marketing-analytics/campaign-effect-observational.ipynb

{
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  {
   "cell_type": "markdown",
   "source": [
    "# Campaign Effect Estimation in Observational Data Using Causal Inference Methods\n",
    "\n",
    "This notebook demonstrates how to analyze causal effects in marketing campaigns based on observational data. We focus on measuring the overall campaign efficiency (average treatment effect (ATE)) and do not study how the treatment effect varies across the individuals (which can be done using conditional average treatment effect (CATE)).\n",
    "\n",
    "### Use Case\n",
    "We consider a scenario where selected customers were provided (treated) with personalized offers. We do not assume that proper A/B testing was done, so we just study the observational data. Our goal is to measure the *average treatment effect* of the offers on conversions (purchases) taking into account factors such as demographics that influence both offer assignments and customers' purchasing decisions. \n",
    "\n",
    "### Prototype: Approach and Data\n",
    "We use generated data (simulator) to demonstrate the causal inference methods in a clean way. We simulate a simple promotion campaign scenario and then use regression and propensity matching methods to estimate the treatment effect. \n",
    "\n",
    "### Usage and Productization\n",
    "This prototype can be used to evaluate causal effects in a real data sample before building predictive promotion targeting or budget optimization models. This template can also be adapted to a wide range of customer communication and personalization scenarios.\n",
    "\n",
    "It is recommended to start with modifying the data generator to match your use case and data samples. Once the generator is modified, different inference methods can be evaluated and fine-tuned using simulations. Once we are confident that the methods produce valid results for simulated data, we can apply them to the actual data sample."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e97dc74960eac7a3"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Generation\n",
    "\n",
    "We use a basic data generation model with the following variables:\n",
    "* `age` - observed customer demographics variable\n",
    "* `state` - observed customer demographics variable\n",
    "* `is_offer` - indicates customers who were targeted with a special offer  \n",
    "* `is_conversion` - indicates conversion after a promotion campaign\n",
    "    \n",
    "Both targeting decisions (`is_offer`) and conversions (`is_conversion`) are dependent on demographics. In other words, the demographic variables are the back doors. \n",
    "\n",
    "We provide several visualizations that help to understand the correlation between the variables. In particular, these visualization indicate that offers and conversions are positively correlated. This is true even if the `promo_effect` parameter is set to zero. Our goal will be to estimate the real causal effect of the offers on conversions controlling for the back doors."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "221d96f12dcbb699"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "#\n",
    "# Imports and settings\n",
    "#\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tabulate import tabulate\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "from dowhy import CausalModel"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-07T15:03:09.729860Z",
     "start_time": "2024-01-07T15:03:09.407005Z"
    }
   },
   "id": "bc94f5b850c9e1b5"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-------+---------+------------+-----------------+\n",
      "|    |   age | state   | is_offer   | is_conversion   |\n",
      "|----+-------+---------+------------+-----------------|\n",
      "|  0 |    30 | TX      | False      | True            |\n",
      "|  1 |    28 | TX      | False      | False           |\n",
      "|  2 |    31 | TX      | False      | False           |\n",
      "|  3 |    29 | TX      | False      | False           |\n",
      "|  4 |    25 | TX      | False      | False           |\n",
      "+----+-------+---------+------------+-----------------+\n",
      "+--------+-------------+---------+------------+-----------------+\n",
      "|        |         age | state   |   is_offer |   is_conversion |\n",
      "|--------+-------------+---------+------------+-----------------|\n",
      "| count  | 100000      | 100000  |     100000 |          100000 |\n",
      "| unique |    nan      | 3       |          2 |               2 |\n",
      "| top    |    nan      | NJ      |          0 |               0 |\n",
      "| freq   |    nan      | 33461   |      67312 |           79570 |\n",
      "| mean   |     28.0038 | nan     |        nan |             nan |\n",
      "| std    |      2.5325 | nan     |        nan |             nan |\n",
      "| min    |     20      | nan     |        nan |             nan |\n",
      "| 25%    |     26      | nan     |        nan |             nan |\n",
      "| 50%    |     28      | nan     |        nan |             nan |\n",
      "| 75%    |     30      | nan     |        nan |             nan |\n",
      "| max    |     40      | nan     |        nan |             nan |\n",
      "+--------+-------------+---------+------------+-----------------+\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 2 Axes>",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1500x1000 with 4 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def print_df(df):\n",
    "    print(tabulate(df, headers='keys', tablefmt='psql'))\n",
    "\n",
    "def generate_promotion_data(promo_effect = 0.05, state_effect = 0.02, age_effect = 0.2, n = 100000, rnd_seed = 42):\n",
    "    \n",
    "    rng = np.random.default_rng(rnd_seed)\n",
    "    \n",
    "    # Demographic features\n",
    "    data = pd.DataFrame({\n",
    "        'age': 20 + rng.binomial(40, 0.2, n),\n",
    "        'state': rng.choice(['CA', 'NJ', 'TX'], n)\n",
    "    })\n",
    "    data['age_std'] = MinMaxScaler().fit_transform(np.array(data['age']).reshape(-1,1))\n",
    "\n",
    "    # Historical promotion campaigns: the offers were targeted based on demographics\n",
    "    data['is_offer'] = rng.uniform(size = n) < (\n",
    "            0.20 +\n",
    "            0.10 * (data['state'] == 'CA') +\n",
    "            0.03 * (data['state'] == 'TX') +\n",
    "            0.01 * (data['state'] == 'NJ') +\n",
    "            0.20 * (data['age_std'])\n",
    "    )\n",
    "\n",
    "    # Outcome depends both on offer and demographic\n",
    "    data['is_conversion'] = rng.uniform(size = n) < (\n",
    "            0.10 +\n",
    "            promo_effect * data['is_offer'] +\n",
    "            state_effect * (data['state'] == 'CA') +\n",
    "            age_effect   * (data['age_std'])\n",
    "    )\n",
    "    \n",
    "    data.drop(columns=['age_std'], inplace=True)\n",
    "    \n",
    "    return data\n",
    "\n",
    "data = generate_promotion_data()\n",
    "\n",
    "#\n",
    "# Dataset preview and descriptive statistics\n",
    "#\n",
    "print_df(data.head(5))\n",
    "print_df(data.describe(include='all')) \n",
    "\n",
    "\n",
    "#\n",
    "# Visualize the correlation between offers and conversion rates\n",
    "#\n",
    "_, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "sns.histplot(data=data, x=\"is_offer\", hue=\"is_conversion\", palette=\"crest\", \n",
    "             stat=\"probability\", multiple=\"dodge\", shrink=.8, discrete=True, ax=ax[0])\n",
    "crosstab = pd.crosstab(data[\"is_offer\"], data[\"is_conversion\"])\n",
    "offer_conversion_rate = crosstab.iloc[:, 1] / crosstab.iloc[:, 0]\n",
    "sns.barplot(offer_conversion_rate, ax=ax[1])\n",
    "plt.ylabel(\"Conversion rate\")\n",
    "\n",
    "\n",
    "#\n",
    "# Visualize the distributions\n",
    "#\n",
    "_, ax = plt.subplots(2, 2, figsize=(15, 10))\n",
    "for j, y in enumerate(['is_offer', 'is_conversion']):\n",
    "    sns.kdeplot(\n",
    "        data=data, x=\"age\", hue=y, bw_adjust=2,\n",
    "        fill=True, common_norm=False, palette=\"crest\",\n",
    "        alpha=.5, linewidth=0, ax=ax[0][j]\n",
    "    )\n",
    "    sns.histplot(\n",
    "        data=data, x=\"state\", hue=y, shrink=.8, palette=\"crest\", \n",
    "        stat=\"probability\", multiple=\"dodge\", ax=ax[1][j]\n",
    "    )"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-07T15:08:17.436213Z",
     "start_time": "2024-01-07T15:08:15.048574Z"
    }
   },
   "id": "c24c6e0f56169d41"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Solution 1: Promotion Effect Estimation Using Regression \n",
    "\n",
    "In this section, we start with estimating the average treatment effect (ATE) using a manually implemented regression to control for back doors. We then use an off-the-shelf library method to achieve the same."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e10b27522aef34ef"
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:          is_conversion   No. Observations:               100000\n",
      "Model:                          Logit   Df Residuals:                    99995\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Sun, 07 Jan 2024   Pseudo R-squ.:                  0.1183\n",
      "Time:                        07:27:23   Log-Likelihood:                -44918.\n",
      "converged:                       True   LL-Null:                       -50946.\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept         -2.0747      0.031    -66.427      0.000      -2.136      -2.013\n",
      "C(state)[T.NJ]    -0.1353      0.020     -6.629      0.000      -0.175      -0.095\n",
      "C(state)[T.TX]    -0.1121      0.020     -5.539      0.000      -0.152      -0.072\n",
      "is_offer           1.7715      0.017    104.934      0.000       1.738       1.805\n",
      "age                0.0716      0.066      1.084      0.278      -0.058       0.201\n",
      "==================================================================================\n",
      "        Logit Marginal Effects       \n",
      "=====================================\n",
      "Dep. Variable:          is_conversion\n",
      "Method:                          dydx\n",
      "At:                           overall\n",
      "==================================================================================\n",
      "                    dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "C(state)[T.NJ]    -0.0194      0.003     -6.634      0.000      -0.025      -0.014\n",
      "C(state)[T.TX]    -0.0160      0.003     -5.541      0.000      -0.022      -0.010\n",
      "is_offer           0.2534      0.002    125.907      0.000       0.249       0.257\n",
      "age                0.0102      0.009      1.084      0.278      -0.008       0.029\n",
      "==================================================================================\n"
     ]
    }
   ],
   "source": [
    "data = generate_promotion_data(promo_effect=0.3, age_effect=0.0)\n",
    "\n",
    "def get_logit_model(data: pd.DataFrame):\n",
    "    #\n",
    "    # Feature engineering\n",
    "    #\n",
    "    df = data.copy()\n",
    "    df['is_offer'] = 1*df['is_offer']\n",
    "    df['is_conversion'] = 1*df['is_conversion']\n",
    "    df['age'] = MinMaxScaler().fit_transform(np.array(df['age']).reshape(-1,1))\n",
    "\n",
    "    #\n",
    "    # Model fitting \n",
    "    #\n",
    "    model_logit = smf.logit(\"is_conversion ~ is_offer + C(state) + age\", data=df).fit(disp=0) \n",
    "    \n",
    "    return model_logit, df\n",
    "\n",
    "#\n",
    "# Create a model and perform diagnostics. The estimated marginal effect should \n",
    "# match the true effect specified in the data generator.\n",
    "#\n",
    "logit_model, _ = get_logit_model(data)\n",
    "print(logit_model.summary())\n",
    "print(logit_model.get_margeff().summary())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-07T15:27:23.975485Z",
     "start_time": "2024-01-07T15:27:23.246381Z"
    }
   },
   "id": "e7b3d356a95d9d58"
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x1000 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Evaluate the distribution of the effect using bootstrapping\n",
    "#\n",
    "_, ax = plt.subplots(2, 1, figsize=(7, 10))\n",
    "for j, x in enumerate(['is_offer', 'age']):\n",
    "    effect = []\n",
    "    for i in range(100):\n",
    "        df = data.sample(frac=0.2) # bootstrapping\n",
    "        logit_model, _ = get_logit_model(df)\n",
    "        effect.append(logit_model.get_margeff().summary_frame()['dy/dx'][x])\n",
    "\n",
    "    sns.histplot(effect, ax=ax[j])\n",
    "    ax[j].set_xlabel(f\"Effect of {x}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-07T15:27:49.373804Z",
     "start_time": "2024-01-07T15:27:29.883812Z"
    }
   },
   "id": "4ecbffe0a61850c"
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                                 \n",
      "───────────(E[is_conversion|state,age])\n",
      "d[is_offer]                            \n",
      "Estimand assumption 1, Unconfoundedness: If U→{is_offer} and U→is_conversion then P(is_conversion|is_offer,state,age,U) = P(is_conversion|is_offer,state,age)\n",
      "\n",
      "## Realized estimand\n",
      "b: is_conversion~is_offer+state+age\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.30579888971335434\n",
      "p-value: [0.]\n",
      "95.0% confidence interval: [[0.30077753 0.31082025]]\n",
      "\n",
      "Causal Estimate is 0.306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Estimate the is_offer->is_conversion effect using a library methods\n",
    "#\n",
    "graph = \"\"\"digraph {\n",
    "    is_offer->is_conversion; \n",
    "    age->is_conversion; \n",
    "    age->is_offer; \n",
    "    state->is_conversion; \n",
    "    state->is_offer;\n",
    "}\"\"\"\n",
    "df = data[['age', 'state', 'is_conversion', 'is_offer']].copy()\n",
    "model = CausalModel(\n",
    "    data=df,\n",
    "    treatment='is_offer',\n",
    "    outcome='is_conversion',\n",
    "    graph=graph)\n",
    "\n",
    "# Identify causal effect and return target estimands\n",
    "identified_estimand = model.identify_effect()\n",
    "\n",
    "# Estimate the target estimand using a statistical method.\n",
    "causal_estimate_reg = model.estimate_effect(identified_estimand,\n",
    "        method_name=\"backdoor.linear_regression\",\n",
    "        confidence_intervals=True,\n",
    "        test_significance=True)\n",
    "print(causal_estimate_reg)\n",
    "print(f\"Causal Estimate is {causal_estimate_reg.value:.3}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-07T15:36:51.121253Z",
     "start_time": "2024-01-07T15:36:50.904668Z"
    }
   },
   "id": "dfd0a63c977de68f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Solution 2: Promotion Effect Estimation Using Propensity Scoring\n",
    "\n",
    "In this section, we  estimate the average treatment effect using propensity score matching/weighting. We use the model fitted in the previous section."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "23a021437115257e"
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                                 \n",
      "───────────(E[is_conversion|state,age])\n",
      "d[is_offer]                            \n",
      "Estimand assumption 1, Unconfoundedness: If U→{is_offer} and U→is_conversion then P(is_conversion|is_offer,state,age,U) = P(is_conversion|is_offer,state,age)\n",
      "\n",
      "## Realized estimand\n",
      "b: is_conversion~is_offer+state+age\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 0.30574958545302766\n",
      "95.0% confidence interval: (0.2996564731485418, 0.31120974532863077)\n"
     ]
    }
   ],
   "source": [
    "estimate = model.estimate_effect(identified_estimand,\n",
    "                                 method_name=\"backdoor.propensity_score_weighting\",\n",
    "                                 confidence_intervals=True,\n",
    "                                 target_units=\"ate\")\n",
    "\n",
    "print(estimate)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-07T15:32:09.841639Z",
     "start_time": "2024-01-07T15:31:23.320812Z"
    }
   },
   "id": "96c81902a75a5893"
  }
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