", "t_lasso_best_params = rs.best_params_\n", "\n", "pipe_forest = Pipeline([ \n", " ('forest', RandomForestRegressor())\n", " ]\n", " )\n", "\n", "\n", "param_grid= [{\n", " 'forest__n_estimators': np.unique(np.logspace(0, 3, 25).astype(int)),\n", " 'forest__max_features': np.arange(0.1, 1.01, 0.1),\n", " 'forest__min_samples_split': np.arange(0.01, 0.2, 0.01),\n", " 'forest__min_samples_leaf': np.arange(2, 50, 2),\n", " 'forest__max_depth': np.unique(np.logspace(1, 4, 20).astype(int))\n", " }]\n", "\n", "rs = RandomizedSearchCV(estimator=pipe_forest, \n", " param_distributions=param_grid, \n", " scoring='neg_mean_squared_error', \n", " cv=2, \n", " n_iter = 10,\n", " n_jobs=-1,\n", " random_state=73)\n", "\n", "rs.fit(XW_train, T_train)\n", "\n", "print(rs.best_params_)\n", "print(rs.best_score_)\n", "\n", "t_forest_best_params = rs.best_params_\n", "\n", "pipe_booster = Pipeline([ \n", " ('booster', HistGradientBoostingRegressor())\n", " ]\n", " )\n", "\n", "\n", "param_grid= [{\n", " 'booster__min_samples_leaf': np.arange(2, 50, 2),\n", " 'booster__max_depth': np.unique(np.logspace(1, 4, 20).astype(int)),\n", " 'booster__learning_rate':np.arange(0,1.001,0.1)\n", " }]\n", "\n", "rs = RandomizedSearchCV(estimator=pipe_booster, \n", " param_distributions=param_grid, \n", " scoring='neg_mean_squared_error', \n", " cv=2, \n", " n_iter = 10,\n", " n_jobs=-1,\n", " random_state=73)\n", "\n", "rs.fit(XW_train, T_train)\n", "\n", "print(rs.best_params_)\n", "print(rs.best_score_)\n", "\n", "t_booster_best_params = rs.best_params_\n", "\n", "# Remove 'booster__' prefix\n", "t_params = {k.split('__')[-1]:v for k,v in t_forest_best_params.items()}\n", "\n", "# Create best model\n", "best_t = RandomForestRegressor(**t_params)\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.7**\n", ">\n", "> Estimate a `LinearDML` as well as an untuned and tuned `CausalForestDML` using the new models for `T` and `Y`.\n", "> \n", "> Plot the conditional average treatment effect and the the 95\\% confidence interval for all three models on `X_test`. Which do your prefer?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### BEGIN SOLUTION\n", "\n", "linear_est_best = LinearDML(model_t=best_t, model_y=best_y)\n", "linear_est_best.fit(Y_train, T_train, X=X_train, W=W_train)\n", "\n", "te_pred_linear_best = linear_est_best.effect(X_test)\n", "te_pred_interval_linear_best = linear_est_best.effect_interval(X_test, alpha=0.05)\n", "\n", "cf_est_best = CausalForestDML()\n", "cf_est_best.fit(Y_train, T_train, X=X_train, W=W_train)\n", "\n", "te_pred_cf_best = cf_est_best.effect(X_test)\n", "te_pred_interval_cf_best = cf_est_best.effect_interval(X_test, alpha=0.05)\n", "\n", "cf_tuned_est_best = CausalForestDML(model_t=best_t, model_y=best_y)\n", "cf_tuned_est_best.tune(Y_train, T_train, X=X_train, W=W_train)\n", "cf_tuned_est_best.fit(Y_train, T_train, X=X_train, W=W_train)\n", "\n", "te_pred_tuned_cf_best = cf_tuned_est_best.effect(X_test)\n", "te_pred_interval_tuned_cf_best = cf_tuned_est_best.effect_interval(X_test, alpha=0.05)\n", "\n", "\n", "# Plot Orange Juice elasticity as a function of income\n", "plt.figure(figsize=(10,6))\n", "\n", "# Linear\n", "plt.plot(X_test, te_pred_linear_best, label=\"Linear\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_linear_best[0], te_pred_interval_linear_best[1], alpha=.5)\n", "# Untuned CF\n", "plt.plot(X_test, te_pred_cf_best, label=\"Causal forest\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_cf_best[0], te_pred_interval_cf_best[1], alpha=.5)\n", "# Tuned CF\n", "plt.plot(X_test, te_pred_tuned_cf_best, label=\"Tuned causal forest\")\n", "plt.fill_between(X_test.flatten(), te_pred_interval_tuned_cf_best[0], te_pred_interval_tuned_cf_best[1], alpha=.5)\n", "# Make pretty\n", "plt.xlabel(r'Scale(Income)')\n", "plt.ylabel('Orange Juice Elasticity')\n", "plt.legend()\n", "plt.title(\"Orange Juice Elasticity vs Income\")\n", "plt.show()\n", "\n", "# No clear way to tell which is the correct model.\n", "# Given that the tuned causal forest is tuned using the R-loss, \n", "# I would atleast prefer that over the untuned causal forest\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise 2.8**\n", ">\n", "> Score the three new models using the `Rscorer`, now predicting the residuals using the models for `T` and `Y. Which model is the preferred one? Is it preferred over a constant average treatment effect?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "0.0008682275612443835\n", "[0.0008682275612443835, -0.0029440261518598465, -0.0017967482149718883]\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "\n", "scorer = RScorer(model_y=best_y, model_t=best_t)\n", "scorer.fit(Y_val, T_val, X=X_val, W=W_val)\n", "\n", "cate_models_extended = [linear_est_best, cf_est_best, cf_tuned_est_best]\n", "best_model_extended, best_score_extended, score_list_extended = scorer.best_model(cate_models_extended, return_scores = True)\n", "\n", "print(best_model_extended)\n", "print(best_score_extended)\n", "print(score_list_extended)\n", "\n", "# This time linear dml preferred, although again VERY close to 0\n", "# - Would be interesting to run again with more folds at all steps and monte carlo iterations\n", "# - Also more exhaustive model search for Y and Y\n", "# Qualitatively, the conclusion is the same:\n", "# More price sensitive when less income\n", "\n", "### END SOLUTION" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Finishing thoughts\n", "\n", "Today we have examined double machine learning and how to implement these models. \n", "\n", "In practice, you should spend more time creating the models for `T` and `Y`, as they are quintessential in double machine learning. You should also increase the amount of folds in cross validation, as this increases the amount of data to train on when creating residuals. Furthermore, both the models and `Rscorer` support monte carlo iterations, `mc_iters`, which allows you to repeat the same process again with different splits, thus creating less noisy estimates of the residuals. We have not done this today as the running time of these models quite quickly becomes too long for an exercise set.\n", "\n", "If you wish to examine linear treatment heterogeneity, you should also look into the input parameters `featurizer` and `treatment_featurizer`, which enables quick and easy interactions of covariates and treatments, possibly in combination with a `SparseLinearDML`. See e.g. [this notebook](https://github.com/microsoft/EconML/blob/main/notebooks/Treatment%20Featurization%20Examples.ipynb)\n", "\n", "All of the `econml` functionality that we went through last week can also still be used with our models, e.g. explainability, CATE interpreters and policy learning.\n", "\n", "Additionally, you should be aware that `econml` has a folder with notebook examples on their [GitHub](https://github.com/microsoft/EconML/tree/main/notebooks), where you can see the many different possibilities." ] } ], "metadata": { "kernelspec": { "display_name": "vive_env", "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": "3.10.4" }, "vscode": { "interpreter": { "hash": "50f1b660881291c5d31ad4d40c48915b3ef9364cc881d538585b11a7eb841304" } } }, "nbformat": 4, "nbformat_minor": 4 }