DoWhy_Causal_Graph_error.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "pip install econml\n",
        "pip install dowhy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqsKm4gGnmOS",
        "outputId": "b24fb180-2815-46de-b112-407f27652343"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed econml-0.14.1 shap-0.41.0 slicer-0.0.7 sparse-0.14.0\n",
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            "Installing collected packages: causal-learn, dowhy\n",
            "Successfully installed causal-learn-0.1.3.7 dowhy-0.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Some imports to get us started\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "\n",
        "# Utilities\n",
        "import os\n",
        "import urllib.request\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from networkx.drawing.nx_pydot import to_pydot\n",
        "from IPython.display import Image, display\n",
        "\n",
        "# Generic ML imports\n",
        "import lightgbm as lgb\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "# DoWhy imports\n",
        "import dowhy\n",
        "from dowhy import CausalModel\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"
      ],
      "metadata": {
        "id": "A-hllWDHnl4q"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "BUrUkxBcnH-p"
      },
      "outputs": [],
      "source": [
        "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",
      "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"
      ],
      "metadata": {
        "id": "AusjYogbnO8l"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = X_data.columns.tolist()"
      ],
      "metadata": {
        "id": "uL4vvj6gnXDy"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "mj0c_dROndTb"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# initiate an EconML cate estimator\n",
        "est = LinearIntentToTreatDRIV(model_t_xwz=model_T_XZ, model_y_xw=model_Y_X,\n",
        "                            flexible_model_effect=flexible_model_effect,\n",
        "                            featurizer=PolynomialFeatures(degree=1, include_bias=False))"
      ],
      "metadata": {
        "id": "o7ROAuwSnfVq"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit through dowhy\n",
        "test_customers = X_data.iloc[:1000]\n",
        "est_dw=est.dowhy.fit(Y, T, Z=Z, X=X_data, outcome_names=[\"days_visited_post\"], treatment_names=[\"became_member\"],\n",
        "                     feature_names=feature_names, instrument_names=[\"easier_signup\"], target_units=test_customers,\n",
        "                     inference=\"statsmodels\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycz5dPQmniAr",
        "outputId": "d6676abb-666e-4ced-cbef-1905d811d2fe"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.020091 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 870\n",
            "[LightGBM] [Info] Number of data points in the train set: 66666, number of used features: 20\n",
            "[LightGBM] [Info] Start training from score 9.969835\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Number of positive: 23480, number of negative: 43186\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007183 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 872\n",
            "[LightGBM] [Info] Number of data points in the train set: 66666, number of used features: 21\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.352204 -> initscore=-0.609367\n",
            "[LightGBM] [Info] Start training from score -0.609367\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.024965 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 870\n",
            "[LightGBM] [Info] Number of data points in the train set: 66666, number of used features: 20\n",
            "[LightGBM] [Info] Start training from score 9.969835\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Number of positive: 23480, number of negative: 43186\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.014121 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 872\n",
            "[LightGBM] [Info] Number of data points in the train set: 66666, number of used features: 21\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.352204 -> initscore=-0.609367\n",
            "[LightGBM] [Info] Start training from score -0.609367\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003376 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 435\n",
            "[LightGBM] [Info] Number of data points in the train set: 66666, number of used features: 10\n",
            "[LightGBM] [Info] Start training from score 2.643659\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.014606 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 870\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 20\n",
            "[LightGBM] [Info] Start training from score 9.953050\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Number of positive: 23480, number of negative: 43187\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006976 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 872\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 21\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.352198 -> initscore=-0.609391\n",
            "[LightGBM] [Info] Start training from score -0.609391\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003506 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 870\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 20\n",
            "[LightGBM] [Info] Start training from score 9.953050\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Number of positive: 23480, number of negative: 43187\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004269 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 872\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 21\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.352198 -> initscore=-0.609391\n",
            "[LightGBM] [Info] Start training from score -0.609391\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007209 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 435\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 10\n",
            "[LightGBM] [Info] Start training from score 2.777323\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004038 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 870\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 20\n",
            "[LightGBM] [Info] Start training from score 9.933625\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Number of positive: 23480, number of negative: 43187\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004487 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 872\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 21\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.352198 -> initscore=-0.609391\n",
            "[LightGBM] [Info] Start training from score -0.609391\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006245 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 870\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 20\n",
            "[LightGBM] [Info] Start training from score 9.933625\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Number of positive: 23480, number of negative: 43187\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006911 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 872\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 21\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.352198 -> initscore=-0.609391\n",
            "[LightGBM] [Info] Start training from score -0.609391\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001906 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 435\n",
            "[LightGBM] [Info] Number of data points in the train set: 66667, number of used features: 10\n",
            "[LightGBM] [Info] Start training from score 2.725469\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize causal graph\n",
        "plt.figure(figsize=(10,8))\n",
        "est_dw.view_model(layout=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "1NmnHHtKnjAG",
        "outputId": "a33c937e-0d56-4baf-bcdd-f6c3110ec3c2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}