diff --git a/econml/tests/test_notebooks.py b/econml/tests/test_notebooks.py
index 9bc13d2d8..8a38cad75 100644
--- a/econml/tests/test_notebooks.py
+++ b/econml/tests/test_notebooks.py
@@ -9,9 +9,10 @@
import traitlets
_nbdir = os.path.join(os.path.dirname(__file__), '..', '..', 'notebooks')
-_notebooks = [path
- for path in os.listdir(_nbdir)
- if path.endswith('.ipynb')]
+_nbsubdirs = ['.', 'CustomerScenarios'] # TODO: add AutoML notebooks
+_notebooks = [
+ os.path.join(subdir, path) for subdir
+ in _nbsubdirs for path in os.listdir(os.path.join(_nbdir, subdir)) if path.endswith('.ipynb')]
@pytest.mark.parametrize("file", _notebooks)
diff --git a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb
new file mode 100644
index 000000000..2d1032261
--- /dev/null
+++ b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb
@@ -0,0 +1,865 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Customer Segmentation -- Estimate Individualized Responses to Incentives\n",
+ "\n",
+ "Nowadays, business decision makers rely on estimating the causal effect of interventions to answer what-if questions about shifts in strategy, such as promoting specific product with discount, adding new features to a website or increasing investment from a sales team. However, rather than learning whether to take action for a specific intervention for all users, people are increasingly interested in understanding the different responses from different users to the two alternatives. Identifying the characteristics of users having the strongest response for the intervention could help make rules to segment the future users into different groups. This can help optimize the policy to use the least resources and get the most profit.\n",
+ "\n",
+ "In this case study, we will use a personalized pricing example to explain how the [EconML](https://aka.ms/econml) library could fit into this problem and provide robust and reliable causal solutions.\n",
+ "\n",
+ "### Summary\n",
+ "\n",
+ "1. [Background](#background)\n",
+ "2. [Data](#data)\n",
+ "3. [Get Causal Effects with EconML](#estimate)\n",
+ "4. [Understand Treatment Effects with EconML](#interpret)\n",
+ "5. [Make Policy Decisions with EconML](#policy)\n",
+ "6. [Conclusions](#conclusion)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Background \n",
+ "\n",
+ "\n",
+ "\n",
+ "The global online media market is growing fast over the years. Media companies are always interested in attracting more users into the market and encouraging them to buy more songs or become members. In this example, we'll consider a scenario where one experiment a media company is running is to give small discount (10%, 20% or 0) to their current users based on their income level in order to boost the likelihood of their purchase. The goal is to understand the **heterogeneous price elasticity of demand** for people with different income level, learning which users would respond most strongly to a small discount. Furthermore, their end goal is to make sure that despite decreasing the price for some consumers, the demand is raised enough to boost the overall revenue.\n",
+ "\n",
+ "EconML’s `DMLCateEstimator` based estimators can be used to take the discount variation in existing data, along with a rich set of user features, to estimate heterogeneous price sensitivities that vary with multiple customer features. Then, the `SingleTreeCateInterpreter` provides a presentation-ready summary of the key features that explain the biggest differences in responsiveness to a discount, and the `SingleTreePolicyInterpreter` recommends a policy on who should receive a discount in order to increase revenue (not only demand), which could help the company to set an optimal price for those users in the future. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Some imports to get us started\n",
+ "# Utilities\n",
+ "import os\n",
+ "import urllib.request\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Generic ML imports\n",
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "from sklearn.ensemble import GradientBoostingRegressor\n",
+ "\n",
+ "# EconML imports\n",
+ "from econml.dml import LinearDMLCateEstimator, ForestDMLCateEstimator\n",
+ "from econml.cate_interpreter import SingleTreeCateInterpreter, SingleTreePolicyInterpreter\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data \n",
+ "\n",
+ "\n",
+ "The dataset* has ~10,000 observations and includes 9 continuous and categorical variables that represent user's characteristics and online behaviour history such as age, log income, previous purchase, previous online time per week, etc. \n",
+ "\n",
+ "We define the following variables:\n",
+ "\n",
+ "Feature Name|Type|Details \n",
+ ":--- |:---|:--- \n",
+ "**account_age** |W| user's account age\n",
+ "**age** |W|user's age\n",
+ "**avg_hours** |W| the average hours user was online per week in the past\n",
+ "**days_visited** |W| the average number of days user visited the website per week in the past\n",
+ "**friend_count** |W| number of friends user connected in the account \n",
+ "**has_membership** |W| whether the user had membership\n",
+ "**is_US** |W| whether the user accesses the website from the US \n",
+ "**songs_purchased** |W| the average songs user purchased per week in the past\n",
+ "**income** |X| user's income\n",
+ "**price** |T| the price user was exposed during the discount season (baseline price * samll discount)\n",
+ "**demand** |Y| songs user purchased during the discount season\n",
+ "\n",
+ "**To protect the privacy of the company, we use the simulated data as an example here. The data is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*\n",
+ "\n",
+ "\n",
+ "The treatment and outcome are generated using the following functions:\n",
+ "$$\n",
+ "T = \n",
+ "\\begin{cases}\n",
+ " 1 & \\text{with } p=0.2, \\\\\n",
+ " 0.9 & \\text{with }p=0.3, & \\text{if income}<1 \\\\\n",
+ " 0.8 & \\text{with }p=0.5, \\\\\n",
+ " \\\\\n",
+ " 1 & \\text{with }p=0.7, \\\\\n",
+ " 0.9 & \\text{with }p=0.2, & \\text{if income}\\ge1 \\\\\n",
+ " 0.8 & \\text{with }p=0.1, \\\\\n",
+ "\\end{cases}\n",
+ "$$\n",
+ "\n",
+ "\n",
+ "\\begin{align}\n",
+ "\\gamma(X) & = -3 - 14 \\cdot \\{\\text{income}<1\\} \\\\\n",
+ "\\beta(X,W) & = 20 + 0.5 \\cdot \\text{avg_hours} + 5 \\cdot \\{\\text{days_visited}>4\\} \\\\\n",
+ "Y &= \\gamma(X) \\cdot T + \\beta(X,W)\n",
+ "\\end{align}\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import the sample pricing data\n",
+ "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/Pricing/pricing_sample.csv\"\n",
+ "train_data = pd.read_csv(file_url)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " account_age age avg_hours days_visited friends_count has_membership \\\n",
+ "0 3 53 1.834234 2 8 1 \n",
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+ "\n",
+ " is_US songs_purchased income price demand \n",
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+ "3 1 7.911926 0.929197 1.0 6.361776 \n",
+ "4 0 7.148967 0.533527 0.8 12.624123 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Data sample\n",
+ "train_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define estimator inputs\n",
+ "Y = train_data[\"demand\"] # outcome of interest\n",
+ "T = train_data[\"price\"] # intervention, or treatment\n",
+ "X = train_data[[\"income\"]] # features\n",
+ "W = train_data.drop(columns=[\"demand\", \"price\", \"income\"]) # confounders"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get test data\n",
+ "X_test = np.linspace(0, 5, 100).reshape(-1, 1)\n",
+ "X_test_data = pd.DataFrame(X_test, columns=[\"income\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Get Causal Effects with EconML \n",
+ "To learn the price elasticity on demand as a function of income, we fit the model as follows:\n",
+ "\n",
+ "\n",
+ "\\begin{align}\n",
+ "log(Y) & = \\theta(X) \\cdot log(T) + f(X,W) + \\epsilon \\\\\n",
+ "log(T) & = g(X,W) + \\eta\n",
+ "\\end{align}\n",
+ "\n",
+ "\n",
+ "where $\\epsilon, \\eta$ are uncorrelated error terms. \n",
+ "\n",
+ "The models we fit here aren't an exact match for the data generation function above, but if they are a good approximation, they will allow us to create a good discount policy. Although the model is misspecified, we hope to see that our `DMLCateEstimator` based estimators can still capture the right trend of $\\theta(X)$ and that the recommended policy beats other baseline policies (such as always giving a discount) on revenue. Because of the mismatch between the data generating process and the model we're fitting, there isn't a single true $\\theta(X)$ (the true elasticity varies with not only X but also T and W), but given how we generate the data above, we can still calculate the range of true $\\theta(X)$ to compare against."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define underlying treatment effect function given DGP\n",
+ "def gamma_fn(X):\n",
+ " return -3 - 14 * (X[\"income\"] < 1)\n",
+ "\n",
+ "def beta_fn(X):\n",
+ " return 20 + 0.5 * (X[\"avg_hours\"]) + 5 * (X[\"days_visited\"] > 4)\n",
+ "\n",
+ "def demand_fn(data, T):\n",
+ " Y = gamma_fn(data) * T + beta_fn(data)\n",
+ " return Y\n",
+ "\n",
+ "def true_te(x, n, stats):\n",
+ " if x < 1:\n",
+ " subdata = train_data[train_data[\"income\"] < 1].sample(n=n, replace=True)\n",
+ " else:\n",
+ " subdata = train_data[train_data[\"income\"] >= 1].sample(n=n, replace=True)\n",
+ " te_array = subdata[\"price\"] * gamma_fn(subdata) / (subdata[\"demand\"])\n",
+ " if stats == \"mean\":\n",
+ " return np.mean(te_array)\n",
+ " elif stats == \"median\":\n",
+ " return np.median(te_array)\n",
+ " elif isinstance(stats, int):\n",
+ " return np.percentile(te_array, stats)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get the estimate and range of true treatment effect\n",
+ "truth_te_estimate = np.apply_along_axis(true_te, 1, X_test, 1000, \"mean\") # estimate\n",
+ "truth_te_upper = np.apply_along_axis(true_te, 1, X_test, 1000, 95) # upper level\n",
+ "truth_te_lower = np.apply_along_axis(true_te, 1, X_test, 1000, 5) # lower level"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Parametric heterogeneity\n",
+ "First of all, we can try to learn a **linear projection of the treatment effect** assuming a polynomial form of $\\theta(X)$. We use the `LinearDMLCateEstimator` estimator. Since we don't have any priors on these models, we use a generic gradient boosting tree estimators to learn the expected price and demand from the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get log_T and log_Y\n",
+ "log_T = np.log(T)\n",
+ "log_Y = np.log(Y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train EconML model\n",
+ "est = LinearDMLCateEstimator(\n",
+ " model_y=GradientBoostingRegressor(),\n",
+ " model_t=GradientBoostingRegressor(),\n",
+ " featurizer=PolynomialFeatures(degree=2, include_bias=False),\n",
+ ")\n",
+ "est.fit(log_Y, log_T, X, W, inference=\"statsmodels\")\n",
+ "# Get treatment effect and its confidence interval\n",
+ "te_pred = est.effect(X_test)\n",
+ "te_pred_interval = est.effect_interval(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Compare the estimate and the truth\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\n",
+ "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\n",
+ "plt.fill_between(\n",
+ " X_test.flatten(),\n",
+ " te_pred_interval[0],\n",
+ " te_pred_interval[1],\n",
+ " alpha=0.2,\n",
+ " label=\"90% Confidence Interval\",\n",
+ ")\n",
+ "plt.fill_between(\n",
+ " X_test.flatten(),\n",
+ " truth_te_lower,\n",
+ " truth_te_upper,\n",
+ " alpha=0.2,\n",
+ " label=\"True Elasticity Range\",\n",
+ ")\n",
+ "plt.xlabel(\"Income\")\n",
+ "plt.ylabel(\"Songs Sales Elasticity\")\n",
+ "plt.title(\"Songs Sales Elasticity vs Income\")\n",
+ "plt.legend(loc=\"lower right\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the plot above, it's clear to see that the true treatment effect is a **nonlinear** function of income, with elasticity around -1.75 when income is smaller than 1 and a small negative value when income is larger than 1. The model fits a quadratic treatment effect, which is not a great fit. But it still captures the overall trend: the elasticity is negative and people are less sensitive to the price change if they have higher income."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "
Coefficient Results
\n",
+ "
\n",
+ "
point_estimate
stderr
zstat
pvalue
ci_lower
ci_upper
\n",
+ "
\n",
+ "
\n",
+ "
income
2.451
0.065
37.659
0.0
2.344
2.558
\n",
+ "
\n",
+ "
\n",
+ "
income^2
-0.443
0.022
-20.517
0.0
-0.479
-0.408
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Intercept Results
\n",
+ "
\n",
+ "
point_estimate
stderr
zstat
pvalue
ci_lower
ci_upper
\n",
+ "
\n",
+ "
\n",
+ "
intercept
-3.04
0.042
-72.165
0.0
-3.109
-2.97
\n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "\n",
+ "\"\"\"\n",
+ " Coefficient Results \n",
+ "===============================================================\n",
+ " point_estimate stderr zstat pvalue ci_lower ci_upper\n",
+ "---------------------------------------------------------------\n",
+ "income 2.451 0.065 37.659 0.0 2.344 2.558\n",
+ "income^2 -0.443 0.022 -20.517 0.0 -0.479 -0.408\n",
+ " Intercept Results \n",
+ "================================================================\n",
+ " point_estimate stderr zstat pvalue ci_lower ci_upper\n",
+ "----------------------------------------------------------------\n",
+ "intercept -3.04 0.042 -72.165 0.0 -3.109 -2.97\n",
+ "----------------------------------------------------------------\n",
+ "\"\"\""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Get the final coefficient and intercept summary\n",
+ "est.summary(feat_name=X.columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`LinearDMLCateEstimator` estimator can also return the summary of the coefficients and intercept for the final model, including point estimates, p-values and confidence intervals. From the table above, we notice that $income$ has positive effect and ${income}^2$ has negative effect, and both of them are statistically significant."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Nonparametric Heterogeneity\n",
+ "Since we already know the true treatment effect function is nonlinear, let us fit another model using `ForestDMLCateEstimator`, which assumes a fully **nonparametric estimation of the treatment effect**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train EconML model\n",
+ "est = ForestDMLCateEstimator(\n",
+ " model_y=GradientBoostingRegressor(), model_t=GradientBoostingRegressor()\n",
+ ")\n",
+ "est.fit(log_Y, log_T, X, W, inference=\"blb\")\n",
+ "# Get treatment effect and its confidence interval\n",
+ "te_pred = est.effect(X_test)\n",
+ "te_pred_interval = est.effect_interval(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Compare the estimate and the truth\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\n",
+ "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\n",
+ "plt.fill_between(\n",
+ " X_test.flatten(),\n",
+ " te_pred_interval[0],\n",
+ " te_pred_interval[1],\n",
+ " alpha=0.2,\n",
+ " label=\"90% Confidence Interval\",\n",
+ ")\n",
+ "plt.fill_between(\n",
+ " X_test.flatten(),\n",
+ " truth_te_lower,\n",
+ " truth_te_upper,\n",
+ " alpha=0.2,\n",
+ " label=\"True Elasticity Range\",\n",
+ ")\n",
+ "plt.xlabel(\"Income\")\n",
+ "plt.ylabel(\"Songs Sales Elasticity\")\n",
+ "plt.title(\"Songs Sales Elasticity vs Income\")\n",
+ "plt.legend(loc=\"lower right\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We notice that this model fits much better than the `LinearDMLCateEstimator`, the 90% confidence interval correctly covers the true treatment effect estimate and captures the variation when income is around 1. Overall, the model shows that people with low income are much more sensitive to the price changes than higher income people."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Understand Treatment Effects with EconML \n",
+ "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n",
+ "\n",
+ "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by the any of the EconML estimators. In the figure below we can see in dark red users respond strongly to the discount and the in white users respond lightly to the discount."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n",
+ "intrp.interpret(est, X_test)\n",
+ "plt.figure(figsize=(25, 5))\n",
+ "intrp.plot(feature_names=X.columns, fontsize=12)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Make Policy Decision with EconML \n",
+ "We want to make policy decisions to maximum the **revenue** instead of the demand. In this scenario,\n",
+ "\n",
+ "\n",
+ "\\begin{align}\n",
+ "Rev & = Y \\cdot T \\\\\n",
+ " & = \\exp^{log(Y)} \\cdot T\\\\\n",
+ " & = \\exp^{(\\theta(X) \\cdot log(T) + f(X,W) + \\epsilon)} \\cdot T \\\\\n",
+ " & = \\exp^{(f(X,W) + \\epsilon)} \\cdot T^{(\\theta(X)+1)}\n",
+ "\\end{align}\n",
+ "\n",
+ "\n",
+ "With the decrease of price, revenue will increase only if $\\theta(X)+1<0$. Thus, we set `sample_treatment_cast=-1` here to learn **what kinds of customers we should give a small discount to maximum the revenue**.\n",
+ "\n",
+ "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. In the figure below we can see the model recommends to give discount for people with income less than $0.985$ and give original price for the others."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=1, min_impurity_decrease=0.001)\n",
+ "intrp.interpret(est, X_test, sample_treatment_costs=-1, treatment_names=[\"Discount\", \"No-Discount\"])\n",
+ "plt.figure(figsize=(25, 5))\n",
+ "intrp.plot(feature_names=X.columns, fontsize=12)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let us compare our policy with other baseline policies! Our model says which customers to give a small discount to, and for this experiment, we will set a discount level of 10% for those users. Because the model is misspecified we would not expect good results with large discounts. Here, because we know the ground truth, we can evaluate the value of this policy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define function to compute revenue\n",
+ "def revenue_fn(data, discount_level1, discount_level2, baseline_T, policy):\n",
+ " policy_price = baseline_T * (1 - discount_level1) * policy + baseline_T * (1 - discount_level2) * (1 - policy)\n",
+ " demand = demand_fn(data, policy_price)\n",
+ " rev = demand * policy_price\n",
+ " return rev"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "policy_dic = {}\n",
+ "# our policy above\n",
+ "policy = intrp.treat(X)\n",
+ "policy_dic[\"Our Policy\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, policy))\n",
+ "\n",
+ "## previous strategy\n",
+ "policy_dic[\"Previous Strategy\"] = np.mean(train_data[\"price\"] * train_data[\"demand\"])\n",
+ "\n",
+ "## give everyone discount\n",
+ "policy_dic[\"Give Everyone Discount\"] = np.mean(revenue_fn(train_data, 0.1, 0, 1, np.ones(len(X))))\n",
+ "\n",
+ "## don't give discount\n",
+ "policy_dic[\"Give No One Discount\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, np.ones(len(X))))\n",
+ "\n",
+ "## follow our policy, but give -10% discount for the group doesn't recommend to give discount\n",
+ "policy_dic[\"Our Policy + Give Negative Discount for No-Discount Group\"] = np.mean(revenue_fn(train_data, -0.1, 0.1, 1, policy))\n",
+ "\n",
+ "## give everyone -10% discount\n",
+ "policy_dic[\"Give Everyone Negative Discount\"] = np.mean(revenue_fn(train_data, -0.1, 0, 1, np.ones(len(X))))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Revenue
\n",
+ "
Rank
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Our Policy
\n",
+ "
14.686241
\n",
+ "
2.0
\n",
+ "
\n",
+ "
\n",
+ "
Previous Strategy
\n",
+ "
14.349342
\n",
+ "
4.0
\n",
+ "
\n",
+ "
\n",
+ "
Give Everyone Discount
\n",
+ "
13.774469
\n",
+ "
6.0
\n",
+ "
\n",
+ "
\n",
+ "
Give No One Discount
\n",
+ "
14.294606
\n",
+ "
5.0
\n",
+ "
\n",
+ "
\n",
+ "
Our Policy + Give Negative Discount for No-Discount Group
\n",
+ "
15.564411
\n",
+ "
1.0
\n",
+ "
\n",
+ "
\n",
+ "
Give Everyone Negative Discount
\n",
+ "
14.612670
\n",
+ "
3.0
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Revenue Rank\n",
+ "Our Policy 14.686241 2.0\n",
+ "Previous Strategy 14.349342 4.0\n",
+ "Give Everyone Discount 13.774469 6.0\n",
+ "Give No One Discount 14.294606 5.0\n",
+ "Our Policy + Give Negative Discount for No-Disc... 15.564411 1.0\n",
+ "Give Everyone Negative Discount 14.612670 3.0"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# get policy summary table\n",
+ "res = pd.DataFrame.from_dict(policy_dic, orient=\"index\", columns=[\"Revenue\"])\n",
+ "res[\"Rank\"] = res[\"Revenue\"].rank(ascending=False)\n",
+ "res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**We beat the baseline policies!** Our policy gets the highest revenue except for the one raising the price for the No-Discount group. That means our currently baseline price is low, but the way we segment the user does help increase the revenue!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Conclusions \n",
+ "\n",
+ "In this notebook, we have demonstrated the power of using EconML to:\n",
+ "\n",
+ "* Estimate the treatment effect correctly even the model is misspecified\n",
+ "* Interpret the resulting individual-level treatment effects\n",
+ "* Make the policy decision beats the previous and baseline policies\n",
+ "\n",
+ "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [docummentation](https://econml.azurewebsites.net/). "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb
new file mode 100644
index 000000000..716a72576
--- /dev/null
+++ b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb
@@ -0,0 +1,770 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "\n",
+ "\n",
+ "# Recommendation A/B Testing: Experimentation with Imperfect Compliance\n",
+ "\n",
+ "An online business would like to test a new feature or offering of their website and learn its effect on downstream revenue. Furthermore, they would like to know which kind of users respond best to the new version. We call the user-specfic effect a **heterogeneous treatment effect**. \n",
+ "\n",
+ "Ideally, the business would run an A/B tests between the old and new versions of the website. However, a direct A/B test might not work because the business cannot force the customers to take the new offering. Measuring the effect in this way will be misleading since not every customer exposed to the new offering will take it.\n",
+ "\n",
+ "The business also cannot look directly at existing data as it will be biased: the users who use the latest website features are most likely the ones who are very engaged on the website and hence spend more on the company's products to begin with. Estimating the effect this way would be overly optimistic."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "In this customer scenario walkthough, we show how tools from the [EconML](https://aka.ms/econml) library can still use a direct A/B test and mitigate these shortcomings.\n",
+ "\n",
+ "### Summary\n",
+ "\n",
+ "1. [Background](#Background)\n",
+ "2. [Data](#Data)\n",
+ "3. [Get Causal Effects with EconML](#Get-Causal-Effects-with-EconML)\n",
+ "4. [Understand Treatment Effects with EconML](#Understand-Treatment-Effects-with-EconML)\n",
+ "5. [Make Policy Decisions with EconML](#Make-Policy-Decisions-with-EconML)\n",
+ "6. [Conclusions](#Conclusions)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "# Background\n",
+ "\n",
+ "\n",
+ "\n",
+ "In this scenario, a travel website would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. \n",
+ "\n",
+ "A direct A/B test is infeasible because the website cannot force users to become members. Likewise, the travel company can’t look directly at existing data, comparing members and non-members, because the customers who chose to become members are likely already more engaged than other users. \n",
+ "\n",
+ "**Solution:** The company had run an earlier experiment to test the value of a new, faster sign-up process. EconML's IV estimators can exploit this experimental nudge towards membership as an instrument that generates random variation in the likelihood of membership. This is known as an **intent-to-treat** setting: the intention is to give a random group of user the \"treatment\" (access to the easier sign-up process), but not not all users will actually take it. \n",
+ "\n",
+ "EconML's `IntentToTreatDRIV` estimator model takes advantage of the fact that not every customer who was offered the easier sign-up became a member to learn the effect of membership rather than the effect of receiving the quick sign-up."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Some imports to get us started\n",
+ "# Utilities\n",
+ "import os\n",
+ "import urllib.request\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Generic ML imports\n",
+ "import lightgbm as lgb\n",
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "\n",
+ "# EconML imports\n",
+ "from econml.ortho_iv import LinearIntentToTreatDRIV\n",
+ "from econml.cate_interpreter import SingleTreeCateInterpreter, \\\n",
+ " SingleTreePolicyInterpreter\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "# Data\n",
+ "\n",
+ "The data* is comprised of:\n",
+ " * Features collected in the 28 days prior to the experiment (denoted by the suffix `_pre`)\n",
+ " * Experiment variables (whether the use was exposed to the easier signup -> the instrument, and whether the user became a member -> the treatment)\n",
+ " * Variables collected in the 28 days after the experiment (denoted by the suffix `_post`).\n",
+ "\n",
+ "Feature Name | Details \n",
+ ":--- |: --- \n",
+ "**days_visited_exp_pre** |#days a user visits the attractions pages \n",
+ "**days_visited_free_pre** | #days a user visits the website through free channels (e.g. domain direct) \n",
+ "**days_visited_fs_pre** | #days a user visits the flights pages \n",
+ "**days_visited_hs_pre** | #days a user visits the hotels pages \n",
+ "**days_visited_rs_pre** | #days a user visits the restaurants pages \n",
+ "**days_visited_vrs_pre** | #days a user visits the vacation rental pages \n",
+ "**locale_en_US** | whether the user access the website from the US \n",
+ "**os_type** | user's operating system (windows, osx, other) \n",
+ "**revenue_pre** | how much the user spent on the website in the pre-period \n",
+ "**easier_signup** | whether the user was exposed to the easier signup process \n",
+ "**became_member** | whether the user became a member \n",
+ "**days_visited_post** | #days a user visits the website in the 28 days after the experiment \n",
+ "\n",
+ "\n",
+ "**To protect the privacy of the travel company's users, the data used in this scenario is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Import the sample AB data\n",
+ "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/RecommendationAB/ab_sample.csv\" \n",
+ "ab_data = pd.read_csv(file_url)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
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+ " days_visited_exp_pre days_visited_free_pre days_visited_fs_pre \\\n",
+ "0 1 9 7 \n",
+ "1 10 25 27 \n",
+ "2 18 14 8 \n",
+ "3 17 0 23 \n",
+ "4 24 9 22 \n",
+ "\n",
+ " days_visited_hs_pre days_visited_rs_pre days_visited_vrs_pre \\\n",
+ "0 25 6 3 \n",
+ "1 10 27 27 \n",
+ "2 4 5 2 \n",
+ "3 2 3 1 \n",
+ "4 2 3 18 \n",
+ "\n",
+ " locale_en_US revenue_pre os_type_osx os_type_windows easier_signup \\\n",
+ "0 1 0.01 0 1 0 \n",
+ "1 0 2.26 0 0 0 \n",
+ "2 1 0.03 0 1 0 \n",
+ "3 1 418.77 0 1 0 \n",
+ "4 1 1.54 0 0 0 \n",
+ "\n",
+ " became_member days_visited_post \n",
+ "0 0 1 \n",
+ "1 0 15 \n",
+ "2 0 17 \n",
+ "3 0 6 \n",
+ "4 0 12 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Data sample\n",
+ "ab_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Define estimator inputs\n",
+ "Z = ab_data['easier_signup'] # nudge, or instrument\n",
+ "T = ab_data['became_member'] # intervention, or treatment\n",
+ "Y = ab_data['days_visited_post'] # outcome of interest\n",
+ "X_data = ab_data.drop(columns=['easier_signup', 'became_member', 'days_visited_post']) # features"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "The data was generated using the following undelying treatment effect function:\n",
+ "\n",
+ "$$\n",
+ "\\text{treatment_effect} = 0.2 + 0.3 \\cdot \\text{days_visited_free_pre} - 0.2 \\cdot \\text{days_visited_hs_pre} + \\text{os_type_osx}\n",
+ "$$\n",
+ "\n",
+ "The interpretation of this is that users who visited the website before the experiment and/or who use an iPhone tend to benefit from the membership program, whereas users who visited the hotels pages tend to be harmed by membership. **This is the relationship we seek to learn from the data.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Define underlying treatment effect function \n",
+ "TE_fn = lambda X: (0.2 + 0.3 * X['days_visited_free_pre'] - 0.2 * X['days_visited_hs_pre'] + X['os_type_osx']).values\n",
+ "true_TE = TE_fn(X_data)\n",
+ "\n",
+ "# Define the true coefficients to compare with\n",
+ "true_coefs = np.zeros(X_data.shape[1])\n",
+ "true_coefs[[1, 3, -2]] = [0.3, -0.2, 1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "# Get Causal Effects with EconML\n",
+ "\n",
+ "To learn a linear projection of the treatment effect, we use the `LinearIntentToTreatDRIV` EconML estimator. For a more flexible treatment effect function, use the `IntentToTreatDRIV` estimator instead. \n",
+ "\n",
+ "The model requires to define some nuissance models (i.e. models we don't really care about but that matter for the analysis): the model for how the outcome $Y$ depends on the features $X$ (`model_Y_X`) and the model for how the treatment $T$ depends on the instrument $Z$ and features $X$ (`model_T_XZ`). Since we don't have any priors on these models, we use generic boosted tree estimators to learn them. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Define nuissance estimators\n",
+ "lgb_T_XZ_params = {\n",
+ " 'objective' : 'binary',\n",
+ " 'metric' : 'auc',\n",
+ " 'learning_rate': 0.1,\n",
+ " 'num_leaves' : 30,\n",
+ " 'max_depth' : 5\n",
+ "}\n",
+ "\n",
+ "lgb_Y_X_params = {\n",
+ " 'metric' : 'rmse',\n",
+ " 'learning_rate': 0.1,\n",
+ " 'num_leaves' : 30,\n",
+ " 'max_depth' : 5\n",
+ "}\n",
+ "model_T_XZ = lgb.LGBMClassifier(**lgb_T_XZ_params)\n",
+ "model_Y_X = lgb.LGBMRegressor(**lgb_Y_X_params)\n",
+ "flexible_model_effect = lgb.LGBMRegressor(**lgb_Y_X_params)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Train EconML model\n",
+ "model = LinearIntentToTreatDRIV(\n",
+ " model_Y_X = model_Y_X,\n",
+ " model_T_XZ = model_T_XZ,\n",
+ " flexible_model_effect = flexible_model_effect,\n",
+ " featurizer = PolynomialFeatures(degree=1, include_bias=False)\n",
+ ")\n",
+ "model.fit(Y.values, T, Z, X_data.values, inference=\"statsmodels\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Compare learned coefficients with true model coefficients\n",
+ "coef_indices = np.arange(model.coef_.shape[0])\n",
+ "# Calculate error bars\n",
+ "coef_error = np.asarray(model.coef__interval()) # 90% confidence interval for coefficients\n",
+ "coef_error[0, :] = model.coef_ - coef_error[0, :]\n",
+ "coef_error[1, :] = coef_error[1, :] - model.coef_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.errorbar(coef_indices, model.coef_, coef_error, fmt=\"o\", label=\"Learned coefficients\\nand 90% confidence interval\")\n",
+ "plt.scatter(coef_indices, true_coefs, color='C1', label=\"True coefficients\")\n",
+ "plt.xticks(coef_indices, X_data.columns, rotation='vertical')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "We notice that the coefficients estimates are pretty close to the true coefficients for the linear treatment effect function. \n",
+ "\n",
+ "We can also use the `model.summary` function to get point estimates, p-values and confidence intervals. From the table below, we notice that only the **days_visited_free_pre**, **days_visited_hs_pre** and **os_type_osx** features are statistically significant (the confidence interval doesn't contain $0$, p-value < 0.05) for the treatment effect. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "