From cce3ded03c2e72234b46e300666bda291428f2a0 Mon Sep 17 00:00:00 2001
From: Keith Battocchi <kebatt@microsoft.com>
Date: Wed, 17 Jul 2024 18:45:34 -0400
Subject: [PATCH] Fix E731

Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
---
 ...utomated Machine Learning For EconML.ipynb |    6 +-
 ...nd Orthogonal Random Forest Examples.ipynb |   18 +-
 ...sal Model Selection with the RScorer.ipynb |   18 +-
 notebooks/Choosing First Stage Models.ipynb   |   21 +-
 ... A Software Company - EconML + DoWhy.ipynb |    3 +-
 ...nt Attribution at A Software Company.ipynb |  675 +++++------
 ...line Travel Company - EconML + DoWhy.ipynb |    3 +-
 ... Testing at An Online Travel Company.ipynb |  575 ++++-----
 .../Double Machine Learning Examples.ipynb    |   18 +-
 notebooks/ForestLearners Basic Example.ipynb  |    9 +-
 notebooks/Generalized Random Forests.ipynb    |   17 +-
 notebooks/Interpretability with SHAP.ipynb    |    7 +-
 notebooks/Metalearners Examples.ipynb         | 1028 +++++++++--------
 notebooks/OrthoIV and DRIV Examples.ipynb     |   12 +-
 pyproject.toml                                |    1 -
 15 files changed, 1219 insertions(+), 1192 deletions(-)

diff --git a/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb b/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb
index 3c7a34f7..42adc2f1 100644
--- a/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb	
+++ b/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb	
@@ -310,11 +310,13 @@
     "# Outcome support\n",
     "support_Y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n",
     "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def epsilon_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "# Treatment support\n",
     "support_T = support_Y\n",
     "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
-    "eta_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def eta_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "\n",
     "# Generate controls, covariates, treatments and outcomes\n",
     "W = np.random.normal(0, 1, size=(n, n_w))\n",
diff --git a/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb b/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb
index 673c669e..26e37fd8 100644
--- a/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb	
+++ b/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb	
@@ -125,11 +125,13 @@
     "# Outcome support\n",
     "support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\n",
     "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def epsilon_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "# Treatment support \n",
     "support_T = support_Y\n",
     "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
-    "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \n",
+    "def eta_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n) \n",
     "\n",
     "# Generate controls, covariates, treatments and outcomes\n",
     "W = np.random.normal(0, 1, size=(n, n_w))\n",
@@ -552,11 +554,13 @@
     "# Outcome support\n",
     "support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\n",
     "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def epsilon_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "# Treatment support\n",
     "support_T = support_Y\n",
     "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
-    "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \n",
+    "def eta_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n) \n",
     "\n",
     "# Generate controls, covariates, treatments and outcomes\n",
     "W = np.random.normal(0, 1, size=(n, n_w))\n",
@@ -895,11 +899,13 @@
     "    # Outcome support\n",
     "    support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\n",
     "    coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "    epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "    def epsilon_sample(n):\n",
+    "        return np.random.uniform(-1, 1, size=n)\n",
     "    # Treatment support \n",
     "    support_T = support_Y\n",
     "    coefs_T = np.random.uniform(0, 1, size=(support_size, n_treatments))\n",
-    "    eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \n",
+    "    def eta_sample(n):\n",
+    "        return np.random.uniform(-1, 1, size=n) \n",
     "    # Generate controls, covariates, treatments and outcomes\n",
     "    W = np.random.normal(0, 1, size=(n, n_w))\n",
     "    X = np.random.uniform(0, 1, size=(n, n_x))\n",
diff --git a/notebooks/Causal Model Selection with the RScorer.ipynb b/notebooks/Causal Model Selection with the RScorer.ipynb
index 3caf4095..c964252f 100644
--- a/notebooks/Causal Model Selection with the RScorer.ipynb	
+++ b/notebooks/Causal Model Selection with the RScorer.ipynb	
@@ -119,11 +119,13 @@
     "# Outcome support\n",
     "support_Y = np.random.choice(range(n_x), size=support_size, replace=False)\n",
     "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "epsilon_sample = lambda n:np.random.uniform(-1, 1, size=n)\n",
+    "def epsilon_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "# Treatment support\n",
     "support_T = support_Y\n",
     "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
-    "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \n",
+    "def eta_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n) \n",
     "\n",
     "# Generate controls, covariates, treatments and outcomes\n",
     "X = np.random.uniform(0, 1, size=(n, n_x))\n",
@@ -155,8 +157,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "reg = lambda: RandomForestRegressor(min_samples_leaf=10)\n",
-    "clf = lambda: RandomForestClassifier(min_samples_leaf=10)"
+    "def reg():\n",
+    "    return RandomForestRegressor(min_samples_leaf=10)\n",
+    "def clf():\n",
+    "    return RandomForestClassifier(min_samples_leaf=10)"
    ]
   },
   {
@@ -421,8 +425,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "reg = lambda: RandomForestRegressor(min_samples_leaf=10, random_state=123)\n",
-    "clf = lambda: RandomForestClassifier(min_samples_leaf=10, random_state=123)"
+    "def reg():\n",
+    "    return RandomForestRegressor(min_samples_leaf=10, random_state=123)\n",
+    "def clf():\n",
+    "    return RandomForestClassifier(min_samples_leaf=10, random_state=123)"
    ]
   },
   {
diff --git a/notebooks/Choosing First Stage Models.ipynb b/notebooks/Choosing First Stage Models.ipynb
index e66c6a6b..7388e8c3 100644
--- a/notebooks/Choosing First Stage Models.ipynb	
+++ b/notebooks/Choosing First Stage Models.ipynb	
@@ -83,7 +83,8 @@
     "p = 10\n",
     "W = np.random.uniform(size=(n, p))\n",
     "X = np.random.uniform(size=(n, 1))\n",
-    "true_effect = lambda x: x[:, 0] ** 2\n",
+    "def true_effect(x):\n",
+    "    return x[:, 0] ** 2\n",
     "T = W[:, 0] + W[:, 1] ** 2 + np.random.uniform(-1, 1, size=n)\n",
     "Y = (\n",
     "    true_effect(X) * T\n",
@@ -158,12 +159,8 @@
     }
    ],
    "source": [
-    "first_stage = lambda: GridSearchCV(\n",
-    "    estimator=GradientBoostingRegressor(),\n",
-    "    param_grid={\"max_depth\": [3, 5, None], \"n_estimators\": (50, 100, 200)},\n",
-    "    cv=2,\n",
-    "    n_jobs=-1,\n",
-    ")\n",
+    "def first_stage():\n",
+    "    return GridSearchCV(estimator=GradientBoostingRegressor(), param_grid={\"max_depth\": [3, 5, None], \"n_estimators\": (50, 100, 200)}, cv=2, n_jobs=-1)\n",
     "est = LinearDML(\n",
     "    model_y=first_stage(),\n",
     "    model_t=first_stage(),\n",
@@ -381,14 +378,8 @@
     }
    ],
    "source": [
-    "first_stage = lambda: GridSearchCVList(\n",
-    "    [Lasso(max_iter=10000), GradientBoostingRegressor()],\n",
-    "    param_grid_list=[\n",
-    "        {\"alpha\": [0.001, 0.01, 0.1, 1, 10]},\n",
-    "        {\"max_depth\": [3, 5, None], \"n_estimators\": [50, 100, 200]},\n",
-    "    ],\n",
-    "    cv=2,\n",
-    ")"
+    "def first_stage():\n",
+    "    return GridSearchCVList([Lasso(max_iter=10000), GradientBoostingRegressor()], param_grid_list=[{\"alpha\": [0.001, 0.01, 0.1, 1, 10]}, {\"max_depth\": [3, 5, None], \"n_estimators\": [50, 100, 200]}], cv=2)"
    ]
   },
   {
diff --git a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb
index 710f637a..34a04e9c 100644
--- a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb	
@@ -397,7 +397,8 @@
    "outputs": [],
    "source": [
     "# Define underlying treatment effect function\n",
-    "TE_fn = lambda X: np.hstack([5000 + 2 / 100 * X, 5 / 100 * X])\n",
+    "def TE_fn(X):\n",
+    "    return np.hstack([5000 + 2 / 100 * X, 5 / 100 * X])\n",
     "true_TE = TE_fn(X)\n",
     "\n",
     "# Define true coefficients for the three treatments\n",
diff --git a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb
index 2f8d05b5..0e444e64 100644
--- a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb	
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "<img src=\"https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Attribution.png\" width=\"400\">\n",
     "\n",
@@ -12,11 +13,11 @@
     "In an ideal world, the startup would run several randomized experiments where each customer would receive a random assortment of investments. However, this can be logistically prohibitive or strategically unsound: the startup might not have the resources to design such experiments or they might not want to risk losing out on big opportunities due to lack of incentives.\n",
     "\n",
     "In this customer scenario walkthrough, we show how tools from the [EconML](https://aka.ms/econml) library can use historical investment data to learn the effects of multiple investments."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Summary\n",
     "\n",
@@ -26,11 +27,11 @@
     "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)"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Background\n",
     "\n",
@@ -43,12 +44,13 @@
     "The startup faces two challenges: 1) the dataset is biased because historically the larger customers received the most incentives and 2) the observed outcome combines effects from two different investments. Thus, they need a causal model that can accommodate multiple concurrent interventions. \n",
     "\n",
     "**Solution:** EconML’s `Doubly Robust Learner` model jointly estimates the effects of multiple discrete treatments. The model uses flexible functions of observed customer features to filter out spurious correlations in existing data and deliver the causal effect of each intervention on revenue.\n"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Some imports to get us started\r\n",
     "import warnings\r\n",
@@ -69,12 +71,11 @@
     "import seaborn as sns\r\n",
     "\r\n",
     "%matplotlib inline"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Data\n",
     "\n",
@@ -99,46 +100,26 @@
     "**Revenue** | Y | \\\\$ Revenue from customer given by the amount of software purchased\n",
     "\n",
     "**To protect the privacy of the startup's customers, 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.*"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Import the sample multi-attribution data\n",
     "file_url = \"https://msalicedatapublic.z5.web.core.windows.net/datasets/ROI/multi_attribution_sample.csv\"\n",
     "multi_data = pd.read_csv(file_url)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "source": [
-    "# Data sample\n",
-    "multi_data.head()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "   Global Flag  Major Flag  SMC Flag  Commercial Flag  IT Spend  \\\n",
-       "0            1           0         1                0     45537   \n",
-       "1            0           0         1                1     20842   \n",
-       "2            0           0         0                1     82171   \n",
-       "3            0           0         0                0     30288   \n",
-       "4            0           0         1                0     25930   \n",
-       "\n",
-       "   Employee Count  PC Count    Size  Tech Support  Discount      Revenue  \n",
-       "0              26        26  152205             0         1  17688.36300  \n",
-       "1             107        70  159038             0         1  14981.43559  \n",
-       "2              10         7  264935             1         1  32917.13894  \n",
-       "3              40        39   77522             1         1  14773.76855  \n",
-       "4              37        43   91446             1         1  17098.69823  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -245,17 +226,38 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "   Global Flag  Major Flag  SMC Flag  Commercial Flag  IT Spend  \\\n",
+       "0            1           0         1                0     45537   \n",
+       "1            0           0         1                1     20842   \n",
+       "2            0           0         0                1     82171   \n",
+       "3            0           0         0                0     30288   \n",
+       "4            0           0         1                0     25930   \n",
+       "\n",
+       "   Employee Count  PC Count    Size  Tech Support  Discount      Revenue  \n",
+       "0              26        26  152205             0         1  17688.36300  \n",
+       "1             107        70  159038             0         1  14981.43559  \n",
+       "2              10         7  264935             1         1  32917.13894  \n",
+       "3              40        39   77522             1         1  14773.76855  \n",
+       "4              37        43   91446             1         1  17098.69823  "
       ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "execution_count": 3
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Data sample\n",
+    "multi_data.head()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Define estimator inputs\n",
     "T_bin = multi_data[\n",
@@ -266,37 +268,22 @@
     "W = multi_data.drop(\n",
     "    columns=[\"Tech Support\", \"Discount\", \"Revenue\", \"Size\"]\n",
     ")  # controls"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We investigate below whether the number of investments given is correlated with the size of the customer. We note that the average customer size is larger for more incentives given. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "source": [
-    "# Average customer size per incentive combination\n",
-    "multi_data[[\"Size\", \"Tech Support\", \"Discount\"]].groupby(\n",
-    "    by=[\"Tech Support\", \"Discount\"], as_index=False\n",
-    ").mean().astype(int)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "   Tech Support  Discount    Size\n",
-       "0             0         0   70943\n",
-       "1             0         1   96466\n",
-       "2             1         0  108978\n",
-       "3             1         1  171466"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -349,16 +336,30 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "   Tech Support  Discount    Size\n",
+       "0             0         0   70943\n",
+       "1             0         1   96466\n",
+       "2             1         0  108978\n",
+       "3             1         1  171466"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "execution_count": 5
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Average customer size per incentive combination\n",
+    "multi_data[[\"Size\", \"Tech Support\", \"Discount\"]].groupby(\n",
+    "    by=[\"Tech Support\", \"Discount\"], as_index=False\n",
+    ").mean().astype(int)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "The data was generated using the following underlying treatment effect function:\n",
     "\n",
@@ -367,15 +368,17 @@
     "$$\n",
     "\n",
     "Therefore, the treatment effect depends on the customer's size as follows: tech support provides an consumption boost of \\$5,000 + 2\\% Size and a discount provides an consumption boost of 5\\% Size.**This is the relationship we seek to learn from the data.**"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Define underlying treatment effect function\n",
-    "TE_fn = lambda X: np.hstack([5000 + 2 / 100 * X, 5 / 100 * X])\n",
+    "def TE_fn(X):\r\n",
+    "    return np.hstack([5000 + 2 / 100 * X, 5 / 100 * X])\n",
     "true_TE = TE_fn(X)\n",
     "\n",
     "# Define true coefficients for the three treatments\n",
@@ -383,19 +386,18 @@
     "true_coefs = [2 / 100, 5 / 100, 7 / 100]\n",
     "true_intercepts = [5000, 0, 5000]\n",
     "treatment_names = [\"Tech Support\", \"Discount\", \"Tech Support & Discount\"]"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Get Causal Effects with EconML"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "To get causal effects, we use EconML's `LinearDRLearner`* estimator. This estimator requires a set of discrete treatments $T$ that corresponds to different types of interventions. Thus, we first map the binary interventions tech support and discount into one categorical variable:\n",
     "\n",
@@ -413,12 +415,13 @@
     "\n",
     "\n",
     "**This estimator assumes a linear relationship between the treatment effect and a transformation of the features $X$ (e.g. a polynomial basis expansion). For more generic forms of the treatment effect, see the `DRLearner` estimator.*"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Transform T to one-dimensional array with consecutive integer encoding\r\n",
     "def treat_map(t):\r\n",
@@ -426,13 +429,24 @@
     "\r\n",
     "\r\n",
     "T = np.apply_along_axis(treat_map, 1, T_bin).astype(int)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.dr._drlearner.LinearDRLearner at 0x237737da668>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train EconML model with generic helper models\r\n",
     "model = LinearDRLearner(\r\n",
@@ -442,51 +456,52 @@
     ")\r\n",
     "# Specify final stage inference type and fit model\r\n",
     "model.fit(Y=Y, T=T, X=X, W=W, inference=\"statsmodels\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<econml.dr._drlearner.LinearDRLearner at 0x237737da668>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 8
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Understand Treatment Effects with EconML\n",
     "\n",
     "We can obtain a summary of the coefficient values as well as confidence intervals by calling the `summary` function on the fitted model for each treatment."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "source": [
-    "for i in range(model._d_t[0]):\r\n",
-    "    print(f\"Investment: {treatment_names[i]}\")\r\n",
-    "    print(f\"True treatment effect: {true_intercepts[i]} + {true_coefs[i]}*Size\")\r\n",
-    "    display(model.summary(T=i + 1))"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Investment: Tech Support\n",
       "True treatment effect: 5000 + 0.02*Size\n"
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Coefficient Results</caption>\n",
+       "<tr>\n",
+       "    <td></td>   <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Size</th>      <td>0.021</td>      <td>0.012</td> <td>1.749</td>  <td>0.08</td>   <td>-0.002</td>    <td>0.044</td> \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<caption>CATE Intercept Results</caption>\n",
+       "<tr>\n",
+       "         <td></td>        <th>point_estimate</th> <th>stderr</th>  <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>cate_intercept</th>    <td>5326.611</td>    <td>845.551</td>  <td>6.3</td>    <td>0.0</td>  <td>3669.361</td> <td>6983.861</td>\n",
+       "</tr>\n",
+       "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
+      ],
       "text/plain": [
        "<class 'econml.utilities.Summary'>\n",
        "\"\"\"\n",
@@ -508,7 +523,21 @@
        "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
        "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
        "\"\"\""
-      ],
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Investment: Discount\n",
+      "True treatment effect: 0 + 0.05*Size\n"
+     ]
+    },
+    {
+     "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
        "<caption>Coefficient Results</caption>\n",
@@ -516,33 +545,19 @@
        "    <td></td>   <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Size</th>      <td>0.021</td>      <td>0.012</td> <td>1.749</td>  <td>0.08</td>   <td>-0.002</td>    <td>0.044</td> \n",
+       "  <th>Size</th>      <td>0.052</td>      <td>0.012</td> <td>4.371</td>   <td>0.0</td>    <td>0.029</td>    <td>0.075</td> \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<caption>CATE Intercept Results</caption>\n",
        "<tr>\n",
-       "         <td></td>        <th>point_estimate</th> <th>stderr</th>  <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
+       "         <td></td>        <th>point_estimate</th> <th>stderr</th>  <th>zstat</th> <th>pvalue</th> <th>ci_lower</th>  <th>ci_upper</th>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>cate_intercept</th>    <td>5326.611</td>    <td>845.551</td>  <td>6.3</td>    <td>0.0</td>  <td>3669.361</td> <td>6983.861</td>\n",
+       "  <th>cate_intercept</th>     <td>358.699</td>    <td>848.771</td> <td>0.423</td>  <td>0.673</td> <td>-1304.861</td> <td>2022.258</td>\n",
        "</tr>\n",
        "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
-      ]
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Investment: Discount\n",
-      "True treatment effect: 0 + 0.05*Size\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
+      ],
       "text/plain": [
        "<class 'econml.utilities.Summary'>\n",
        "\"\"\"\n",
@@ -564,7 +579,21 @@
        "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
        "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
        "\"\"\""
-      ],
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Investment: Tech Support & Discount\n",
+      "True treatment effect: 5000 + 0.07*Size\n"
+     ]
+    },
+    {
+     "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
        "<caption>Coefficient Results</caption>\n",
@@ -572,33 +601,19 @@
        "    <td></td>   <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Size</th>      <td>0.052</td>      <td>0.012</td> <td>4.371</td>   <td>0.0</td>    <td>0.029</td>    <td>0.075</td> \n",
+       "  <th>Size</th>      <td>0.074</td>      <td>0.012</td> <td>6.292</td>   <td>0.0</td>    <td>0.051</td>    <td>0.096</td> \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<caption>CATE Intercept Results</caption>\n",
        "<tr>\n",
-       "         <td></td>        <th>point_estimate</th> <th>stderr</th>  <th>zstat</th> <th>pvalue</th> <th>ci_lower</th>  <th>ci_upper</th>\n",
+       "         <td></td>        <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>cate_intercept</th>     <td>358.699</td>    <td>848.771</td> <td>0.423</td>  <td>0.673</td> <td>-1304.861</td> <td>2022.258</td>\n",
+       "  <th>cate_intercept</th>    <td>4899.208</td>    <td>851.54</td> <td>5.753</td>   <td>0.0</td>   <td>3230.22</td> <td>6568.196</td>\n",
        "</tr>\n",
        "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
-      ]
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Investment: Tech Support & Discount\n",
-      "True treatment effect: 5000 + 0.07*Size\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
+      ],
       "text/plain": [
        "<class 'econml.utilities.Summary'>\n",
        "\"\"\"\n",
@@ -620,45 +635,33 @@
        "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
        "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
        "\"\"\""
-      ],
-      "text/html": [
-       "<table class=\"simpletable\">\n",
-       "<caption>Coefficient Results</caption>\n",
-       "<tr>\n",
-       "    <td></td>   <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Size</th>      <td>0.074</td>      <td>0.012</td> <td>6.292</td>   <td>0.0</td>    <td>0.051</td>    <td>0.096</td> \n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<caption>CATE Intercept Results</caption>\n",
-       "<tr>\n",
-       "         <td></td>        <th>point_estimate</th> <th>stderr</th> <th>zstat</th> <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>cate_intercept</th>    <td>4899.208</td>    <td>851.54</td> <td>5.753</td>   <td>0.0</td>   <td>3230.22</td> <td>6568.196</td>\n",
-       "</tr>\n",
-       "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where $T$ is the one-hot-encoding of the discrete treatment and for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and the designated treatment $j$ passed to summary. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
       ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "for i in range(model._d_t[0]):\r\n",
+    "    print(f\"Investment: {treatment_names[i]}\")\r\n",
+    "    print(f\"True treatment effect: {true_intercepts[i]} + {true_coefs[i]}*Size\")\r\n",
+    "    display(model.summary(T=i + 1))"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "From the summary panels, we see that the learned coefficients/intercepts are close to the true coefficients/intercepts and the p-values are small for most of these. \n",
     "\n",
     "We further use the `coef_, coef__interval` and the `intercept_, intercept__interval` methods to obtain the learned coefficient values and build confidence intervals. We compare the true and the learned coefficients through the plots below."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Compare learned coefficients with true model coefficients\r\n",
     "# Aggregate data\r\n",
@@ -677,13 +680,26 @@
     ").T\r\n",
     "intercept_error[0, :] = intercepts - intercept_error[0, :]\r\n",
     "intercept_error[1, :] = intercept_error[1, :] - intercepts"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot coefficients\r\n",
     "plt.figure(figsize=(6, 5))\r\n",
@@ -710,25 +726,11 @@
     "plt.title(\"Intercepts\")\r\n",
     "plt.grid()\r\n",
     "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x360 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Make Policy Decisions with EconML\n",
     "\n",
@@ -738,54 +740,70 @@
     "* The cost of `tech support` scales with the number of PCs a customer has. You can imagine that if the software product needs tech support to be installed on each machine, there is a cost (\\\\$100 here) per machine.\n",
     "* The cost of `discount` is a fixed \\\\$7,000. Think of this as giving the customer the first \\\\$7,000 worth of product for free.\n",
     "* The cost of `tech support` and `discount` is the sum of the cost of each of these. Note that this might not be the case in every business application: it is possible that managing multiple incentive programs can add overhead. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Define cost function\r\n",
     "def cost_fn(multi_data):\r\n",
     "    t1_cost = multi_data[[\"PC Count\"]].values * 100\r\n",
     "    t2_cost = np.ones((multi_data.shape[0], 1)) * 7000\r\n",
     "    return np.hstack([t1_cost, t2_cost, t1_cost + t2_cost])"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We use the model's `const_marginal_effect` method to find the counterfactual treatment effect for each possible treatment. We then subtract the treatment cost and choose the treatment which the highest return. That is the recommended policy."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Get roi for each customer and possible treatment\r\n",
     "potential_roi = model.const_marginal_effect(X=X.values) - cost_fn(multi_data)\r\n",
     "# Add a column of 0s for no treatment\r\n",
     "potential_roi = np.hstack([np.zeros((X.shape[0], 1)), potential_roi])"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "all_treatments = np.array([\"None\"] + treatment_names)\r\n",
     "recommended_T = np.argmax(potential_roi, axis=1)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax1 = sns.scatterplot(\r\n",
     "    x=X.iloc[:, 0].values.flatten(),\r\n",
@@ -803,67 +821,35 @@
     "    title=\"Optimal Investment Policy by Customer\",\r\n",
     ")\r\n",
     "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {
-    "scrolled": true
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We compare different policies: the optimal policy we learned, the current policy, and the policy under which each customer is given all incentives. We note that the optimal policy has a much higher ROI than the alternatives. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "roi_current = potential_roi[np.arange(X.shape[0]), T].sum()\r\n",
     "roi_optimal = potential_roi[np.arange(X.shape[0]), recommended_T].sum()\r\n",
     "roi_bothT = potential_roi[:, -1].sum()\r\n",
     "all_rois = np.array([roi_optimal, roi_current, roi_bothT])\r\n",
     "Y_baseline = (Y - model.effect(X=X.values, T1=T)).sum()"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "source": [
-    "pd.DataFrame(\r\n",
-    "    {\r\n",
-    "        \"Policy\": [\"Optimal\", \"Current\", \"All Investments\"],\r\n",
-    "        \"ROI ($)\": all_rois,\r\n",
-    "        \"ROI (% of baseline Y)\": np.round(all_rois / Y_baseline * 100, 1),\r\n",
-    "    }\r\n",
-    ")"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "            Policy       ROI ($)  ROI (% of baseline Y)\n",
-       "0          Optimal  9.735966e+06                   64.0\n",
-       "1          Current  2.535076e+06                   16.7\n",
-       "2  All Investments  9.683787e+05                    6.4"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -910,49 +896,19 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "            Policy       ROI ($)  ROI (% of baseline Y)\n",
+       "0          Optimal  9.735966e+06                   64.0\n",
+       "1          Current  2.535076e+06                   16.7\n",
+       "2  All Investments  9.683787e+05                    6.4"
       ]
      },
+     "execution_count": 17,
      "metadata": {},
-     "execution_count": 17
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Performance of policies based on ground truth"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "source": [
-    "true_roi = np.zeros((X.shape[0], 4))\r\n",
-    "true_roi[:, 1:3] = TE_fn(X.iloc[:, [0]])\r\n",
-    "true_roi[:, 3] = np.sum(true_roi[:, 1:3], axis=1)\r\n",
-    "true_roi[:, 1:] -= cost_fn(multi_data)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "source": [
-    "roi_current = true_roi[np.arange(X.shape[0]), T].sum()\r\n",
-    "roi_optimal = true_roi[np.arange(X.shape[0]), recommended_T].sum()\r\n",
-    "roi_bothT = true_roi[:, -1].sum()\r\n",
-    "all_rois = np.array([roi_optimal, roi_current, roi_bothT])\r\n",
-    "Y_baseline = (Y - model.effect(X=X.values, T1=T)).sum()"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
    "source": [
     "pd.DataFrame(\r\n",
     "    {\r\n",
@@ -961,17 +917,47 @@
     "        \"ROI (% of baseline Y)\": np.round(all_rois / Y_baseline * 100, 1),\r\n",
     "    }\r\n",
     ")"
-   ],
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performance of policies based on ground truth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_roi = np.zeros((X.shape[0], 4))\r\n",
+    "true_roi[:, 1:3] = TE_fn(X.iloc[:, [0]])\r\n",
+    "true_roi[:, 3] = np.sum(true_roi[:, 1:3], axis=1)\r\n",
+    "true_roi[:, 1:] -= cost_fn(multi_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roi_current = true_roi[np.arange(X.shape[0]), T].sum()\r\n",
+    "roi_optimal = true_roi[np.arange(X.shape[0]), recommended_T].sum()\r\n",
+    "roi_bothT = true_roi[:, -1].sum()\r\n",
+    "all_rois = np.array([roi_optimal, roi_current, roi_bothT])\r\n",
+    "Y_baseline = (Y - model.effect(X=X.values, T1=T)).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "            Policy     ROI ($)  ROI (% of baseline Y)\n",
-       "0          Optimal  8920500.30                   58.6\n",
-       "1          Current  1829938.41                   12.0\n",
-       "2  All Investments   373176.80                    2.5"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1018,24 +1004,41 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "            Policy     ROI ($)  ROI (% of baseline Y)\n",
+       "0          Optimal  8920500.30                   58.6\n",
+       "1          Current  1829938.41                   12.0\n",
+       "2  All Investments   373176.80                    2.5"
       ]
      },
+     "execution_count": 20,
      "metadata": {},
-     "execution_count": 20
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "pd.DataFrame(\r\n",
+    "    {\r\n",
+    "        \"Policy\": [\"Optimal\", \"Current\", \"All Investments\"],\r\n",
+    "        \"ROI ($)\": all_rois,\r\n",
+    "        \"ROI (% of baseline Y)\": np.round(all_rois / Y_baseline * 100, 1),\r\n",
+    "    }\r\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Policy Decisions Using Doubly Robust Policy Learning"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from econml.policy import DRPolicyForest\r\n",
     "\r\n",
@@ -1050,13 +1053,26 @@
     "costs = np.hstack([np.zeros((X.shape[0], 1)), cost_fn(multi_data)])\r\n",
     "est.fit(Y - costs[np.arange(X.shape[0]), T], T, X=X)\r\n",
     "recommended_T = est.predict(X.values)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax1 = sns.scatterplot(\r\n",
     "    x=X.iloc[:, 0].values.flatten(),\r\n",
@@ -1074,88 +1090,58 @@
     "    title=\"Optimal Investment Policy by Customer\",\r\n",
     ")\r\n",
     "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
-   "source": [
-    "plt.figure(figsize=(15, 5))\r\n",
-    "est.plot(0, max_depth=2, treatment_names=['None'] + treatment_names)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1080x360 with 1 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "plt.figure(figsize=(15, 5))\r\n",
+    "est.plot(0, max_depth=2, treatment_names=['None'] + treatment_names)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Performance based on ground truth"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "roi_current = true_roi[np.arange(X.shape[0]), T].sum()\r\n",
     "roi_optimal = true_roi[np.arange(X.shape[0]), recommended_T].sum()\r\n",
     "roi_bothT = true_roi[:, -1].sum()\r\n",
     "all_rois = np.array([roi_optimal, roi_current, roi_bothT])\r\n",
     "Y_baseline = (Y - model.effect(X=X.values[:, [0]], T1=T)).sum()"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
-   "source": [
-    "pd.DataFrame(\r\n",
-    "    {\r\n",
-    "        \"Policy\": [\"Optimal\", \"Current\", \"All Investments\"],\r\n",
-    "        \"ROI ($)\": all_rois,\r\n",
-    "        \"ROI (% of baseline Y)\": np.round(all_rois / Y_baseline * 100, 1),\r\n",
-    "    }\r\n",
-    ")"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "            Policy     ROI ($)  ROI (% of baseline Y)\n",
-       "0          Optimal  8735944.79                   57.4\n",
-       "1          Current  1829938.41                   12.0\n",
-       "2  All Investments   373176.80                    2.5"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1202,16 +1188,32 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "            Policy     ROI ($)  ROI (% of baseline Y)\n",
+       "0          Optimal  8735944.79                   57.4\n",
+       "1          Current  1829938.41                   12.0\n",
+       "2  All Investments   373176.80                    2.5"
       ]
      },
+     "execution_count": 25,
      "metadata": {},
-     "execution_count": 25
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "pd.DataFrame(\r\n",
+    "    {\r\n",
+    "        \"Policy\": [\"Optimal\", \"Current\", \"All Investments\"],\r\n",
+    "        \"ROI ($)\": all_rois,\r\n",
+    "        \"ROI (% of baseline Y)\": np.round(all_rois / Y_baseline * 100, 1),\r\n",
+    "    }\r\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Conclusions\n",
     "\n",
@@ -1222,21 +1224,23 @@
     "* Build investment policies around the learned effects\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/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": []
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.6.6 64-bit"
+   "display_name": "Python 3.6.6 64-bit",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1249,9 +1253,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.6"
-  },
-  "interpreter": {
-   "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb
index f0bb288d..31e504b7 100644
--- a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb	
@@ -369,7 +369,8 @@
    "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",
+    "def TE_fn(X):\n",
+    "    return (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",
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
index d4f18414..6e047b3f 100644
--- 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	
@@ -2,6 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "<img src=\"https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Recomendation.png\" width=\"400\">\n",
     "\n",
@@ -12,15 +17,15 @@
     "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"
     }
-   }
-  },
-  {
-   "cell_type": "markdown",
+   },
    "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",
@@ -32,15 +37,15 @@
     "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"
     }
-   }
-  },
-  {
-   "cell_type": "markdown",
+   },
    "source": [
     "# Background\n",
     "\n",
@@ -53,16 +58,17 @@
     "**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."
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
    "source": [
     "# Some imports to get us started\r\n",
     "# Utilities\r\n",
@@ -82,16 +88,15 @@
     "\r\n",
     "import matplotlib.pyplot as plt\r\n",
     "%matplotlib inline"
-   ],
-   "outputs": [],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "# Data\n",
     "\n",
@@ -117,68 +122,34 @@
     "\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.*"
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "source": [
-    "# Import the sample AB data\n",
-    "file_url = \"https://msalicedatapublic.z5.web.core.windows.net/datasets/RecommendationAB/ab_sample.csv\"   \n",
-    "ab_data = pd.read_csv(file_url)"
-   ],
-   "outputs": [],
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
     }
-   }
+   },
+   "outputs": [],
+   "source": [
+    "# Import the sample AB data\n",
+    "file_url = \"https://msalicedatapublic.z5.web.core.windows.net/datasets/RecommendationAB/ab_sample.csv\"   \n",
+    "ab_data = pd.read_csv(file_url)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "source": [
-    "# Data sample\n",
-    "ab_data.head()"
-   ],
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "   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  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -297,37 +268,71 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "   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": {},
-     "execution_count": 3
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   "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"
-   ],
-   "outputs": [],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "The data was generated using the following undelying treatment effect function:\n",
     "\n",
@@ -336,50 +341,52 @@
     "$$\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.**"
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   },
   {
    "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",
+    "def TE_fn(X):\r\n",
+    "    return (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]"
-   ],
-   "outputs": [],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   }
+   ]
   },
   {
    "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. "
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
    "source": [
     "# Define nuissance estimators\r\n",
     "lgb_T_XZ_params = {\r\n",
@@ -399,17 +406,28 @@
     "model_T_XZ = lgb.LGBMClassifier(**lgb_T_XZ_params)\r\n",
     "model_Y_X = lgb.LGBMRegressor(**lgb_Y_X_params)\r\n",
     "flexible_model_effect = lgb.LGBMRegressor(**lgb_Y_X_params)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.iv.dr._dr.LinearIntentToTreatDRIV at 0x260fd1a6fc8>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train EconML model\r\n",
     "model = LinearIntentToTreatDRIV(\r\n",
@@ -419,28 +437,17 @@
     "    featurizer = PolynomialFeatures(degree=1, include_bias=False)\r\n",
     ")\r\n",
     "model.fit(Y, T, Z=Z, X=X_data, inference=\"statsmodels\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<econml.iv.dr._dr.LinearIntentToTreatDRIV at 0x260fd1a6fc8>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 7
-    }
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
    "source": [
     "# Compare learned coefficients with true model coefficients\r\n",
     "coef_indices = np.arange(model.coef_.shape[0])\r\n",
@@ -448,98 +455,62 @@
     "coef_error = np.asarray(model.coef__interval()) # 95% confidence interval for coefficients\r\n",
     "coef_error[0, :] = model.coef_ - coef_error[0, :]\r\n",
     "coef_error[1, :] = coef_error[1, :] - model.coef_"
-   ],
-   "outputs": [],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.errorbar(coef_indices, model.coef_, coef_error, fmt=\"o\", label=\"Learned coefficients\\nand 95% confidence interval\")\r\n",
     "plt.scatter(coef_indices, true_coefs, color='C1', label=\"True coefficients\")\r\n",
     "plt.xticks(coef_indices, X_data.columns, rotation='vertical')\r\n",
     "plt.legend()\r\n",
     "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "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. "
-   ],
    "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,
-   "source": [
-    "model.summary()"
-   ],
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "<class 'econml.utilities.Summary'>\n",
-       "\"\"\"\n",
-       "                            Coefficient Results                             \n",
-       "============================================================================\n",
-       "                      point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
-       "----------------------------------------------------------------------------\n",
-       "days_visited_exp_pre             0.0  0.007   0.055  0.956   -0.011    0.012\n",
-       "days_visited_free_pre          0.284  0.007  38.206    0.0    0.272    0.296\n",
-       "days_visited_fs_pre           -0.011  0.007  -1.582  0.114   -0.022      0.0\n",
-       "days_visited_hs_pre            -0.19  0.007 -27.999    0.0   -0.201   -0.178\n",
-       "days_visited_rs_pre            0.001  0.007   0.092  0.927    -0.01    0.012\n",
-       "days_visited_vrs_pre             0.0  0.007   0.028  0.978   -0.011    0.011\n",
-       "locale_en_US                  -0.013  0.113  -0.111  0.911   -0.199    0.174\n",
-       "revenue_pre                     -0.0    0.0  -1.231  0.218     -0.0      0.0\n",
-       "os_type_osx                    0.961  0.139   6.922    0.0    0.732    1.189\n",
-       "os_type_windows                0.041  0.138   0.298  0.766   -0.186    0.269\n",
-       "                       CATE Intercept Results                      \n",
-       "===================================================================\n",
-       "               point_estimate stderr zstat pvalue ci_lower ci_upper\n",
-       "-------------------------------------------------------------------\n",
-       "cate_intercept          0.537   0.27 1.989  0.047    0.093    0.981\n",
-       "-------------------------------------------------------------------\n",
-       "\n",
-       "<sub>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",
-       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
-       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. 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.</sub>\n",
-       "\"\"\""
-      ],
       "text/html": [
        "<table class=\"simpletable\">\n",
        "<caption>Coefficient Results</caption>\n",
@@ -586,36 +557,85 @@
        "  <th>cate_intercept</th>      <td>0.537</td>      <td>0.27</td>  <td>1.989</td>  <td>0.047</td>   <td>0.093</td>    <td>0.981</td> \n",
        "</tr>\n",
        "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. 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.</sub>"
+      ],
+      "text/plain": [
+       "<class 'econml.utilities.Summary'>\n",
+       "\"\"\"\n",
+       "                            Coefficient Results                             \n",
+       "============================================================================\n",
+       "                      point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
+       "----------------------------------------------------------------------------\n",
+       "days_visited_exp_pre             0.0  0.007   0.055  0.956   -0.011    0.012\n",
+       "days_visited_free_pre          0.284  0.007  38.206    0.0    0.272    0.296\n",
+       "days_visited_fs_pre           -0.011  0.007  -1.582  0.114   -0.022      0.0\n",
+       "days_visited_hs_pre            -0.19  0.007 -27.999    0.0   -0.201   -0.178\n",
+       "days_visited_rs_pre            0.001  0.007   0.092  0.927    -0.01    0.012\n",
+       "days_visited_vrs_pre             0.0  0.007   0.028  0.978   -0.011    0.011\n",
+       "locale_en_US                  -0.013  0.113  -0.111  0.911   -0.199    0.174\n",
+       "revenue_pre                     -0.0    0.0  -1.231  0.218     -0.0      0.0\n",
+       "os_type_osx                    0.961  0.139   6.922    0.0    0.732    1.189\n",
+       "os_type_windows                0.041  0.138   0.298  0.766   -0.186    0.269\n",
+       "                       CATE Intercept Results                      \n",
+       "===================================================================\n",
+       "               point_estimate stderr zstat pvalue ci_lower ci_upper\n",
+       "-------------------------------------------------------------------\n",
+       "cate_intercept          0.537   0.27 1.989  0.047    0.093    0.981\n",
+       "-------------------------------------------------------------------\n",
+       "\n",
+       "<sub>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",
+       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
+       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. 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.</sub>\n",
+       "\"\"\""
       ]
      },
+     "execution_count": 10,
      "metadata": {},
-     "execution_count": 10
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   }
+   "source": [
+    "model.summary()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "source": [
-    "test_customers = X_data.iloc[:1000]\r\n",
-    "true_customer_TE = TE_fn(test_customers)\r\n",
-    "model_customer_TE = model.effect(test_customers)"
-   ],
-   "outputs": [],
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
     }
-   }
+   },
+   "outputs": [],
+   "source": [
+    "test_customers = X_data.iloc[:1000]\r\n",
+    "true_customer_TE = TE_fn(test_customers)\r\n",
+    "model_customer_TE = model.effect(test_customers)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# How close are the predicted treatment effect to the true treatment effects for 1000 users?\n",
     "plt.scatter(true_customer_TE, model.effect(test_customers), label=\"Predicted vs True treatment effect\")\n",
@@ -623,117 +643,103 @@
     "plt.ylabel(\"Predicted treatment effect\")\n",
     "plt.legend()\n",
     "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "# Understand Treatment Effects with EconML\n",
     "\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 who respond negatively to the membership program and in dark green users who respond positively."
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x360 with 1 Axes>"
+      ]
+     },
+     "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(model, test_customers)\n",
     "plt.figure(figsize=(25, 5))\n",
     "intrp.plot(feature_names=X_data.columns, fontsize=12)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1800x360 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "# Make Policy Decisions with EconML\n",
     "\n",
     "Interventions usually have a cost: incetivizing a user to become a member can be costly (e.g by offering a discount). Thus, we would like to know what customers to target to maximize the profit from their increased engagement. This is the **treatment policy**. \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. "
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=10)\n",
     "intrp.interpret(model, test_customers, sample_treatment_costs=0.2)\n",
     "plt.figure(figsize=(25, 5))\n",
     "intrp.plot(feature_names=X_data.columns, fontsize=12)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1800x360 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "# Conclusions\n",
     "\n",
@@ -744,12 +750,7 @@
     "* Build policies around the learned effects\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/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
-   ],
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   }
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/Double Machine Learning Examples.ipynb b/notebooks/Double Machine Learning Examples.ipynb
index 49ef708a..77488385 100644
--- a/notebooks/Double Machine Learning Examples.ipynb	
+++ b/notebooks/Double Machine Learning Examples.ipynb	
@@ -139,11 +139,13 @@
     "# Outcome support\n",
     "support_Y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n",
     "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def epsilon_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "# Treatment support\n",
     "support_T = support_Y\n",
     "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
-    "eta_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def eta_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "\n",
     "# Generate controls, covariates, treatments and outcomes\n",
     "W = np.random.normal(0, 1, size=(n, n_w))\n",
@@ -769,11 +771,13 @@
     "# Outcome support\n",
     "support_Y = np.random.choice(range(n_w), size=support_size, replace=False)\n",
     "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "epsilon_sample = lambda n:np.random.uniform(-1, 1, size=n)\n",
+    "def epsilon_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "# Treatment support\n",
     "support_T = support_Y\n",
     "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
-    "eta_sample = lambda n: np.random.uniform(-1, 1, size=n) \n",
+    "def eta_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n) \n",
     "\n",
     "# Generate controls, covariates, treatments and outcomes\n",
     "W = np.random.normal(0, 1, size=(n, n_w))\n",
@@ -1632,11 +1636,13 @@
     "# Outcome support\n",
     "support_Y = np.random.choice(np.arange(n_w), size=support_size, replace=False)\n",
     "coefs_Y = np.random.uniform(0, 1, size=support_size)\n",
-    "epsilon_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def epsilon_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "# Treatment support\n",
     "support_T = support_Y\n",
     "coefs_T = np.random.uniform(0, 1, size=support_size)\n",
-    "eta_sample = lambda n: np.random.uniform(-1, 1, size=n)\n",
+    "def eta_sample(n):\n",
+    "    return np.random.uniform(-1, 1, size=n)\n",
     "\n",
     "# Generate controls, covariates, treatments and outcomes\n",
     "W = np.random.normal(0, 1, size=(n, n_w))\n",
diff --git a/notebooks/ForestLearners Basic Example.ipynb b/notebooks/ForestLearners Basic Example.ipynb
index d9fabee2..f43f845d 100644
--- a/notebooks/ForestLearners Basic Example.ipynb	
+++ b/notebooks/ForestLearners Basic Example.ipynb	
@@ -66,9 +66,12 @@
     "n = 2000\n",
     "p = 10\n",
     "X = np.random.normal(size=(n, p))\n",
-    "true_propensity = lambda x: .4 + .2 * (x[:, 0] > 0)\n",
-    "true_effect = lambda x: (x[:, 0] * (x[:, 0] > 0))\n",
-    "true_conf = lambda x: x[:, 1] + np.clip(x[:, 2], - np.inf, 0)\n",
+    "def true_propensity(x):\n",
+    "    return 0.4 + 0.2 * (x[:, 0] > 0)\n",
+    "def true_effect(x):\n",
+    "    return x[:, 0] * (x[:, 0] > 0)\n",
+    "def true_conf(x):\n",
+    "    return x[:, 1] + np.clip(x[:, 2], -np.inf, 0)\n",
     "T = np.random.binomial(1, true_propensity(X))\n",
     "Y =  true_effect(X) * T + true_conf(X) + np.random.normal(size=(n,))"
    ]
diff --git a/notebooks/Generalized Random Forests.ipynb b/notebooks/Generalized Random Forests.ipynb
index d2659575..a951c921 100644
--- a/notebooks/Generalized Random Forests.ipynb	
+++ b/notebooks/Generalized Random Forests.ipynb	
@@ -84,8 +84,8 @@
     "n_treatments = 1\n",
     "# true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_treatments - 1))])\n",
     "# true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n",
-    "true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n",
-    "                               np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n",
+    "def true_te(X):\n",
+    "    return np.hstack([(X[:, [0]] > 0) * X[:, [0]], np.ones((X.shape[0], n_treatments - 1)) * np.arange(1, n_treatments).reshape(1, -1)])\n",
     "X = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n",
     "for t in range(n_treatments):\n",
@@ -378,7 +378,8 @@
     "n_features = 10\n",
     "n_treatments = 2\n",
     "# true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_treatments - 1))])\n",
-    "true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n",
+    "def true_te(X):\n",
+    "    return np.hstack([X[:, [0]] > 0, np.ones((X.shape[0], n_treatments - 1))])\n",
     "# true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n",
     "#                                np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n",
     "Z = np.random.normal(0, 1, size=(n_samples, n_treatments))\n",
@@ -616,7 +617,8 @@
     "n_samples = 2000\n",
     "n_features = 10\n",
     "n_outputs = 2\n",
-    "true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_outputs - 1))])\n",
+    "def true_te(X):\n",
+    "    return np.hstack([X[:, [0]] ** 2 + 1, np.ones((X.shape[0], n_outputs - 1))])\n",
     "# true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_outputs - 1))])\n",
     "# true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n",
     "#                                np.ones((X.shape[0], n_outputs - 1))*np.arange(1, n_outputs).reshape(1, -1)])\n",
@@ -808,8 +810,8 @@
     "n_outputs = 2\n",
     "# true_te = lambda X: np.hstack([X[:, [0]]**2 + 1, np.ones((X.shape[0], n_treatments - 1))])\n",
     "# true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n",
-    "true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n",
-    "                               np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n",
+    "def true_te(X):\n",
+    "    return np.hstack([(X[:, [0]] > 0) * X[:, [0]], np.ones((X.shape[0], n_treatments - 1)) * np.arange(1, n_treatments).reshape(1, -1)])\n",
     "X = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "W = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n",
@@ -1459,7 +1461,8 @@
     "n_samples = 2000\n",
     "n_features = 10\n",
     "n_treatments = 2\n",
-    "true_te = lambda X: np.hstack([X[:, [0]]>0, np.ones((X.shape[0], n_treatments - 1))])\n",
+    "def true_te(X):\n",
+    "    return np.hstack([X[:, [0]] > 0, np.ones((X.shape[0], n_treatments - 1))])\n",
     "Z = np.random.normal(0, 1, size=(n_samples, n_treatments))\n",
     "X = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "U = np.random.normal(0, .2, size=(n_samples, 1))\n",
diff --git a/notebooks/Interpretability with SHAP.ipynb b/notebooks/Interpretability with SHAP.ipynb
index 210cae5b..06b6594d 100644
--- a/notebooks/Interpretability with SHAP.ipynb	
+++ b/notebooks/Interpretability with SHAP.ipynb	
@@ -69,7 +69,8 @@
     "np.random.seed(123)\n",
     "n_samples = 5000\n",
     "n_features = 10\n",
-    "true_te = lambda X: (X[:, 0]>0) * X[:, 0]\n",
+    "def true_te(X):\n",
+    "    return (X[:, 0] > 0) * X[:, 0]\n",
     "X = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "W = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "T = np.random.binomial(1, scipy.special.expit(X[:, 0]))\n",
@@ -293,8 +294,8 @@
     "n_features = 10\n",
     "n_treatments = 2\n",
     "n_outputs = 3\n",
-    "true_te = lambda X: np.hstack([(X[:, [0]]>0) * X[:, [0]],\n",
-    "                               np.ones((X.shape[0], n_treatments - 1))*np.arange(1, n_treatments).reshape(1, -1)])\n",
+    "def true_te(X):\n",
+    "    return np.hstack([(X[:, [0]] > 0) * X[:, [0]], np.ones((X.shape[0], n_treatments - 1)) * np.arange(1, n_treatments).reshape(1, -1)])\n",
     "X = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "W = np.random.normal(0, 1, size=(n_samples, n_features))\n",
     "T = np.random.normal(0, 1, size=(n_samples, n_treatments))\n",
diff --git a/notebooks/Metalearners Examples.ipynb b/notebooks/Metalearners Examples.ipynb
index c058c49f..e87bec23 100644
--- a/notebooks/Metalearners Examples.ipynb	
+++ b/notebooks/Metalearners Examples.ipynb	
@@ -1,513 +1,515 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<table border=\"0\">\n",
-    "    <tr>\n",
-    "        <td>\n",
-    "            <img src=\"https://ictd2016.files.wordpress.com/2016/04/microsoft-research-logo-copy.jpg\" style=\"width 30px;\" />\n",
-    "             </td>\n",
-    "        <td>\n",
-    "            <img src=\"https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/MSR-ALICE-HeaderGraphic-1920x720_1-800x550.jpg\" style=\"width 100px;\"/></td>\n",
-    "        </tr>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Metalearners Estimators: Use Cases and Examples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Metalearners are binary treatment CATE estimators that model the response surfaces, $Y(0)$ and $Y(1)$, separately. To account for a heterogeneous propensity of treatment $P(T\\mid X)$, the two modeled responses $E(Y(0)\\mid X)$ and $E(Y(1)\\mid X)$ are weighted in different ways in the final CATE estimation. For a detailed overview of these methods, see [this paper](https://arxiv.org/abs/1706.03461). \n",
-    "\n",
-    "The EconML SDK implements the following `metalearners`:\n",
-    "\n",
-    "* T-Learner\n",
-    "\n",
-    "* S-Learner\n",
-    "\n",
-    "* X-Learner\n",
-    "\n",
-    "* DomainAdaptation-Learner\n",
-    "\n",
-    "* DoublyRobust-Learner\n",
-    "\n",
-    "In this notebook, we compare the performance of these four CATE estimatiors on synthetic data and semi-synthetic data.\n",
-    "\n",
-    "**Notebook contents:**\n",
-    "\n",
-    "1. Example usage with synthetic data\n",
-    "\n",
-    "2. Example usage with semi-synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Main imports\n",
-    "from econml.metalearners import TLearner, SLearner, XLearner, DomainAdaptationLearner\n",
-    "\n",
-    "# Helper imports \n",
-    "import numpy as np\n",
-    "from numpy.random import binomial, multivariate_normal, normal, uniform\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingRegressor\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Example Usage with Synthetic Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.1. DGP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We use the data generating process (DGP) from [Kunzel et al.](https://arxiv.org/abs/1706.03461). The DGP is described by the following equations:\n",
-    "\n",
-    "$\n",
-    "Y = \\mu_1(x) \\cdot T + \\mu_0(x) \\cdot (1-T) + \\epsilon \\\\\n",
-    "T \\sim Bern(e(x)), \\; e(x) = P(T=1|X=x)\n",
-    "$\n",
-    "\n",
-    "where \n",
-    "\n",
-    "$\n",
-    "\\mu_0(x) = x^T\\beta,\\; with \\;\\beta\\sim Unif([-3, 3]^d),\\; X_i \\sim N(0, \\Sigma)\\\\\n",
-    "\\mu_1(x) = \\mu_0(x) + 8 \\mathbb{I}(x_2>0.1) => CATE(x) = 8 \\mathbb{I}(x_2>0.1)\n",
-    "$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define DGP\n",
-    "def generate_data(n, d, controls_outcome, treatment_effect, propensity):\n",
-    "    \"\"\"Generates population data for given untreated_outcome, treatment_effect and propensity functions.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "        n (int): population size\n",
-    "        d (int): number of covariates\n",
-    "        controls_outcome (func): untreated outcome conditional on covariates\n",
-    "        treatment_effect (func): treatment effect conditional on covariates\n",
-    "        propensity (func): probability of treatment conditional on covariates\n",
-    "    \"\"\"\n",
-    "    # Generate covariates\n",
-    "    X = multivariate_normal(np.zeros(d), np.diag(np.ones(d)), n)\n",
-    "    # Generate treatment\n",
-    "    T = np.apply_along_axis(lambda x: binomial(1, propensity(x), 1)[0], 1, X)\n",
-    "    # Calculate outcome\n",
-    "    Y0 = np.apply_along_axis(lambda x: controls_outcome(x), 1, X)\n",
-    "    treat_effect = np.apply_along_axis(lambda x: treatment_effect(x), 1, X)\n",
-    "    Y = Y0 + treat_effect * T\n",
-    "    return (Y, T, X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# controls outcome, treatment effect, propensity definitions\n",
-    "def generate_controls_outcome(d):\n",
-    "    beta = uniform(-3, 3, d)\n",
-    "    return lambda x: np.dot(x, beta) + normal(0, 1)\n",
-    "treatment_effect = lambda x: (1 if x[1] > 0.1 else 0)*8\n",
-    "propensity = lambda x: (0.8 if (x[2]>-0.5 and x[2]<0.5) else 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# DGP constants and test data\n",
-    "d = 5\n",
-    "n = 1000\n",
-    "n_test = 250\n",
-    "controls_outcome = generate_controls_outcome(d)\n",
-    "X_test = multivariate_normal(np.zeros(d), np.diag(np.ones(d)), n_test)\n",
-    "delta = 6/n_test\n",
-    "X_test[:, 1] = np.arange(-3, 3, delta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Y, T, X = generate_data(n, d, controls_outcome, treatment_effect, propensity)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.2. Train Estimators"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate T learner\n",
-    "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
-    "T_learner = TLearner(models=models)\n",
-    "# Train T_learner\n",
-    "T_learner.fit(Y, T, X=X)\n",
-    "# Estimate treatment effects on test data\n",
-    "T_te = T_learner.effect(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate S learner\n",
-    "overall_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
-    "S_learner = SLearner(overall_model=overall_model)\n",
-    "# Train S_learner\n",
-    "S_learner.fit(Y, T, X=X)\n",
-    "# Estimate treatment effects on test data\n",
-    "S_te = S_learner.effect(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate X learner\n",
-    "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
-    "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n",
-    "                                                  min_samples_leaf=int(n/100))\n",
-    "X_learner = XLearner(models=models, propensity_model=propensity_model)\n",
-    "# Train X_learner\n",
-    "X_learner.fit(Y, T, X=X)\n",
-    "# Estimate treatment effects on test data\n",
-    "X_te = X_learner.effect(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate Domain Adaptation learner\n",
-    "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
-    "final_models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
-    "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n",
-    "                                                  min_samples_leaf=int(n/100))\n",
-    "DA_learner = DomainAdaptationLearner(models=models,\n",
-    "                                     final_models=final_models,\n",
-    "                                     propensity_model=propensity_model)\n",
-    "# Train DA_learner\n",
-    "DA_learner.fit(Y, T, X=X)\n",
-    "# Estimate treatment effects on test data\n",
-    "DA_te = DA_learner.effect(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate Doubly Robust Learner\n",
-    "from econml.dr import DRLearner\n",
-    "outcome_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
-    "pseudo_treatment_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
-    "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n",
-    "                                                  min_samples_leaf=int(n/100))\n",
-    "\n",
-    "DR_learner = DRLearner(model_regression=outcome_model, model_propensity=propensity_model,\n",
-    "                       model_final=pseudo_treatment_model, cv=5)\n",
-    "# Train DR_learner\n",
-    "DR_learner.fit(Y, T, X=X)\n",
-    "# Estimate treatment effects on test data\n",
-    "DR_te = DR_learner.effect(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.3. Visual Comparisons"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "### Comparison plot of the different learners\n",
-    "plt.figure(figsize=(7, 5))\n",
-    "plt.plot(X_test[:, 1], np.apply_along_axis(treatment_effect, 1, X_test), color='black', ls='--', label='Baseline')\n",
-    "plt.scatter(X_test[:, 1], T_te, label=\"T-learner\")\n",
-    "plt.scatter(X_test[:, 1], S_te, label=\"S-learner\")\n",
-    "plt.scatter(X_test[:, 1], DA_te, label=\"DA-learner\")\n",
-    "plt.scatter(X_test[:, 1], X_te, label=\"X-learner\")\n",
-    "plt.scatter(X_test[:, 1], DR_te, label=\"DR-learner\")\n",
-    "plt.xlabel('$x_1$')\n",
-    "plt.ylabel('Treatment Effect')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualization of bias distribution\n",
-    "expected_te = np.apply_along_axis(treatment_effect, 1, X_test)\n",
-    "plt.violinplot([np.abs(T_te - expected_te), \n",
-    "                np.abs(S_te - expected_te),\n",
-    "                np.abs(DA_te - expected_te),\n",
-    "                np.abs(X_te - expected_te),\n",
-    "                np.abs(DR_te - expected_te)\n",
-    "               ], showmeans=True)\n",
-    "plt.ylabel(\"Bias distribution\")\n",
-    "plt.xticks([1, 2, 3, 4, 5], ['T-learner', 'S-learner', 'DA-learner', 'X-learner', 'DR-learner'])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Example Usage with Semi-synthetic Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.1. DGP\n",
-    "\n",
-    "We use the Response Surface B from [Hill (2011)](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162) to generate sythetic outcome surfaces from real-world covariates and treatment assignments (Infant Health Development Program data). Since the original data was part of a randomized trial, a subset of the treated infants (those with non-white mothers) has been removed from the data in order to mimic the observational data setting. For more details, see [Hill (2011)](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162).\n",
-    "\n",
-    "\n",
-    "The DGP is described by the following equations:\n",
-    "\n",
-    "$\n",
-    "Y(0) = e^{(X+W)\\beta} + \\epsilon_0, \\;\\epsilon_0 \\sim N(0, 1)\\\\\n",
-    "Y(1) = X\\beta - \\omega + \\epsilon_1, \\;\\epsilon_1 \\sim N(0, 1)\\\\\n",
-    "$\n",
-    "\n",
-    "where $X$ is a covariate matrix, $W$ is a constant matrix with entries equal to $0.5$ and $w$ is a constant calculated such that the CATT equals $4$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from econml.data.dgps import ihdp_surface_B"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Y, T, X, expected_te = ihdp_surface_B()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.2. Train Estimators"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# T-learner\n",
-    "T_learner.fit(Y, T, X=X)\n",
-    "T_te = T_learner.effect(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# S-learner\n",
-    "S_learner.fit(Y, T, X=X)\n",
-    "S_te = S_learner.effect(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# X-learner\n",
-    "X_learner.fit(Y, T, X=X)\n",
-    "X_te = X_learner.effect(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Domain adaptation learner\n",
-    "DA_learner.fit(Y, T, X=X)\n",
-    "DA_te = DA_learner.effect(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Doubly robust learner\n",
-    "DR_learner.fit(Y, T, X=X)\n",
-    "DR_te = DR_learner.effect(X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.3. Visual Comparisons"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualization of bias distribution\n",
-    "plt.violinplot([np.abs(T_te - expected_te), \n",
-    "                np.abs(S_te - expected_te),\n",
-    "                np.abs(DA_te - expected_te),\n",
-    "                np.abs(X_te - expected_te),\n",
-    "                np.abs(DR_te - expected_te)\n",
-    "               ], showmeans=True)\n",
-    "plt.ylabel(\"Bias distribution\")\n",
-    "plt.xticks([1, 2, 3, 4, 5], ['T-learner', 'S-learner', 'DA-learner', 'X-learner', 'DR-learner'])\n",
-    "plt.show()"
-   ]
-  }
- ],
- "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": "3.7.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table border=\"0\">\n",
+    "    <tr>\n",
+    "        <td>\n",
+    "            <img src=\"https://ictd2016.files.wordpress.com/2016/04/microsoft-research-logo-copy.jpg\" style=\"width 30px;\" />\n",
+    "             </td>\n",
+    "        <td>\n",
+    "            <img src=\"https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/MSR-ALICE-HeaderGraphic-1920x720_1-800x550.jpg\" style=\"width 100px;\"/></td>\n",
+    "        </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Metalearners Estimators: Use Cases and Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Metalearners are binary treatment CATE estimators that model the response surfaces, $Y(0)$ and $Y(1)$, separately. To account for a heterogeneous propensity of treatment $P(T\\mid X)$, the two modeled responses $E(Y(0)\\mid X)$ and $E(Y(1)\\mid X)$ are weighted in different ways in the final CATE estimation. For a detailed overview of these methods, see [this paper](https://arxiv.org/abs/1706.03461). \n",
+    "\n",
+    "The EconML SDK implements the following `metalearners`:\n",
+    "\n",
+    "* T-Learner\n",
+    "\n",
+    "* S-Learner\n",
+    "\n",
+    "* X-Learner\n",
+    "\n",
+    "* DomainAdaptation-Learner\n",
+    "\n",
+    "* DoublyRobust-Learner\n",
+    "\n",
+    "In this notebook, we compare the performance of these four CATE estimatiors on synthetic data and semi-synthetic data.\n",
+    "\n",
+    "**Notebook contents:**\n",
+    "\n",
+    "1. Example usage with synthetic data\n",
+    "\n",
+    "2. Example usage with semi-synthetic data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Main imports\n",
+    "from econml.metalearners import TLearner, SLearner, XLearner, DomainAdaptationLearner\n",
+    "\n",
+    "# Helper imports \n",
+    "import numpy as np\n",
+    "from numpy.random import binomial, multivariate_normal, normal, uniform\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingRegressor\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Example Usage with Synthetic Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. DGP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the data generating process (DGP) from [Kunzel et al.](https://arxiv.org/abs/1706.03461). The DGP is described by the following equations:\n",
+    "\n",
+    "$\n",
+    "Y = \\mu_1(x) \\cdot T + \\mu_0(x) \\cdot (1-T) + \\epsilon \\\\\n",
+    "T \\sim Bern(e(x)), \\; e(x) = P(T=1|X=x)\n",
+    "$\n",
+    "\n",
+    "where \n",
+    "\n",
+    "$\n",
+    "\\mu_0(x) = x^T\\beta,\\; with \\;\\beta\\sim Unif([-3, 3]^d),\\; X_i \\sim N(0, \\Sigma)\\\\\n",
+    "\\mu_1(x) = \\mu_0(x) + 8 \\mathbb{I}(x_2>0.1) => CATE(x) = 8 \\mathbb{I}(x_2>0.1)\n",
+    "$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define DGP\n",
+    "def generate_data(n, d, controls_outcome, treatment_effect, propensity):\n",
+    "    \"\"\"Generates population data for given untreated_outcome, treatment_effect and propensity functions.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "        n (int): population size\n",
+    "        d (int): number of covariates\n",
+    "        controls_outcome (func): untreated outcome conditional on covariates\n",
+    "        treatment_effect (func): treatment effect conditional on covariates\n",
+    "        propensity (func): probability of treatment conditional on covariates\n",
+    "    \"\"\"\n",
+    "    # Generate covariates\n",
+    "    X = multivariate_normal(np.zeros(d), np.diag(np.ones(d)), n)\n",
+    "    # Generate treatment\n",
+    "    T = np.apply_along_axis(lambda x: binomial(1, propensity(x), 1)[0], 1, X)\n",
+    "    # Calculate outcome\n",
+    "    Y0 = np.apply_along_axis(lambda x: controls_outcome(x), 1, X)\n",
+    "    treat_effect = np.apply_along_axis(lambda x: treatment_effect(x), 1, X)\n",
+    "    Y = Y0 + treat_effect * T\n",
+    "    return (Y, T, X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# controls outcome, treatment effect, propensity definitions\n",
+    "def generate_controls_outcome(d):\n",
+    "    beta = uniform(-3, 3, d)\n",
+    "    return lambda x: np.dot(x, beta) + normal(0, 1)\n",
+    "def treatment_effect(x):\n",
+    "    return (1 if x[1] > 0.1 else 0) * 8\n",
+    "def propensity(x):\n",
+    "    return 0.8 if x[2] > -0.5 and x[2] < 0.5 else 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DGP constants and test data\n",
+    "d = 5\n",
+    "n = 1000\n",
+    "n_test = 250\n",
+    "controls_outcome = generate_controls_outcome(d)\n",
+    "X_test = multivariate_normal(np.zeros(d), np.diag(np.ones(d)), n_test)\n",
+    "delta = 6/n_test\n",
+    "X_test[:, 1] = np.arange(-3, 3, delta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y, T, X = generate_data(n, d, controls_outcome, treatment_effect, propensity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Train Estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instantiate T learner\n",
+    "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
+    "T_learner = TLearner(models=models)\n",
+    "# Train T_learner\n",
+    "T_learner.fit(Y, T, X=X)\n",
+    "# Estimate treatment effects on test data\n",
+    "T_te = T_learner.effect(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instantiate S learner\n",
+    "overall_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
+    "S_learner = SLearner(overall_model=overall_model)\n",
+    "# Train S_learner\n",
+    "S_learner.fit(Y, T, X=X)\n",
+    "# Estimate treatment effects on test data\n",
+    "S_te = S_learner.effect(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instantiate X learner\n",
+    "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
+    "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n",
+    "                                                  min_samples_leaf=int(n/100))\n",
+    "X_learner = XLearner(models=models, propensity_model=propensity_model)\n",
+    "# Train X_learner\n",
+    "X_learner.fit(Y, T, X=X)\n",
+    "# Estimate treatment effects on test data\n",
+    "X_te = X_learner.effect(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instantiate Domain Adaptation learner\n",
+    "models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
+    "final_models = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
+    "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n",
+    "                                                  min_samples_leaf=int(n/100))\n",
+    "DA_learner = DomainAdaptationLearner(models=models,\n",
+    "                                     final_models=final_models,\n",
+    "                                     propensity_model=propensity_model)\n",
+    "# Train DA_learner\n",
+    "DA_learner.fit(Y, T, X=X)\n",
+    "# Estimate treatment effects on test data\n",
+    "DA_te = DA_learner.effect(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Instantiate Doubly Robust Learner\n",
+    "from econml.dr import DRLearner\n",
+    "outcome_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
+    "pseudo_treatment_model = GradientBoostingRegressor(n_estimators=100, max_depth=6, min_samples_leaf=int(n/100))\n",
+    "propensity_model = RandomForestClassifier(n_estimators=100, max_depth=6, \n",
+    "                                                  min_samples_leaf=int(n/100))\n",
+    "\n",
+    "DR_learner = DRLearner(model_regression=outcome_model, model_propensity=propensity_model,\n",
+    "                       model_final=pseudo_treatment_model, cv=5)\n",
+    "# Train DR_learner\n",
+    "DR_learner.fit(Y, T, X=X)\n",
+    "# Estimate treatment effects on test data\n",
+    "DR_te = DR_learner.effect(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3. Visual Comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### Comparison plot of the different learners\n",
+    "plt.figure(figsize=(7, 5))\n",
+    "plt.plot(X_test[:, 1], np.apply_along_axis(treatment_effect, 1, X_test), color='black', ls='--', label='Baseline')\n",
+    "plt.scatter(X_test[:, 1], T_te, label=\"T-learner\")\n",
+    "plt.scatter(X_test[:, 1], S_te, label=\"S-learner\")\n",
+    "plt.scatter(X_test[:, 1], DA_te, label=\"DA-learner\")\n",
+    "plt.scatter(X_test[:, 1], X_te, label=\"X-learner\")\n",
+    "plt.scatter(X_test[:, 1], DR_te, label=\"DR-learner\")\n",
+    "plt.xlabel('$x_1$')\n",
+    "plt.ylabel('Treatment Effect')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualization of bias distribution\n",
+    "expected_te = np.apply_along_axis(treatment_effect, 1, X_test)\n",
+    "plt.violinplot([np.abs(T_te - expected_te), \n",
+    "                np.abs(S_te - expected_te),\n",
+    "                np.abs(DA_te - expected_te),\n",
+    "                np.abs(X_te - expected_te),\n",
+    "                np.abs(DR_te - expected_te)\n",
+    "               ], showmeans=True)\n",
+    "plt.ylabel(\"Bias distribution\")\n",
+    "plt.xticks([1, 2, 3, 4, 5], ['T-learner', 'S-learner', 'DA-learner', 'X-learner', 'DR-learner'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Example Usage with Semi-synthetic Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. DGP\n",
+    "\n",
+    "We use the Response Surface B from [Hill (2011)](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162) to generate sythetic outcome surfaces from real-world covariates and treatment assignments (Infant Health Development Program data). Since the original data was part of a randomized trial, a subset of the treated infants (those with non-white mothers) has been removed from the data in order to mimic the observational data setting. For more details, see [Hill (2011)](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162).\n",
+    "\n",
+    "\n",
+    "The DGP is described by the following equations:\n",
+    "\n",
+    "$\n",
+    "Y(0) = e^{(X+W)\\beta} + \\epsilon_0, \\;\\epsilon_0 \\sim N(0, 1)\\\\\n",
+    "Y(1) = X\\beta - \\omega + \\epsilon_1, \\;\\epsilon_1 \\sim N(0, 1)\\\\\n",
+    "$\n",
+    "\n",
+    "where $X$ is a covariate matrix, $W$ is a constant matrix with entries equal to $0.5$ and $w$ is a constant calculated such that the CATT equals $4$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from econml.data.dgps import ihdp_surface_B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y, T, X, expected_te = ihdp_surface_B()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Train Estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# T-learner\n",
+    "T_learner.fit(Y, T, X=X)\n",
+    "T_te = T_learner.effect(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# S-learner\n",
+    "S_learner.fit(Y, T, X=X)\n",
+    "S_te = S_learner.effect(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X-learner\n",
+    "X_learner.fit(Y, T, X=X)\n",
+    "X_te = X_learner.effect(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Domain adaptation learner\n",
+    "DA_learner.fit(Y, T, X=X)\n",
+    "DA_te = DA_learner.effect(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Doubly robust learner\n",
+    "DR_learner.fit(Y, T, X=X)\n",
+    "DR_te = DR_learner.effect(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3. Visual Comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualization of bias distribution\n",
+    "plt.violinplot([np.abs(T_te - expected_te), \n",
+    "                np.abs(S_te - expected_te),\n",
+    "                np.abs(DA_te - expected_te),\n",
+    "                np.abs(X_te - expected_te),\n",
+    "                np.abs(DR_te - expected_te)\n",
+    "               ], showmeans=True)\n",
+    "plt.ylabel(\"Bias distribution\")\n",
+    "plt.xticks([1, 2, 3, 4, 5], ['T-learner', 'S-learner', 'DA-learner', 'X-learner', 'DR-learner'])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/OrthoIV and DRIV Examples.ipynb b/notebooks/OrthoIV and DRIV Examples.ipynb
index 263df408..2027002f 100644
--- a/notebooks/OrthoIV and DRIV Examples.ipynb	
+++ b/notebooks/OrthoIV and DRIV Examples.ipynb	
@@ -195,7 +195,8 @@
     }
    ],
    "source": [
-    "func = lambda X: 10\n",
+    "def func(X):\n",
+    "    return 10\n",
     "n = 5000\n",
     "p = 10\n",
     "y, T, Z, X = dgp(n, p, func)"
@@ -241,8 +242,10 @@
     }
    ],
    "source": [
-    "model = lambda: LinearRegression()\n",
-    "model_clf = lambda: LogisticRegression()"
+    "def model():\n",
+    "    return LinearRegression()\n",
+    "def model_clf():\n",
+    "    return LogisticRegression()"
    ]
   },
   {
@@ -732,7 +735,8 @@
     }
    ],
    "source": [
-    "func = lambda X: 10 * X[:, 0]"
+    "def func(X):\n",
+    "    return 10 * X[:, 0]"
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index a38f0cfb..25b1772f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -155,7 +155,6 @@ ignore = [
     "E402", # Module level import not at top of file
     "E713", # Test for membership should be 'not in'
     "E722", # Do not use bare 'except'
-    "E731", # Do not assign a lambda expression, use a def
     "D100", # Missing docstring in public module
     "D101", # Missing docstring in public class
     "D102", # Missing docstring in public method