Case_Study_Customer_Segmentation_at_TripAdvisor_with_Recommendation_AB_Tests.ipynb

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      "name": "Case Study - Customer Segmentation at TripAdvisor with Recommendation AB Tests.ipynb",
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        "<a href=\"https://colab.research.google.com/github/swap-253/COVID-19-Radiography-Results-Prediction-Using-CNNs/blob/main/Case_Study_Customer_Segmentation_at_TripAdvisor_with_Recommendation_AB_Tests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "<img src=\"https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Recomendation.png\" width=\"400\">\n",
        "\n",
        "# Recommendation A/B Testing: Experimentation with Imperfect Compliance\n",
        "\n",
        "An online business would like to test a new feature or offering of their website and learn its effect on downstream revenue. Furthermore, they would like to know which kind of users respond best to the new version. We call the user-specfic effect a **heterogeneous treatment effect**. \n",
        "\n",
        "Ideally, the business would run an A/B tests between the old and new versions of the website. However, a direct A/B test might not work because the business cannot force the customers to take the new offering. Measuring the effect in this way will be misleading since not every customer exposed to the new offering will take it.\n",
        "\n",
        "The business also cannot look directly at existing data as it will be biased: the users who use the latest website features are most likely the ones who are very engaged on the website and hence spend more on the company's products to begin with. Estimating the effect this way would be overly optimistic.\n",
        "\n",
        "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."
      ]
    },
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      "source": [
        "# Background\n",
        "\n",
        "<img src=\"https://www.marketing91.com/wp-content/uploads/2016/09/Tripadvisor.jpg\" width=\"450\">\n",
        "\n",
        "In this scenario, TripAdvisor would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. \n",
        "\n",
        "A direct A/B test is infeasible because the website cannot force users to become members. Likewise, the travel company can’t look directly at existing data, comparing members and non-members, because the customers who chose to become members are likely already more engaged than other users. \n",
        "\n",
        "**Solution:** TripAdvisor had run an earlier experiment to test the value of a new, faster sign-up process. EconML's IV estimators can exploit this experimental nudge towards membership as an instrument that generates random variation in the likelihood of membership. This is known as an **intent-to-treat** setting: the intention is to give a random group of user the \"treatment\" (access to the easier sign-up process), but not not all users will actually take it. \n",
        "\n",
        "EconML's `IntentToTreatDRIV` estimator model takes advantage of the fact that not every customer who was offered the easier sign-up became a member to learn the effect of membership rather than the effect of receiving the quick sign-up."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwEmHiEm0wcz"
      },
      "source": [
        "#!pip install econml"
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
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      "source": [
        "# Some imports to get us started\n",
        "# Utilities\n",
        "import os\n",
        "import urllib.request\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Generic ML imports\n",
        "import lightgbm as lgb\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "# EconML imports\n",
        "from econml.iv.dr import LinearIntentToTreatDRIV\n",
        "from econml.cate_interpreter import SingleTreeCateInterpreter, \\\n",
        "                                    SingleTreePolicyInterpreter\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
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      "source": [
        "# Data\n",
        "\n",
        "The data* is comprised of:\n",
        " * Features collected in the 28 days prior to the experiment (denoted by the suffix `_pre`)\n",
        " * Experiment variables (whether the use was exposed to the easier signup -> the instrument, and whether the user became a member -> the treatment)\n",
        " * Variables collected in the 28 days after the experiment (denoted by the suffix `_post`).\n",
        "\n",
        "Feature Name | Type | Details \n",
        ":--- |:--- |:--- \n",
        "**days_visited_exp_pre** | X | #days a user visits the attractions pages \n",
        "**days_visited_free_pre** | X | #days a user visits the website through free channels (e.g. domain direct) \n",
        "**days_visited_fs_pre** | X | #days a user visits the flights pages \n",
        "**days_visited_hs_pre** | X | #days a user visits the hotels pages \n",
        "**days_visited_rs_pre** | X | #days a user visits the restaurants pages \n",
        "**days_visited_vrs_pre** | X |#days a user visits the vacation rental pages \n",
        "**locale_en_US** | X | whether the user access the website from the US \n",
        "**os_type** | X | user's operating system (windows, osx, other) \n",
        "**revenue_pre** | X | how much the user spent on the website in the pre-period \n",
        "**easier_signup** | Z | whether the user was exposed to the easier signup process \n",
        "**became_member** | T | whether the user became a member \n",
        "**days_visited_post** | Y | #days a user visits the website in the 28 days after the experiment \n",
        "\n",
        "\n",
        "**To protect the privacy of the travel company's users, the data used in this scenario is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4uQ27bT2mdC"
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      "source": [
        "# Import the sample AB data\n",
        "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/RecommendationAB/ab_sample.csv\"   \n",
        "ab_data = pd.read_csv(file_url)"
      ],
      "execution_count": null,
      "outputs": []
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        "id": "tfWo_MgU225o",
        "outputId": "023469cd-0854-464f-ab2a-f81d95e4c997"
      },
      "source": [
        "# Data sample\n",
        "ab_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   days_visited_exp_pre  ...  days_visited_post\n",
              "0                     1  ...                  1\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "G_FlyBOY25Vs"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHqUvEHY3BuR"
      },
      "source": [
        "The data was generated using the following undelying treatment effect function:\n",
        "\n",
        "$$\n",
        "\\text{treatment_effect} = 0.2 + 0.3 \\cdot \\text{days_visited_free_pre} - 0.2 \\cdot \\text{days_visited_hs_pre} + \\text{os_type_osx}\n",
        "$$\n",
        "\n",
        "The interpretation of this is that users who visited the website before the experiment and/or who use an iPhone tend to benefit from the membership program, whereas users who visited the hotels pages tend to be harmed by membership. **This is the relationship we seek to learn from the data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "md7J6nql27iz"
      },
      "source": [
        "# Define underlying treatment effect function \n",
        "TE_fn = lambda X: (0.2 + 0.3 * X['days_visited_free_pre'] - 0.2 * X['days_visited_hs_pre'] + X['os_type_osx']).values\n",
        "true_TE = TE_fn(X_data)\n",
        "\n",
        "# Define the true coefficients to compare with\n",
        "true_coefs = np.zeros(X_data.shape[1])\n",
        "true_coefs[[1, 3, -2]] = [0.3, -0.2, 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGL-VpOX3Mky"
      },
      "source": [
        "# Get Causal Effects with EconML\n",
        "\n",
        "To learn a linear projection of the treatment effect, we use the `LinearIntentToTreatDRIV` EconML estimator. For a more flexible treatment effect function, use the `IntentToTreatDRIV` estimator instead. \n",
        "\n",
        "The model requires to define some nuissance models (i.e. models we don't really care about but that matter for the analysis): the model for how the outcome $Y$ depends on the features $X$ (`model_Y_X`) and the model for how the treatment $T$ depends on the instrument $Z$ and features $X$ (`model_T_XZ`). Since we don't have any priors on these models, we use generic boosted tree estimators to learn them. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3NoOeci3FkQ"
      },
      "source": [
        "# Define nuissance estimators\n",
        "lgb_T_XZ_params = {\n",
        "    'objective' : 'binary',\n",
        "    'metric' : 'auc',\n",
        "    'learning_rate': 0.1,\n",
        "    'num_leaves' : 30,\n",
        "    'max_depth' : 5\n",
        "}\n",
        "\n",
        "lgb_Y_X_params = {\n",
        "    'metric' : 'rmse',\n",
        "    'learning_rate': 0.1,\n",
        "    'num_leaves' : 30,\n",
        "    'max_depth' : 5\n",
        "}\n",
        "model_T_XZ = lgb.LGBMClassifier(**lgb_T_XZ_params)\n",
        "model_Y_X = lgb.LGBMRegressor(**lgb_Y_X_params)\n",
        "flexible_model_effect = lgb.LGBMRegressor(**lgb_Y_X_params)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxE5SwmJ358l",
        "outputId": "ab87cdeb-d788-452c-b275-8ed8b6f0474f"
      },
      "source": [
        "# Train EconML model\n",
        "model = LinearIntentToTreatDRIV(\n",
        "    model_Y_X = model_Y_X,\n",
        "    model_T_XZ = model_T_XZ,\n",
        "    flexible_model_effect = flexible_model_effect,\n",
        "    featurizer = PolynomialFeatures(degree=1, include_bias=False)\n",
        ")\n",
        "model.fit(Y, T, Z=Z, X=X_data, inference=\"statsmodels\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<econml.iv.dr._dr.LinearIntentToTreatDRIV at 0x7efc2340da90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Jjyg7pI4Bul"
      },
      "source": [
        "# Compare learned coefficients with true model coefficients\n",
        "coef_indices = np.arange(model.coef_.shape[0])\n",
        "# Calculate error bars\n",
        "coef_error = np.asarray(model.coef__interval()) # 90% confidence interval for coefficients\n",
        "coef_error[0, :] = model.coef_ - coef_error[0, :]\n",
        "coef_error[1, :] = coef_error[1, :] - model.coef_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "MTXZx92q4D17",
        "outputId": "24571c35-a23e-4140-da77-3e37932ca52c"
      },
      "source": [
        "plt.errorbar(coef_indices, model.coef_, coef_error, fmt=\"o\", label=\"Learned coefficients\\nand 90% confidence interval\")\n",
        "plt.scatter(coef_indices, true_coefs, color='C1', label=\"True coefficients\")\n",
        "plt.xticks(coef_indices, X_data.columns, rotation='vertical')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDxEdWNQ4MYh"
      },
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "-WWObNrt4GI4",
        "outputId": "2b5dd458-6b87-4345-9da3-ab2dcaaad9aa"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>days_visited_exp_pre</th>       <td>0.001</td>      <td>0.007</td>  <td>0.076</td>   <td>0.94</td>   <td>-0.011</td>    <td>0.012</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>days_visited_free_pre</th>      <td>0.287</td>      <td>0.007</td> <td>38.633</td>    <td>0.0</td>    <td>0.274</td>    <td>0.299</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>days_visited_fs_pre</th>        <td>-0.01</td>      <td>0.007</td> <td>-1.458</td>   <td>0.145</td>  <td>-0.021</td>    <td>0.001</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>days_visited_hs_pre</th>       <td>-0.189</td>      <td>0.007</td> <td>-27.938</td>   <td>0.0</td>    <td>-0.2</td>    <td>-0.178</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>days_visited_rs_pre</th>       <td>-0.001</td>      <td>0.007</td> <td>-0.085</td>   <td>0.932</td>  <td>-0.012</td>    <td>0.011</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>days_visited_vrs_pre</th>      <td>-0.001</td>      <td>0.007</td> <td>-0.145</td>   <td>0.885</td>  <td>-0.012</td>    <td>0.01</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>locale_en_US</th>              <td>-0.039</td>      <td>0.113</td> <td>-0.349</td>   <td>0.727</td>  <td>-0.226</td>    <td>0.147</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>revenue_pre</th>                <td>-0.0</td>        <td>0.0</td>  <td>-1.191</td>   <td>0.234</td>   <td>-0.0</td>      <td>0.0</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>os_type_osx</th>                <td>0.968</td>      <td>0.139</td>  <td>6.976</td>    <td>0.0</td>    <td>0.74</td>     <td>1.196</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>os_type_windows</th>            <td>0.034</td>      <td>0.138</td>  <td>0.247</td>   <td>0.805</td>  <td>-0.193</td>    <td>0.262</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>0.514</td>      <td>0.271</td> <td>1.898</td>  <td>0.058</td>   <td>0.069</td>    <td>0.959</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           0.001  0.007   0.076   0.94   -0.011    0.012\n",
              "days_visited_free_pre          0.287  0.007  38.633    0.0    0.274    0.299\n",
              "days_visited_fs_pre            -0.01  0.007  -1.458  0.145   -0.021    0.001\n",
              "days_visited_hs_pre           -0.189  0.007 -27.938    0.0     -0.2   -0.178\n",
              "days_visited_rs_pre           -0.001  0.007  -0.085  0.932   -0.012    0.011\n",
              "days_visited_vrs_pre          -0.001  0.007  -0.145  0.885   -0.012     0.01\n",
              "locale_en_US                  -0.039  0.113  -0.349  0.727   -0.226    0.147\n",
              "revenue_pre                     -0.0    0.0  -1.191  0.234     -0.0      0.0\n",
              "os_type_osx                    0.968  0.139   6.976    0.0     0.74    1.196\n",
              "os_type_windows                0.034  0.138   0.247  0.805   -0.193    0.262\n",
              "                       CATE Intercept Results                      \n",
              "===================================================================\n",
              "               point_estimate stderr zstat pvalue ci_lower ci_upper\n",
              "-------------------------------------------------------------------\n",
              "cate_intercept          0.514  0.271 1.898  0.058    0.069    0.959\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",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ndUWzKo4T7T"
      },
      "source": [
        "test_customers = X_data.iloc[:1000]\n",
        "true_customer_TE = TE_fn(test_customers)\n",
        "model_customer_TE = model.effect(test_customers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "jKJjRRoL4dA7",
        "outputId": "c21f56b9-4caa-4d4d-e8a9-a6792c331a4b"
      },
      "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",
        "plt.xlabel(\"True treatment effect\")\n",
        "plt.ylabel(\"Predicted treatment effect\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXN-uQdc49uV"
      },
      "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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "P1udSSD34y0z",
        "outputId": "5c6f9cc1-4f91-45fc-8f9a-4e8825524f6e"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eni2Xze5ULI"
      },
      "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. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "hsZsTefa5A1C",
        "outputId": "e3c10678-4a50-4e61-ef51-b3e85e9b8c44"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qk4_f4tx5gZz"
      },
      "source": [
        "# Conclusions\n",
        "\n",
        "In this notebook, we have demonstrated the power of using EconML to:\n",
        "\n",
        "* Get valid causal insights in seemingly impossible scenarios\n",
        "* Intepret the resulting individual-level treatment effects\n",
        "* 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/microsoft/EconML) or our [documentation](https://econml.azurewebsites.net/). "
      ]
    }
  ]
}