notebooks/overviews/Be careful when interpreting predictive models in search of causal insights.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Be careful when interpreting predictive models in search of causal insights\n",
    "\n",
    "*A joint article about causality and interpretable machine learning with Eleanor Dillon, Jacob LaRiviere, Scott Lundberg, Jonathan Roth, and Vasilis Syrgkanis from Microsoft.*\n",
    "\n",
    "Predictive machine learning models like XGBoost become even more powerful when paired with interpretability tools like SHAP. These tools identify the most informative relationships between the input features and the predicted outcome, which is useful for explaining what the model is doing, getting stakeholder buy-in, and diagnosing potential problems. It is tempting to take this analysis one step further and assume that interpretation tools can also identify what features decision makers should manipulate if they want to change outcomes in the future. However, **in this article, we discuss how using predictive models to guide this kind of policy choice can often be misleading.**\n",
    "\n",
    "The reason relates to the fundamental difference between *correlation* and *causation*. SHAP makes transparent the correlations picked up by predictive ML models. But making correlations transparent does not make them causal! All predictive models implicitly assume that everyone will keep behaving the same way in the future, and therefore correlation patterns will stay constant. To understand what happens if someone starts behaving differently, we need to build causal models, which requires making assumptions and using the tools of causal analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A subscriber retention example\n",
    "\n",
    "Imagine we are tasked with building a model that predicts whether a customer will renew their product subscription. Let's assume that after a bit of digging we manage to get eight features which are important for predicting churn: customer discount, ad spending, customer's monthly usage, last upgrade, bugs reported by a customer, interactions with a customer, sales calls with a customer, and macroeconomic activity. We then use those features to train a basic XGBoost model to predict if a customer will renew their subscription when it expires:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:19:54.988406Z",
     "start_time": "2021-06-02T13:19:54.519512Z"
    }
   },
   "outputs": [],
   "source": [
    "# This cell defines the functions we use to generate the data in our scenario\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "import sklearn\n",
    "import xgboost\n",
    "\n",
    "class FixableDataFrame(pd.DataFrame):\n",
    "    \"\"\" Helper class for manipulating generative models.\n",
    "    \"\"\"\n",
    "    def __init__(self, *args, fixed={}, **kwargs):\n",
    "        self.__dict__[\"__fixed_var_dictionary\"] = fixed\n",
    "        super(FixableDataFrame, self).__init__(*args, **kwargs)\n",
    "    def __setitem__(self, key, value):\n",
    "        out = super(FixableDataFrame, self).__setitem__(key, value)\n",
    "        if isinstance(key, str) and key in self.__dict__[\"__fixed_var_dictionary\"]:\n",
    "            out = super(FixableDataFrame, self).__setitem__(key, self.__dict__[\"__fixed_var_dictionary\"][key])\n",
    "        return out\n",
    "\n",
    "# generate the data\n",
    "def generator(n, fixed={}, seed=0):\n",
    "    \"\"\" The generative model for our subscriber retention example.\n",
    "    \"\"\"\n",
    "    if seed is not None:\n",
    "        np.random.seed(seed)\n",
    "    X = FixableDataFrame(fixed=fixed)\n",
    "    \n",
    "    # the number of sales calls made to this customer\n",
    "    X[\"Sales calls\"] = np.random.uniform(0, 4, size=(n,)).round()\n",
    "    \n",
    "    # the number of sales calls made to this customer\n",
    "    X[\"Interactions\"] = X[\"Sales calls\"] + np.random.poisson(0.2, size=(n,))\n",
    "    \n",
    "    # the health of the regional economy this customer is a part of\n",
    "    X[\"Economy\"] = np.random.uniform(0, 1, size=(n,))\n",
    "    \n",
    "    # the time since the last product upgrade when this customer came up for renewal\n",
    "    X[\"Last upgrade\"] = np.random.uniform(0, 20, size=(n,))\n",
    "    \n",
    "    # how much the user perceives that they need the product\n",
    "    X[\"Product need\"] = (X[\"Sales calls\"] * 0.1 + np.random.normal(0, 1, size=(n,)))\n",
    "    \n",
    "    # the fractional discount offered to this customer upon renewal\n",
    "    X[\"Discount\"] = ((1-scipy.special.expit(X[\"Product need\"])) * 0.5 + 0.5 * np.random.uniform(0, 1, size=(n,))) / 2\n",
    "    \n",
    "    # What percent of the days in the last period was the user actively using the product\n",
    "    X[\"Monthly usage\"] = scipy.special.expit(X[\"Product need\"] * 0.3 + np.random.normal(0, 1, size=(n,)))\n",
    "    \n",
    "    # how much ad money we spent per user targeted at this user (or a group this user is in) \n",
    "    X[\"Ad spend\"] = X[\"Monthly usage\"] * np.random.uniform(0.99, 0.9, size=(n,)) + (X[\"Last upgrade\"] < 1) + (X[\"Last upgrade\"] < 2)\n",
    "    \n",
    "    # how many bugs did this user encounter in the since their last renewal\n",
    "    X[\"Bugs faced\"] = np.array([np.random.poisson(v*2) for v in X[\"Monthly usage\"]])\n",
    "    \n",
    "    # how many bugs did the user report?\n",
    "    X[\"Bugs reported\"] = (X[\"Bugs faced\"] * scipy.special.expit(X[\"Product need\"])).round()\n",
    "    \n",
    "    # did the user renew?\n",
    "    X[\"Did renew\"] = scipy.special.expit(7 * (\n",
    "          0.18 * X[\"Product need\"] \\\n",
    "        + 0.08 * X[\"Monthly usage\"] \\\n",
    "        + 0.1 * X[\"Economy\"] \\\n",
    "        + 0.05 * X[\"Discount\"] \\\n",
    "        + 0.05 * np.random.normal(0, 1, size=(n,)) \\\n",
    "        + 0.05 * (1 - X['Bugs faced'] / 20) \\\n",
    "        + 0.005 * X[\"Sales calls\"] \\\n",
    "        + 0.015 * X[\"Interactions\"] \\\n",
    "        + 0.1 / (X[\"Last upgrade\"]/4 + 0.25)\n",
    "        + X[\"Ad spend\"] * 0.0 - 0.45\n",
    "    ))\n",
    "    \n",
    "    # in real life we would make a random draw to get either 0 or 1 for if the\n",
    "    # customer did or did not renew. but here we leave the label as the probability\n",
    "    # so that we can get less noise in our plots. Uncomment this line to get\n",
    "    # noiser causal effect lines but the same basic results\n",
    "    X[\"Did renew\"] = scipy.stats.bernoulli.rvs(X[\"Did renew\"]) \n",
    "    \n",
    "    return X\n",
    "\n",
    "def user_retention_dataset():\n",
    "    \"\"\" The observed data for model training.\n",
    "    \"\"\"\n",
    "    n = 10000\n",
    "    X_full = generator(n)\n",
    "    y = X_full[\"Did renew\"]\n",
    "    X = X_full.drop([\"Did renew\", \"Product need\", \"Bugs faced\"], axis=1)\n",
    "    return X, y\n",
    "\n",
    "def fit_xgboost(X, y):\n",
    "    \"\"\" Train an XGBoost model with early stopping.\n",
    "    \"\"\"\n",
    "    X_train,X_test,y_train,y_test = sklearn.model_selection.train_test_split(X, y)\n",
    "    dtrain = xgboost.DMatrix(X_train, label=y_train)\n",
    "    dtest = xgboost.DMatrix(X_test, label=y_test)\n",
    "    model = xgboost.train(\n",
    "        { \"eta\": 0.001, \"subsample\": 0.5, \"max_depth\": 2, \"objective\": \"reg:logistic\"}, dtrain, num_boost_round=200000,\n",
    "        evals=((dtest, \"test\"),), early_stopping_rounds=20, verbose_eval=False\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:20:03.641067Z",
     "start_time": "2021-06-02T13:19:55.372220Z"
    }
   },
   "outputs": [],
   "source": [
    "X, y = user_retention_dataset()\n",
    "model = fit_xgboost(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have our XGBoost customer retention model in hand, we can begin exploring what it has learned with an interpretability tool like SHAP. We start by plotting the global importance of each feature in the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:20:10.386335Z",
     "start_time": "2021-06-02T13:20:03.642300Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "explainer = shap.Explainer(model)\n",
    "shap_values = explainer(X)\n",
    "\n",
    "clust = shap.utils.hclust(X, y, linkage=\"complete\")\n",
    "shap.plots.bar(shap_values, clustering=clust, clustering_cutoff=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bar plot shows that the discount offered, ad spend, and number of bugs reported are the top three factors driving the model's prediction of customer retention. This is interesting and at first glance looks reasonable. The bar plot also includes a feature redundancy clustering which we will use later.\n",
    "\n",
    "However, when we dig deeper and look at how changing the value of each feature impacts the model's prediction, we find some unintuitive patterns. SHAP scatter plots show how changing the value of a feature impacts the model's prediction of renewal probabilities. If the blue dots follow an increasing pattern, this means that the larger the feature, the higher is the model's predicted renewal probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:20:11.922321Z",
     "start_time": "2021-06-02T13:20:10.387598Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shap.plots.scatter(shap_values, ylabel=\"SHAP value\\n(higher means more likely to renew)\")\n",
    "for col in list(X.columns):\n",
    "    shap.plots.scatter(shap_values[:, col], title=\"SHAP value\\n(higher means more likely to renew)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction tasks versus causal tasks\n",
    "The scatter plots show some surprising findings:\n",
    "- Users who report more bugs are more likely to renew!\n",
    "- Users with larger discounts are less likely to renew!\n",
    "\n",
    "We triple-check our code and data pipelines to rule out a bug, then talk to some business partners who offer an intuitive explanation:\n",
    "- Users with high usage who value the product are more likely to report bugs and to renew their subscriptions.\n",
    "- The sales force tends to give high discounts to customers they think are less likely to be interested in the product, and these customers have higher churn.\n",
    "\n",
    "Are these at-first counter-intuitive relationships in the model a problem? *That depends on what our goal is!*\n",
    "\n",
    "Our original goal for this model was to predict customer retention, which is useful for projects like estimating future revenue for financial planning. Since users reporting more bugs are in fact more likely to renew, capturing this relationship in the model is helpful for prediction. As long as our model has good fit out-of-sample, we should be able to provide finance with a good prediction, and therefore shouldn't worry about the direction of this relationship in the model.\n",
    "\n",
    "This is an example of a class of tasks called **prediction tasks**. In a prediction task, the goal is to predict an outcome `Y` (e.g. renewals) given a set of features `X`. A key component of a prediction exercise is that we only care that the prediction `model(X)` is close to `Y` in data distributions similar to our training set. A simple correlation between `X` and `Y` can be helpful for these types of predictions.\n",
    "\n",
    "However, suppose a second team picks up our prediction model with the new goal of determining what actions our company can take to retain more customers. This team cares a lot about how each `X` feature relates to `Y`, not just in our training distribution, but the counterfactual scenario produced when the world changes. In that use case, it is no longer sufficient to identify a stable correlation between variables; this team wants to know whether manipulating feature `X` will cause a change in `Y`. Picture the face of the chief of engineering when you tell him that you want him to introduce new bugs to increase customer renewals!\n",
    "\n",
    "This is an example of a class of tasks called **causal tasks**. In a causal task, we want to know how changing an aspect of the world X (e.g bugs reported) affects an outcome `Y` (renewals). In this case, it's critical to know whether changing `X` causes an increase in `Y`, or whether the relationship in the data is merely correlational."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The challenges of estimating causal effects\n",
    "\n",
    "A useful tool to understanding causal relationships is writing down a causal graph of the data generating process we're interested in. A causal graph of our example illustrates why the robust predictive relationships picked up by our XGBoost customer retention model differ from the causal relationships of interest to the team that wants to plan interventions to increase retention. This graph is just a summary of the true data generating mechanism (which is defined above). Solid ovals represent features that we observe, while dashed ovals represent hidden features that we don't measure. Each feature is a function of all the features with an arrow to it, plus some random effects.\n",
    "\n",
    "In our example we know the causal graph because we simulate the data. In practice the true causal graph will not be known, but we may be able to use context-specific domain knowledge about how the world works  to infer which relationships can or cannot exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:21:03.445175Z",
     "start_time": "2021-06-02T13:21:02.941311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.42.3 (20191010.1750)\n",
       " -->\n",
       "<!-- Title: %3 Pages: 1 -->\n",
       "<svg width=\"572pt\" height=\"332pt\"\n",
       " viewBox=\"0.00 0.00 571.82 332.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 328)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-328 567.82,-328 567.82,4 -4,4\"/>\n",
       "<!-- Bugs reported -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>Bugs reported</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"43.56\" cy=\"-18\" rx=\"43.62\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"43.56\" y=\"-15.5\" font-family=\"Times,serif\" font-size=\"10.00\">Bugs reported</text>\n",
       "</g>\n",
       "<!-- Monthly usage -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>Monthly usage</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"139.56\" cy=\"-162\" rx=\"45.83\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"139.56\" y=\"-159.5\" font-family=\"Times,serif\" font-size=\"10.00\">Monthly usage</text>\n",
       "</g>\n",
       "<!-- Ad spend -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>Ad spend</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"200.56\" cy=\"-90\" rx=\"32.93\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"200.56\" y=\"-87.5\" font-family=\"Times,serif\" font-size=\"10.00\">Ad spend</text>\n",
       "</g>\n",
       "<!-- Monthly usage&#45;&gt;Ad spend -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>Monthly usage&#45;&gt;Ad spend</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M153.71,-144.76C161.6,-135.71 171.55,-124.29 180.26,-114.3\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"182.98,-116.5 186.91,-106.67 177.7,-111.91 182.98,-116.5\"/>\n",
       "</g>\n",
       "<!-- Bugs faced -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>Bugs faced</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"75.56\" cy=\"-90\" rx=\"36.49\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"75.56\" y=\"-87.5\" font-family=\"Times,serif\" font-size=\"10.00\">Bugs faced</text>\n",
       "</g>\n",
       "<!-- Monthly usage&#45;&gt;Bugs faced -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>Monthly usage&#45;&gt;Bugs faced</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M124.72,-144.76C116.44,-135.71 105.99,-124.29 96.86,-114.3\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"99.21,-111.68 89.88,-106.67 94.05,-116.41 99.21,-111.68\"/>\n",
       "</g>\n",
       "<!-- Did renew -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>Did renew</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"329.56\" cy=\"-18\" rx=\"34.96\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"329.56\" y=\"-15.5\" font-family=\"Times,serif\" font-size=\"10.00\">Did renew</text>\n",
       "</g>\n",
       "<!-- Monthly usage&#45;&gt;Did renew -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>Monthly usage&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M138.41,-143.98C138.04,-124.19 140.55,-91.71 158.56,-72 190.8,-36.72 246,-24.7 284.95,-20.73\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"285.32,-24.21 294.97,-19.84 284.7,-17.23 285.32,-24.21\"/>\n",
       "</g>\n",
       "<!-- Sales calls -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>Sales calls</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"459.56\" cy=\"-306\" rx=\"34.96\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"459.56\" y=\"-303.5\" font-family=\"Times,serif\" font-size=\"10.00\">Sales calls</text>\n",
       "</g>\n",
       "<!-- Interactions -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>Interactions</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"525.56\" cy=\"-234\" rx=\"38.03\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"525.56\" y=\"-231.5\" font-family=\"Times,serif\" font-size=\"10.00\">Interactions</text>\n",
       "</g>\n",
       "<!-- Sales calls&#45;&gt;Interactions -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>Sales calls&#45;&gt;Interactions</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M474.21,-289.46C482.8,-280.35 493.83,-268.65 503.47,-258.43\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"506.18,-260.65 510.5,-250.98 501.09,-255.85 506.18,-260.65\"/>\n",
       "</g>\n",
       "<!-- Product need -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>Product need</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"334.56\" cy=\"-234\" rx=\"41.41\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"334.56\" y=\"-231.5\" font-family=\"Times,serif\" font-size=\"10.00\">Product need</text>\n",
       "</g>\n",
       "<!-- Sales calls&#45;&gt;Product need -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>Sales calls&#45;&gt;Product need</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M436.92,-292.32C417.72,-281.57 389.89,-265.98 368.1,-253.78\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"369.6,-250.61 359.17,-248.78 366.18,-256.72 369.6,-250.61\"/>\n",
       "</g>\n",
       "<!-- Sales calls&#45;&gt;Did renew -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>Sales calls&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M461.21,-287.99C464.31,-246.9 466.9,-140.56 419.56,-72 407.01,-53.83 386.04,-40.95 367.49,-32.45\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"368.64,-29.13 358.08,-28.39 365.88,-35.56 368.64,-29.13\"/>\n",
       "</g>\n",
       "<!-- Economy -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>Economy</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"360.56\" cy=\"-90\" rx=\"32.93\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"360.56\" y=\"-87.5\" font-family=\"Times,serif\" font-size=\"10.00\">Economy</text>\n",
       "</g>\n",
       "<!-- Economy&#45;&gt;Did renew -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>Economy&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M353.21,-72.41C349.64,-64.34 345.25,-54.43 341.23,-45.35\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"344.32,-43.68 337.07,-35.96 337.92,-46.52 344.32,-43.68\"/>\n",
       "</g>\n",
       "<!-- Discount -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>Discount</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"334.56\" cy=\"-162\" rx=\"31.58\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"334.56\" y=\"-159.5\" font-family=\"Times,serif\" font-size=\"10.00\">Discount</text>\n",
       "</g>\n",
       "<!-- Discount&#45;&gt;Did renew -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>Discount&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M328.07,-144.1C324.48,-133.84 320.39,-120.36 318.56,-108 315.51,-87.34 318.68,-63.89 322.38,-46.25\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"325.89,-46.62 324.71,-36.09 319.07,-45.06 325.89,-46.62\"/>\n",
       "</g>\n",
       "<!-- Last upgrade -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>Last upgrade</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"244.56\" cy=\"-162\" rx=\"40.74\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"244.56\" y=\"-159.5\" font-family=\"Times,serif\" font-size=\"10.00\">Last upgrade</text>\n",
       "</g>\n",
       "<!-- Last upgrade&#45;&gt;Ad spend -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>Last upgrade&#45;&gt;Ad spend</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M234.13,-144.41C228.82,-135.95 222.24,-125.49 216.32,-116.08\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"219.2,-114.07 210.91,-107.47 213.27,-117.8 219.2,-114.07\"/>\n",
       "</g>\n",
       "<!-- Last upgrade&#45;&gt;Did renew -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>Last upgrade&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M254.48,-144.43C269.26,-119.74 297.17,-73.11 314.49,-44.18\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"317.57,-45.84 319.71,-35.46 311.57,-42.24 317.57,-45.84\"/>\n",
       "</g>\n",
       "<!-- Interactions&#45;&gt;Did renew -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>Interactions&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M524.8,-215.98C522.45,-183.63 512.83,-112.88 473.56,-72 447.08,-44.44 405.06,-31.12 373.22,-24.74\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"373.75,-21.28 363.28,-22.9 372.48,-28.16 373.75,-21.28\"/>\n",
       "</g>\n",
       "<!-- Product need&#45;&gt;Bugs reported -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>Product need&#45;&gt;Bugs reported</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M293.17,-232.92C240.68,-230.98 149.26,-221.3 84.56,-180 50.84,-158.48 43.21,-145.95 30.56,-108 23.76,-87.6 28.04,-63.38 33.45,-45.37\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"36.79,-46.4 36.59,-35.81 30.14,-44.21 36.79,-46.4\"/>\n",
       "</g>\n",
       "<!-- Product need&#45;&gt;Monthly usage -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>Product need&#45;&gt;Monthly usage</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M303.66,-221.91C270.74,-210.09 218.4,-191.3 181.7,-178.13\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"182.86,-174.82 172.27,-174.74 180.5,-181.41 182.86,-174.82\"/>\n",
       "</g>\n",
       "<!-- Product need&#45;&gt;Discount -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>Product need&#45;&gt;Discount</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M334.56,-215.7C334.56,-207.98 334.56,-198.71 334.56,-190.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"338.06,-190.1 334.56,-180.1 331.06,-190.1 338.06,-190.1\"/>\n",
       "</g>\n",
       "<!-- Product need&#45;&gt;Did renew -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>Product need&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M350.58,-217.12C378.07,-187.84 428.88,-123.63 402.56,-72 394.43,-56.05 379.01,-43.78 364.43,-35.08\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"365.98,-31.94 355.54,-30.13 362.57,-38.05 365.98,-31.94\"/>\n",
       "</g>\n",
       "<!-- Bugs faced&#45;&gt;Bugs reported -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>Bugs faced&#45;&gt;Bugs reported</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M67.81,-72.05C64.12,-63.97 59.62,-54.12 55.5,-45.11\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"58.58,-43.43 51.24,-35.79 52.21,-46.34 58.58,-43.43\"/>\n",
       "</g>\n",
       "<!-- Bugs faced&#45;&gt;Did renew -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>Bugs faced&#45;&gt;Did renew</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M104.26,-78.84C111.2,-76.49 118.62,-74.08 125.56,-72 181.63,-55.21 247.27,-38.71 288.63,-28.69\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"289.7,-32.04 298.61,-26.29 288.06,-25.23 289.7,-32.04\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x7fd804a75810>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "names = [\n",
    "         \"Bugs reported\", \"Monthly usage\", \"Sales calls\", \"Economy\",\n",
    "         \"Discount\", \"Last upgrade\", \"Ad spend\", \"Interactions\"\n",
    "]\n",
    "g = graphviz.Digraph()\n",
    "for name in names:\n",
    "    g.node(name, fontsize=\"10\")\n",
    "g.node(\"Product need\", style=\"dashed\", fontsize=\"10\")\n",
    "g.node(\"Bugs faced\", style=\"dashed\", fontsize=\"10\")\n",
    "g.node(\"Did renew\", style=\"filled\", fontsize=\"10\")\n",
    "\n",
    "g.edge(\"Product need\", \"Did renew\")\n",
    "g.edge(\"Product need\", \"Discount\")\n",
    "g.edge(\"Product need\", \"Bugs reported\")\n",
    "g.edge(\"Product need\", \"Monthly usage\")\n",
    "g.edge(\"Discount\", \"Did renew\")\n",
    "g.edge(\"Monthly usage\", \"Bugs faced\")\n",
    "g.edge(\"Monthly usage\", \"Did renew\")\n",
    "g.edge(\"Monthly usage\", \"Ad spend\")\n",
    "g.edge(\"Economy\", \"Did renew\")\n",
    "g.edge(\"Sales calls\", \"Did renew\")\n",
    "g.edge(\"Sales calls\", \"Product need\")\n",
    "g.edge(\"Sales calls\", \"Interactions\")\n",
    "g.edge(\"Interactions\", \"Did renew\")\n",
    "g.edge(\"Bugs faced\", \"Did renew\")\n",
    "g.edge(\"Bugs faced\", \"Bugs reported\")\n",
    "g.edge(\"Last upgrade\", \"Did renew\")\n",
    "g.edge(\"Last upgrade\", \"Ad spend\")\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are lots of relationships in this graph, but the first important concern is that some of the features we can measure are influenced by **unmeasured confounding features** like product need and bugs faced. For example, users who report more bugs are encountering more bugs because they use the product more, and they are also more likely to report those bugs because they need the product more. Product need has its own direct causal effect on renewal. Because we can't directly measure product need, the correlation we end up capturing in predictive models between bugs reported and renewal combines a small negative direct effect of bugs faced and a large positive confounding effect from product need. The figure below plots the SHAP values in our example against the true causal effect of each feature (known in this example since we generated the data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:24:09.505493Z",
     "start_time": "2021-06-02T13:24:07.354864Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "scatter() got an unexpected keyword argument 'overlay'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-1ce1364c988d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m shap.plots.scatter(shap_values, title=\"SHAP value\\n(higher means more likely to renew)\", overlay={\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0;34m\"True causal effects\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmarginal_effects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m })\n",
      "\u001b[0;31mTypeError\u001b[0m: scatter() got an unexpected keyword argument 'overlay'"
     ]
    }
   ],
   "source": [
    "def marginal_effects(generative_model, num_samples=100, columns=None, max_points=20, logit=True, seed=0):\n",
    "    \"\"\" Helper function to compute the true marginal causal effects.\n",
    "    \"\"\"\n",
    "    X = generative_model(num_samples)\n",
    "    if columns is None:\n",
    "        columns = X.columns\n",
    "    ys = [[] for _ in columns]\n",
    "    xs = [X[c].values for c in columns]\n",
    "    xs = np.sort(xs, axis=1)\n",
    "    xs = [xs[i] for i in range(len(xs))]\n",
    "    for i,c in enumerate(columns):\n",
    "        xs[i] = np.unique([np.nanpercentile(xs[i], v, interpolation='nearest') for v in np.linspace(0, 100, max_points)])\n",
    "        for x in xs[i]:\n",
    "            Xnew = generative_model(num_samples, fixed={c: x}, seed=seed)\n",
    "            val = Xnew[\"Did renew\"].mean()\n",
    "            if logit:\n",
    "                val = scipy.special.logit(val)\n",
    "            ys[i].append(val)\n",
    "        ys[i] = np.array(ys[i])\n",
    "    ys = [ys[i] - ys[i].mean() for i in range(len(ys))]\n",
    "    return list(zip(xs, ys))\n",
    "\n",
    "shap.plots.scatter(shap_values, title=\"SHAP value\\n(higher means more likely to renew)\", overlay={\n",
    "    \"True causal effects\": marginal_effects(generator, 10000, X.columns)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 16 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 918
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def marginal_effects(generative_model, num_samples=100, columns=None, max_points=20, logit=True, seed=0):\n",
    "    \"\"\" Helper function to compute the true marginal causal effects.\n",
    "    \"\"\"\n",
    "    X = generative_model(num_samples)\n",
    "    if columns is None:\n",
    "        columns = X.columns\n",
    "    ys = [[] for _ in columns]\n",
    "    xs = [X[c].values for c in columns]\n",
    "    xs = np.sort(xs, axis=1)\n",
    "    xs = [xs[i] for i in range(len(xs))]\n",
    "    for i,c in enumerate(columns):\n",
    "        xs[i] = np.unique([np.nanpercentile(xs[i], v, interpolation='nearest') for v in np.linspace(0, 100, max_points)])\n",
    "        for x in xs[i]:\n",
    "            Xnew = generative_model(num_samples, fixed={c: x}, seed=seed)\n",
    "            val = Xnew[\"Did renew\"].mean()\n",
    "            if logit:\n",
    "                val = scipy.special.logit(val)\n",
    "            ys[i].append(val)\n",
    "        ys[i] = np.array(ys[i])\n",
    "    ys = [ys[i] - ys[i].mean() for i in range(len(ys))]\n",
    "    return list(zip(xs, ys))\n",
    "\n",
    "shap.plots.scatter(shap_values, ylabel=\"SHAP value\\n(higher means more likely to renew)\", overlay={\n",
    "    \"True causal effects\": marginal_effects(generator, 10000, X.columns)\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictive model captures an overall positive effect of bugs reported on retention (as shown with SHAP), even though the causal effect of reporting a bug is zero, and the effect of encoutering a bug is negative.\n",
    "\n",
    "We see a similar problem with Discounts, which are also driven by unobserved customer need for the product. Our predictive model finds a negative relationship between discounts and retention, driven by this correlation with the unobserved feature, Product Need, even though there is actually a small positive causal effect of discounts on renewal! Put another way, if two customers with have the same Product Need and are otherwise similar, then the customer with the larger discount is more likely to renew.\n",
    "\n",
    "This plot also reveals a second, sneakier problem when we start to interpret predictive models as if they were causal. Notice that Ad Spend has a similar problem - it has no causal effect on retention (the black line is flat), but the predictive model is picking up a positive effect!\n",
    "\n",
    "In this case, Ad Spend is only driven by Last Upgrade and Monthly Usage, so we don't have an *unobserved* confounding problem, instead we have an *observed* confounding problem. There is statistical redundancy between Ad Spend and features that influence Ad Spend. When we have the same information captured by several features, predictive models can use any of those features for prediction, even though they are not all causal. While Ad Spend has no causal effect on renewal itself, it is strongly correlated with several features that do drive renewal. Our regularized model identifies Ad Spend as a useful predictor because it summarizes multiple causal drivers (so leading to a sparser model), but that becomes seriously misleading if we start to interpret it as a causal effect.\n",
    "\n",
    "We will now tackle each piece of our example in turn to illustrate when predictive models can accurately measure causal effects, and when they cannot. We will also introduce some causal tools that can sometimes estimate causal effects in cases where predictive models fail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When predictive models can answer causal questions\n",
    "\n",
    "Let's start with the successes in our example. Notice that our predictive model does a good job of capturing the real causal effect of the Economy feature (a better economy has a positive effect on retention). So when can we expect predictive models to capture true causal effects?\n",
    "\n",
    "The important ingredient that allowed XGBoost to get a good causal effect estimate for Economy is the feature's strong independent component (in this simulation); its predictive power for retention is not strongly redundant with any other measured features, or with any unmeasured confounders. In consequence, it is not subject to bias from either unmeasured confounders or feature redundancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:49:54.574360Z",
     "start_time": "2021-06-02T13:49:54.386868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Economy is independent of other measured features.\n",
    "shap.plots.bar(shap_values, clustering=clust, clustering_cutoff=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have added clustering to the right side of the SHAP bar plot we can see the redundancy structure of our data as a dendrogram. When features merge together at the bottom (left) of the dendrogram it means that that the information those features contain about the outcome (renewal) is very redundant and the model could have used either feature. When features merge together at the top (right) of the dendrogram it means the information they contain about the outcome is independent from each other.\n",
    "\n",
    "We can see that Economy is independent from all the other measured features by noting that Economy does not merge with any other features until the very top of the clustering dendrogram. This tells us that Economy does not suffer from observed confounding. But to trust that the Economy effect is causal we also need to check for unobserved confounding. Checking for unmeasured confounders is harder and requires using domain knowledge (provided by the business partners in our example above).\n",
    "\n",
    "For classic predictive ML models to deliver causal results the features need to be independent not only of other features in the model, but also of unobserved confounders. It's not common to find examples of drivers of interest that exhibit this level of independence naturally, but we can often find examples of independent features when our data contains some experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When predictive models cannot answer causal questions but causal inference methods can help\n",
    "\n",
    "In most real-world datasets features are not independent and unconfounded, so standard predictive models will not learn the true causal effects. As a result, explaining them with SHAP will not reveal causal effects. But all is not lost, sometimes we can fix or at least minimize this problem using the tools of observational causal inference.\n",
    "\n",
    "### Observed confounding\n",
    "The first scenario where causal inference can help is observed confounding. A feature is \"confounded\" when there is another feature that causally affects both the original feature and the outcome we are predicting. If we can measure that other feature it is called an *observed confounder*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T13:59:03.307874Z",
     "start_time": "2021-06-02T13:59:03.163652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ad spend is very redundant with Monthly usage and Last upgrade.\n",
    "shap.plots.bar(shap_values, clustering=clust, clustering_cutoff=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of this in our scenario is the Ad Spend feature. Even though Ad Spend has no direct causal effect on retention, it is correlated with the Last Upgrade and Monthly Usage features, which do drive retention. Our predictive model identifies Ad Spend as the one of the best single predictors of retention because it captures so many of the true causal drivers through correlations. XGBoost imposes regularization, which is a fancy way of saying that it tries to choose the simplest possible model that still predicts well. If it could predict equally well using one feature rather than three, it will tend to do that to avoid overfitting. But this means that if Ad Spend is highly correlated with both Last Upgrade and Monthly Usage, XGBoost may use Ad Spend instead of the causal features! This property of XGBoost (or any other machine learning model with regularization) is very useful for generating robust predictions of future retention, but not good for understanding which features we should manipulate if we want to increase retention.\n",
    "\n",
    "This highlights the importance of matching the right modeling tools to each question. Unlike the bug reporting example, there is nothing intuitively wrong with the conclusion that increasing ad spend increases retention. Without proper attention to what our predictive model is, and is not, measuring, we could easily have proceeded with this finding and only learned our mistake after increasing spending on advertising and not getting the renewal results we expected.\n",
    "\n",
    "### Observational Causal Inference\n",
    "The good news for Ad Spend is that we can measure all the features that could confound it (those features with arrows into Ad Spend in our causal graph above). Therefore, this is an example of observed confounding, and we should be able to disentangle the correlation patterns using only the data we've already collected; we just need to use the right tools from observational causal inference. These tools allow us to specify what features could confound Ad Spend and then adjust for those features, to get an **unconfounded** estimate of the causal effect of Ad Spend on product renewal.\n",
    "\n",
    "One particularly flexible tool for observational causal inference is double/debiased machine learning. It uses any machine learning model you want to first deconfound the feature of interest (i.e. Ad Spend) and then estimate the average causal effect of changing that feature (i.e. the average slope of the causal effect).\n",
    "\n",
    "Double ML works as follows:\n",
    "1. Train a model to predict a feature of interest (i.e. Ad Spend) using a set of possible confounders (i.e. any features not caused by Ad Spend).\n",
    "2. Train a model to predict the outcome (i.e. Did Renew) using the same set of possible confounders.\n",
    "3. Train a model to predict the residual variation of the outcome (the variation left after subtracting our prediction) using the residual variation of the causal feature of interest.\n",
    "\n",
    "The intuition is that if Ad Spend causes renewal, then the part of Ad Spend that can't be predicted by other confounding features should be correlated with the part of renewal that can't be predicted by other confounding features. Stated another way, double ML assumes that there is an independent (unobserved) noise feature that impacts Ad Spend (since Ad Spend is not perfectly determined by the other features), so we can impute the value of this independent noise feature and then train a model on this independent feature to predict the output.\n",
    "\n",
    "While we could do all the double ML steps manually, it is easier to use a causal inference package like econML or CausalML. Here we use econML's LinearDML model. This returns a P-value of whether that treatment has a non-zero a causal effect, and works beautifully in our scenario, correctly identifying that there is no evidence for a causal effect of ad spending on renewal (P-value = 0.85):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T14:03:45.850277Z",
     "start_time": "2021-06-02T14:03:43.851967Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:03:44] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:03:44] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from econml.dml import LinearDML\n",
    "from sklearn.base import BaseEstimator, clone\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class RegressionWrapper(BaseEstimator):\n",
    "    \"\"\" Turns a classifier into a 'regressor'.\n",
    "    \n",
    "    We use the regression formulation of double ML, so we need to approximate the classifer\n",
    "    as a regression model. This treats the probabilities as just quantitative value targets\n",
    "    for least squares regression, but it turns out to be a reasonable approximation.\n",
    "    \"\"\"\n",
    "    def __init__(self, clf):\n",
    "        self.clf = clf\n",
    "\n",
    "    def fit(self, X, y, **kwargs):\n",
    "        self.clf_ = clone(self.clf)\n",
    "        self.clf_.fit(X, y, **kwargs)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.clf_.predict_proba(X)[:, 1]\n",
    "\n",
    "# Run Double ML, controlling for all the other features\n",
    "def double_ml(y, causal_feature, control_features):\n",
    "    \"\"\" Use doubleML from econML to estimate the slope of the causal effect of a feature.\n",
    "    \"\"\"\n",
    "    xgb_model = xgboost.XGBClassifier(objective=\"binary:logistic\", random_state=42)\n",
    "    est = LinearDML(model_y=RegressionWrapper(xgb_model))\n",
    "    est.fit(y, causal_feature, W=control_features)\n",
    "    return est.effect_inference()\n",
    "\n",
    "def plot_effect(effect, xs, true_ys, ylim=None):\n",
    "    \"\"\" Plot a double ML effect estimate from econML as a line.\n",
    "    \n",
    "    Note that the effect estimate from double ML is an average effect *slope* not a full\n",
    "    function. So we arbitrarily draw the slope of the line as passing through the origin.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(5, 3))\n",
    "    \n",
    "    pred_xs = [xs.min(), xs.max()]\n",
    "    mid = (xs.min() + xs.max())/2\n",
    "    pred_ys = [effect.pred[0]*(xs.min() - mid), effect.pred[0]*(xs.max() - mid)]\n",
    "    \n",
    "    plt.plot(xs, true_ys - true_ys[0], label='True causal effect', color=\"black\", linewidth=3)\n",
    "    point_pred = effect.point_estimate * pred_xs\n",
    "    pred_stderr = effect.stderr * np.abs(pred_xs)\n",
    "    plt.plot(pred_xs, point_pred - point_pred[0], label='Double ML slope', color=shap.plots.colors.blue_rgb, linewidth=3)\n",
    "    # 99.9% CI\n",
    "    plt.fill_between(pred_xs, point_pred - point_pred[0] - 3.291 * pred_stderr,\n",
    "                     point_pred - point_pred[0] + 3.291 * pred_stderr, alpha=.2, color=shap.plots.colors.blue_rgb)\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"Ad spend\", fontsize=13)\n",
    "    plt.ylabel(\"Zero centered effect\")\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.gca().xaxis.set_ticks_position('bottom')\n",
    "    plt.gca().yaxis.set_ticks_position('left')\n",
    "    plt.gca().spines['right'].set_visible(False)\n",
    "    plt.gca().spines['top'].set_visible(False)\n",
    "    plt.show()\n",
    "\n",
    "# estimate the causal effect of Ad spend controlling for all the other features\n",
    "causal_feature = \"Ad spend\"\n",
    "control_features = [\n",
    "    \"Sales calls\", \"Interactions\", \"Economy\", \"Last upgrade\", \"Discount\",\n",
    "    \"Monthly usage\", \"Bugs reported\"\n",
    "]\n",
    "effect = double_ml(y, X[causal_feature], X.loc[:,control_features])\n",
    "\n",
    "# plot the estimated slope against the true effect\n",
    "xs, true_ys = marginal_effects(generator, 10000, X[[\"Ad spend\"]], logit=False)[0]\n",
    "plot_effect(effect, xs, true_ys, ylim=(-0.2, 0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, double ML (or any other observational causal inference method) only works when you can measure and identify all the possible confounders of the feature for which you want to estimate causal effects. Here we know the causal graph and can see that Monthly Usage and Last Upgrade are the two direct confounders we need to control for. But if we didn't know the causal graph we could still look at the redundancy in the SHAP bar plot and see that Monthly Usage and Last Upgrade are the most redundant features and so are good candidates to control for (as are Discounts and Bugs Reported).\n",
    "\n",
    "### Non-confounding redundancy\n",
    "The second scenario where causal inference can help is non-confounding redundancy. This occurs when the feature we want causal effects for causally drives, or is driven by, another feature included in the model, but that other feature is not a confounder of our feature of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T14:04:36.531520Z",
     "start_time": "2021-06-02T14:04:36.386413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Interactions and sales calls are very redundant with one another.\n",
    "shap.plots.bar(shap_values, clustering=clust, clustering_cutoff=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of this is the Sales Calls feature. Sales Calls directly impact retention, but also have an indirect effect on retention through Interactions. When we include both the Interactions and Sales Calls features in the model the causal effect shared by both features is forced to spread out between them. We can see this in the SHAP scatter plots above, which show how XGBoost underestimates the true causal effect of Sales Calls because most of that effect got put onto the Interactions feature.\n",
    "\n",
    "Non-confounding redundancy can be fixed in principle by removing the redundant variables from the model (see below). For example, if we removed Interactions from the model then we will capture the full effect of making a sales call on renewal probability. This removal is also important for double ML, since double ML will fail to capture indirect causal effects if you control for downstream features caused by the feature of interest. In this case double ML will only measure the \"direct\" effect that does not pass through the other feature. Double ML is however robust to controlling for upstream non-confounding redundancy (where the redundant feature causes the feature of interest), though this will reduce your statistical power to detect true effects.\n",
    "\n",
    "Unfortunately, we often don't know the true causal graph so it can be hard to know when another feature is redundant with our feature of interest because of observed confounding vs. non-confounding redundancy. If it is because of confounding then we should control for that feature using a method like double ML, whereas if it is a downstream consequence then we should drop the feature from our model if we want full causal effects rather than only direct effects. Controlling for a feature we shouldn't tends to hide or split up causal effects, while failing to control for a feature we should have controlled for tends to infer causal effects that do not exist. This generally makes controlling for a feature the safer option when you are uncertain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 399
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit, explain, and plot a univariate model with just Sales calls\n",
    "# Note how this model does not have to split of credit between Sales calls and\n",
    "# Interactions, so we get a better agreement with the true causal effect.\n",
    "sales_calls_model = fit_xgboost(X[[\"Sales calls\"]], y)\n",
    "sales_calls_shap_values = shap.Explainer(sales_calls_model)(X[[\"Sales calls\"]])\n",
    "shap.plots.scatter(sales_calls_shap_values, overlay={\n",
    "    \"True causal effects\": marginal_effects(generator, 10000, [\"Sales calls\"])\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When neither predictive models nor unconfounding methods can answer causal questions\n",
    "\n",
    "Double ML (or any other causal inference method that assumes unconfoundedness) only works when you can measure and identify all the possible confounders of the feature for which you want to estimate causal effects. If you can't measure all the confounders then you are in the hardest possible scenario: unobserved confounding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T14:27:30.673124Z",
     "start_time": "2021-06-02T14:27:30.520558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Discount and Bugs reported seem are fairly independent of the other features we can\n",
    "# measure, but they are not independent of Product need, which is an unobserved confounder.\n",
    "shap.plots.bar(shap_values, clustering=clust, clustering_cutoff=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Discount and Bugs Reported features both suffer from unobserved confounding because not all important variables (e.g., Product Need and Bugs Faced) are measured in the data. Even though both features are relatively independent of all the other features in the model, there are important drivers that are unmeasured. In this case, both predictive models and causal models that require confounders to be observed, like double ML, will fail. This is why double ML estimates a large negative causal effect for the Discount feature even when controlling for all other observed features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-02T14:30:34.851532Z",
     "start_time": "2021-06-02T14:30:33.259104Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:30:33] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:30:33] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# estimate the causal effect of Ad spend controlling for all the other features\n",
    "causal_feature = \"Discount\"\n",
    "control_features = [\n",
    "    \"Sales calls\", \"Interactions\", \"Economy\", \"Last upgrade\",\n",
    "    \"Monthly usage\", \"Ad spend\", \"Bugs reported\"\n",
    "]\n",
    "effect = double_ml(y, X[causal_feature], X.loc[:,control_features])\n",
    "\n",
    "# plot the estimated slope against the true effect\n",
    "xs, true_ys = marginal_effects(generator, 10000, X[[causal_feature]], logit=False)[0]\n",
    "plot_effect(effect, xs, true_ys, ylim=(-0.5, 0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Barring the ability to measure the previously unmeasured features (or features correlated with them), finding causal effects in the presence of unobserved confounding is difficult. In these situations, the only way to identify causal effects that can inform policy is to create or exploit some randomization that breaks the correlation between the features of interest and the unmeasured confounders. Randomized experiments remain the gold standard for finding causal effects in this context.\n",
    "\n",
    "Specialized causal tools based on the principals of instrumental variables, differences-in-differences, or regression discontinuities can sometimes exploit partial randomization even in cases where a full experiment is impossible. For example, instrumental variable techniques can be used to identify causal effects in cases where we cannot randomly assign a treatment, but we can randomly nudge some customers towards treatment, like sending an email encouraging them to explore a new product feature. Difference-in-difference approaches can be helpful when the introduction of new treatments is staggered across groups. Finally, regression discontinuity approaches are a good option when patterns of treatment exhibit sharp cut-offs (for example qualification for treatment based on a specific, measurable trait like revenue over $5,000 per month)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Flexible predictive models like XGBoost or LightGBM are powerful tools for solving prediction problems. However, they are not inherently causal models, so interpreting them with SHAP will fail to accurately answer causal questions in many common situations. Unless features in a model are the result of experimental variation, applying SHAP to predictive models without considering confounding is generally not an appropriate tool to measure causal impacts used to inform policy. SHAP and other interpretability tools can be useful for causal inference, and SHAP is integrated into many causal inference packages, but those use cases are explicitly causal in nature. To that end, using the same data we would collect for prediction problems and using causal inference methods like double ML that are particularly designed to return causal effects is often a good approach for informing policy. In other situations, only an experiment or other source of randomization can really answer what if questions. Causal inference always requires us to make important assumptions. The main point of this article is that the assumptions we make by interpreting a normal predictive model as causal are often unrealistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "Have a comment or question? Checkout github or the Medium version of this article!"
   ]
  }
 ],
 "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": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}