{ "cells": [ { "cell_type": "markdown", "id": "domestic-remove", "metadata": {}, "source": [ "(counterfactuals_do_operator)=\n", "# Counterfactual generation using pymc do-operator\n", "\n", ":::{post} August, 2023\n", ":tags: causality, causal inference, do-operator, counterfactuals\n", ":category: beginner, reference\n", ":author: Shekhar Khandelwal\n", ":::" ] }, { "cell_type": "code", "execution_count": 1, "id": "elect-softball", "metadata": { "tags": [] }, "outputs": [], "source": [ "import warnings\n", "\n", "import arviz.preview as az\n", "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "level-balance", "metadata": { "tags": [] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina' # high resolution figures\n", "az.style.use(\"arviz-variat\")\n", "rng = np.random.default_rng(42)\n", "SEED = 8927" ] }, { "cell_type": "markdown", "id": "sapphire-yellow", "metadata": {}, "source": [ "# Introduction\n", "\n", "In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of counterfactuals in causal analysis using the PyMC framework, with a special focus on the “do-operator.”\n", "Counterfactuals are essentially “what-if” scenarios that allow us to understand the potential outcomes had a different action been taken or a different condition been present. By leveraging the PyMC framework and its “do-operator,” we can programmatically simulate these scenarios, giving us a deeper understanding of the relationships between predictors and target variables.\n", "\n", "Through a step-by-step guide, we will delve into the process of building a PyMC model skeleton, generating data using the do-operator, and validating the relationships captured by the model. Furthermore, we will explore the magic of the do-operator in simulating different ‘what-if’ scenarios, akin to programmatic A/B testing.\n", "\n", "- Step 1. Build a pymc model skeleton\n", "- Step 2. Use model skeleton and generate data using do-operator to infuse relationship between predictors and target variable (ssshhh, that’s a hidden superhero feature of do-operator ;) )\n", "- Step 3. Use observe-operator to assign generated data on the model skeleton\n", "- Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples (isn’t that what we expect a predictive model to do ;) )\n", "- Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios (basically mimic A/B testing…programatically)\n" ] }, { "cell_type": "markdown", "id": "d3756a0d-447e-4ffd-8305-e0f2329dbc3a", "metadata": {}, "source": [ "### Step 1. Build a pymc model skeleton\n", "\n", "For this demo, we are building a very simple Linear Regression model.\n", "- Predictor — ‘a’, ‘b’, ‘c’\n", "- Target Variable — ‘y’\n", "- Coefficients —\n", ">- ‘beta_ay’ -> coefficient of |a|\n", ">- ‘beta_by’ -> coefficient of |b|\n", ">- ‘beta_cy’ -> coefficient of |c|" ] }, { "cell_type": "code", "execution_count": 3, "id": "21e66b38", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "clusteri (1)\n", "\n", "i (1)\n", "\n", "\n", "\n", "beta_ay\n", "\n", "beta_ay\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu\n", "\n", "y_mu\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "beta_ay->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_cy\n", "\n", "beta_cy\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_cy->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_by\n", "\n", "beta_by\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_by->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_y0\n", "\n", "beta_y0\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_y0->y_mu\n", "\n", "\n", "\n", "\n", "\n", "sigma_y\n", "\n", "sigma_y\n", "~\n", "Halfnormal\n", "\n", "\n", "\n", "y\n", "\n", "y\n", "~\n", "Normal\n", "\n", "\n", "\n", "sigma_y->y\n", "\n", "\n", "\n", "\n", "\n", "b\n", "\n", "b\n", "~\n", "Normal\n", "\n", "\n", "\n", "b->y_mu\n", "\n", "\n", "\n", "\n", "\n", "y_mu->y\n", "\n", "\n", "\n", "\n", "\n", "c\n", "\n", "c\n", "~\n", "Normal\n", "\n", "\n", "\n", "c->y_mu\n", "\n", "\n", "\n", "\n", "\n", "a\n", "\n", "a\n", "~\n", "Normal\n", "\n", "\n", "\n", "a->y_mu\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with pm.Model(coords={\"i\": [0]}) as model_generative:\n", " # priors\n", " beta_y0 = pm.Normal(\"beta_y0\")\n", " beta_ay = pm.Normal(\"beta_ay\")\n", " beta_by = pm.Normal(\"beta_by\")\n", " beta_cy = pm.Normal(\"beta_cy\")\n", " # observation noise on Y\n", " sigma_y = pm.HalfNormal(\"sigma_y\")\n", " # core nodes and causal relationships\n", " a = pm.Normal(\"a\", mu=0, sigma=1, dims=\"i\")\n", " b = pm.Normal(\"b\", mu=0, sigma=1, dims=\"i\")\n", " c = pm.Normal(\"c\", mu=0, sigma=1, dims=\"i\")\n", " y_mu = pm.Deterministic(\n", " \"y_mu\", beta_y0 + (beta_ay * a) + (beta_by * b) + (beta_cy * c), dims=\"i\"\n", " )\n", " y = pm.Normal(\"y\", mu=y_mu, sigma=sigma_y, dims=\"i\")\n", "\n", "\n", "pm.model_to_graphviz(model_generative)" ] }, { "cell_type": "markdown", "id": "e2320755-54f5-4051-b73e-feb60de8b783", "metadata": {}, "source": [ "### Step 2. Use model skeleton and generate data using do-operator to infuse relationship between predictors and target variable. We will use this generated data for modelling later.\n", "\n", "Let’s first define the predictors relationship with target variable." ] }, { "cell_type": "code", "execution_count": 4, "id": "62d01fc3-9a12-4dcd-b2f1-d7a09116a3c6", "metadata": {}, "outputs": [], "source": [ "true_values = {\"beta_ay\": 1.5, \"beta_by\": 0.7, \"beta_cy\": 0.3, \"sigma_y\": 0.2, \"beta_y0\": 0.0}" ] }, { "cell_type": "markdown", "id": "4bad6441-6921-446b-82a1-6a995e74faff", "metadata": {}, "source": [ "Basically what we are saying here is, we are intentionally defining the coefficient values, which we expect predictive model to predict later on.\n", "\n", "Now the magic begins. We will use do-operator to use this dictionary and sample data variables. How do we do this ? Simple by passing two arguments to pymc do-operator. First, the model skeleton object. And second, the coefficient dictionary." ] }, { "cell_type": "code", "execution_count": 5, "id": "fbf04aa4-e68f-43fd-ba70-91a287b6b12d", "metadata": {}, "outputs": [], "source": [ "model_simulate = pm.do(model_generative, true_values)" ] }, { "cell_type": "markdown", "id": "a149f565-ccb3-4118-ac5a-da67733e3e5a", "metadata": {}, "source": [ "This will create a new model object with the coefficent variables values infused. " ] }, { "cell_type": "code", "execution_count": 6, "id": "113da0d7-b9d7-4cd2-98fa-7fa794169b94", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "clusteri (1)\n", "\n", "i (1)\n", "\n", "\n", "\n", "b\n", "\n", "b\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu\n", "\n", "y_mu\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "b->y_mu\n", "\n", "\n", "\n", "\n", "\n", "y\n", "\n", "y\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu->y\n", "\n", "\n", "\n", "\n", "\n", "c\n", "\n", "c\n", "~\n", "Normal\n", "\n", "\n", "\n", "c->y_mu\n", "\n", "\n", "\n", "\n", "\n", "a\n", "\n", "a\n", "~\n", "Normal\n", "\n", "\n", "\n", "a->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_ay\n", "\n", "beta_ay\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_ay->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_cy\n", "\n", "beta_cy\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_cy->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_by\n", "\n", "beta_by\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_by->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_y0\n", "\n", "beta_y0\n", "~\n", "Data\n", "\n", "\n", "\n", "beta_y0->y_mu\n", "\n", "\n", "\n", "\n", "\n", "sigma_y\n", "\n", "sigma_y\n", "~\n", "Data\n", "\n", "\n", "\n", "sigma_y->y\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_simulate.to_graphviz()" ] }, { "cell_type": "markdown", "id": "af0e13da-29e6-44a6-852e-c3e965d7c462", "metadata": {}, "source": [ "The gray shades on the coefficient variables depicts the tale. Check the previous model graph, it was all white.\n", "\n", "Now, all we have to do is generate samples, the known pymc way.\n", "\n", "Lets generate 100 samples." ] }, { "cell_type": "code", "execution_count": 7, "id": "2c651c0a-29f8-4669-baf7-986687f59317", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sampling: [a, b, c, y]\n" ] } ], "source": [ "N = 100\n", "\n", "with model_simulate:\n", " simulate = pm.sample_prior_predictive(samples=N)" ] }, { "cell_type": "markdown", "id": "9ecd7313-e68c-4439-803c-576a5c474e4b", "metadata": {}, "source": [ "We know that this generates an Arviz object, and since we have called sample_prior_predictive, hence the object will only contain priors." ] }, { "cell_type": "code", "execution_count": 8, "id": "99a3fede-e773-4823-bb5e-ece89805bcc6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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arviz.InferenceData
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      <xarray.Dataset> Size: 5kB\n",
       "Dimensions:  (chain: 1, draw: 100, i: 1)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 8B 0\n",
       "  * draw     (draw) int64 800B 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
       "  * i        (i) int64 8B 0\n",
       "Data variables:\n",
       "    b        (chain, draw, i) float64 800B 0.9658 0.04536 ... -0.08396 0.5739\n",
       "    y        (chain, draw, i) float64 800B 0.6151 -1.398 ... -0.2965 0.7868\n",
       "    y_mu     (chain, draw, i) float64 800B 0.4538 -1.286 1.129 ... -0.4846 0.924\n",
       "    c        (chain, draw, i) float64 800B -1.584 -1.12 ... -1.041 -0.3577\n",
       "    a        (chain, draw, i) float64 800B 0.1686 -0.6548 ... -0.07567 0.4198\n",
       "Attributes:\n",
       "    created_at:                 2026-02-12T08:05:25.902369+00:00\n",
       "    arviz_version:              0.23.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.27.1

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      <xarray.Dataset> Size: 40B\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    beta_cy  float64 8B 0.3\n",
       "    beta_by  float64 8B 0.7\n",
       "    beta_ay  float64 8B 1.5\n",
       "    beta_y0  float64 8B 0.0\n",
       "    sigma_y  float64 8B 0.2\n",
       "Attributes:\n",
       "    created_at:                 2026-02-12T08:05:25.907365+00:00\n",
       "    arviz_version:              0.23.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.27.1

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\n", " " ], "text/plain": [ "Inference data with groups:\n", "\t> prior\n", "\t> constant_data" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simulate" ] }, { "cell_type": "markdown", "id": "9cc7caf3-61d7-47ac-a207-fa22778a9f2a", "metadata": {}, "source": [ "Extract the sampled prior data into a pandas dataframe." ] }, { "cell_type": "code", "execution_count": 9, "id": "86e38344-28ad-4e0d-a987-4385ed320571", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100, 4)\n" ] }, { "data": { "text/html": [ "
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abcy
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" ], "text/plain": [ " a b c y\n", "0 0.168609 0.965789 -1.583881 0.615137\n", "1 -0.654816 0.045357 -1.119634 -1.397617\n", "2 0.330262 0.955123 -0.115252 0.939636\n", "3 -0.919746 -0.629055 1.350298 -1.482930\n", "4 -0.527499 0.046205 -0.387889 -1.003153" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observed = {\n", " \"a\": simulate.prior[\"a\"].values.flatten(),\n", " \"b\": simulate.prior[\"b\"].values.flatten(),\n", " \"c\": simulate.prior[\"c\"].values.flatten(),\n", " \"y\": simulate.prior[\"y\"].values.flatten(),\n", "}\n", "\n", "df = pd.DataFrame(observed)\n", "print(df.shape)\n", "df.head(5)" ] }, { "cell_type": "markdown", "id": "410b2941-ee10-444b-ab5b-36f229a6dba7", "metadata": {}, "source": [ "Ok, so now we are all set with a sample data." ] }, { "cell_type": "markdown", "id": "ae564cd5-23e4-4225-9ee8-e642ae47eeb7", "metadata": {}, "source": [ "### Step 3. Use observe-operator to assign generated data on the model skeleton\n", "\n", "Now, this is another cool feature of pymc newly introduced observe method. Observe method, takes in a model skeleton and the dictionary with the data for the variables we want to infuse into the model." ] }, { "cell_type": "code", "execution_count": 10, "id": "17860fac-d25b-46bf-b4d7-8a920610a853", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "clusteri (100)\n", "\n", "i (100)\n", "\n", "\n", "\n", "b\n", "\n", "b\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu\n", "\n", "y_mu\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "b->y_mu\n", "\n", "\n", "\n", "\n", "\n", "y\n", "\n", "y\n", "~\n", "Normal\n", "\n", "\n", "\n", "y_mu->y\n", "\n", "\n", "\n", "\n", "\n", "c\n", "\n", "c\n", "~\n", "Normal\n", "\n", "\n", "\n", "c->y_mu\n", "\n", "\n", "\n", "\n", "\n", "a\n", "\n", "a\n", "~\n", "Normal\n", "\n", "\n", "\n", "a->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_ay\n", "\n", "beta_ay\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_ay->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_cy\n", "\n", "beta_cy\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_cy->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_by\n", "\n", "beta_by\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_by->y_mu\n", "\n", "\n", "\n", "\n", "\n", "beta_y0\n", "\n", "beta_y0\n", "~\n", "Normal\n", "\n", "\n", "\n", "beta_y0->y_mu\n", "\n", "\n", "\n", "\n", "\n", "sigma_y\n", "\n", "sigma_y\n", "~\n", "Halfnormal\n", "\n", "\n", "\n", "sigma_y->y\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_dict = {\"a\": df[\"a\"], \"b\": df[\"b\"], \"c\": df[\"c\"], \"y\": df[\"y\"]}\n", "model_inference = pm.observe(model_generative, data_dict)\n", "model_inference.set_dim(\"i\", N, coord_values=np.arange(N))\n", "pm.model_to_graphviz(model_inference)" ] }, { "cell_type": "markdown", "id": "5ad86c3a-ca67-406c-b114-1f5449b354a1", "metadata": {}, "source": [ "See the gray matter again. This time we have observed data infused into the model, and we have to sample for the coefficient and other parameters.\n", "\n", "So, lets sample.\n", "\n", "### Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples" ] }, { "cell_type": "code", "execution_count": 11, "id": "6ef56be2-06a9-49b8-8044-e789f1d254e9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "NUTS: [beta_cy, beta_by, beta_ay, beta_y0, sigma_y]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "80ae42f1e35b4b2eb649124525109d25", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model_inference:\n",
    "    idata = pm.sample(random_seed=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375287a3-068a-4815-b86b-f472676a5416",
   "metadata": {},
   "source": [
    "Now, lets validate if model captured the infused coefficient values in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a37b112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "
"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 860,
       "width": 2903
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = az.plot_dist(\n",
    "    idata,\n",
    "    var_names=list(true_values.keys()),\n",
    ")\n",
    "az.add_lines(pc, true_values);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89e6a59-db38-499e-a6ac-73a53a4fca19",
   "metadata": {},
   "source": [
    "BAM ! Pretty nice fit !\n",
    "\n",
    "Now, lets do what we are supposed to do ! Counterfactuals.\n",
    "\n",
    "Basically, this is about generating target variable values with different predictor values. Basically, answering what if questions !\n",
    "\n",
    "_What-if there was all ‘b’ values as 0 ?_\n",
    "\n",
    "_What-if all ‘b’ values were double ?_\n",
    "\n",
    "How to do this ? Here you go..\n",
    "\n",
    "### Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios.\n",
    "Since, we want to experiment with ‘b’, lets first assign observed values to ‘a’ and ‘c’. Not to ‘y’, because that’s what we want to sample. Correct !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0643453-0bb6-4ceb-9fb1-3adf57f031b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_counterfactual = pm.do(model_inference, {\"a\": df[\"a\"], \"c\": df[\"c\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8067a3-6430-4cf9-b53e-a5bfbd8e4488",
   "metadata": {},
   "source": [
    "Now, lets begin the fun part. Let’s generate counterfactuals.\n",
    "\n",
    "### _Scenario 1 :- What if all values for ‘b’ were 0 ?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7cbeba3-d047-447f-bfe2-077604fd2fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_b0 = pm.do(model_counterfactual, {\"b\": np.zeros(N, dtype=\"int32\")}, prune_vars=True)\n",
    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b23275-029d-40a2-8603-796c740bed29",
   "metadata": {},
   "source": [
    "Just sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edd9b175-5a4b-457a-b9d9-560251733600",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: []\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00f97e61c0844beea9faabcf53bc8a4c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: []\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da4d917410444298ac97c020bc6d0f28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample when 'b' was 0: P(y | (a,c), do(b=0))\n",
    "idata_b0 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b0,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")\n",
    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
    "idata_b1 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b1,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cabe3d1-ecab-4bb8-bcea-d45e91ca0b04",
   "metadata": {},
   "source": [
    "Some basic python and here we have the counterfactuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d853c28-4029-4372-a46d-132239918fe5",
   "metadata": {},
   "outputs": [
    {
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abcyb_scenario_1y_scenario_1
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      ],
      "text/plain": [
       "          a         b         c         y  b_scenario_1  y_scenario_1\n",
       "0  0.168609  0.965789 -1.583881  0.615137             0     -0.257788\n",
       "1 -0.654816  0.045357 -1.119634 -1.397617             0     -1.374868\n",
       "2  0.330262  0.955123 -0.115252  0.939636             0      0.473174\n",
       "3 -0.919746 -0.629055  1.350298 -1.482930             0     -0.973051\n",
       "4 -0.527499  0.046205 -0.387889 -1.003153             0     -0.938561"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"b_scenario_1\"] = 0\n",
    "df[\"y_scenario_1\"] = (\n",
    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
    ")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad2d911-2948-4553-9322-121f064696e0",
   "metadata": {},
   "source": [
    "### _Scenario 2: What if ‘b’ was 5 times as observed_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c22e18f-cb96-4ca9-93f4-e8323b1528b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_b0 = pm.do(model_counterfactual, {\"b\": 5 * df[\"b\"]}, prune_vars=True)\n",
    "model_b1 = pm.do(model_counterfactual, {\"b\": df[\"b\"]}, prune_vars=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5422848d-e6cd-4f3e-8641-640ed227ea78",
   "metadata": {},
   "source": [
    "Sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97990259-7cd9-4801-8af0-b8e3a46ffa7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: []\n"
     ]
    },
    {
     "data": {
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       "model_id": "6db6f2d022124836a956bf459978ba98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "Sampling: []\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "277438af40b340ed956569886307eaa2",
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       "Output()"
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     "output_type": "display_data"
    },
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abcyb_scenario_1y_scenario_1b_scenario_2y_scenario_2
00.1686090.965789-1.5838810.6151370-0.2577884.8289433.090282
1-0.6548160.045357-1.119634-1.3976170-1.3748680.226784-1.217631
20.3302620.955123-0.1152520.93963600.4731744.7756133.784268
3-0.919746-0.6290551.350298-1.4829300-0.973051-3.145274-3.153777
4-0.5274990.046205-0.387889-1.0031530-0.9385610.231026-0.778383
\n",
       "
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      ],
      "text/plain": [
       "          a         b         c         y  b_scenario_1  y_scenario_1  \\\n",
       "0  0.168609  0.965789 -1.583881  0.615137             0     -0.257788   \n",
       "1 -0.654816  0.045357 -1.119634 -1.397617             0     -1.374868   \n",
       "2  0.330262  0.955123 -0.115252  0.939636             0      0.473174   \n",
       "3 -0.919746 -0.629055  1.350298 -1.482930             0     -0.973051   \n",
       "4 -0.527499  0.046205 -0.387889 -1.003153             0     -0.938561   \n",
       "\n",
       "   b_scenario_2  y_scenario_2  \n",
       "0      4.828943      3.090282  \n",
       "1      0.226784     -1.217631  \n",
       "2      4.775613      3.784268  \n",
       "3     -3.145274     -3.153777  \n",
       "4      0.231026     -0.778383  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample when 'b' was 5 times b: P(y | (a,c), do(b=5*b))\n",
    "idata_b0 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b0,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")\n",
    "# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))\n",
    "idata_b1 = pm.sample_posterior_predictive(\n",
    "    idata,\n",
    "    model=model_b1,\n",
    "    predictions=True,\n",
    "    var_names=[\"y_mu\"],\n",
    "    random_seed=SEED,\n",
    ")\n",
    "\n",
    "df[\"b_scenario_2\"] = 5 * df[\"b\"]\n",
    "df[\"y_scenario_2\"] = (\n",
    "    idata_b0.predictions.y_mu.mean((\"chain\", \"draw\")).values.reshape(1, -1).flatten()\n",
    ")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2005665d-300b-4a01-91e4-a0901dbbaa97",
   "metadata": {},
   "source": [
    "Ok, so now you got the idea. It's an open playground. Go back in time, change whatever you want to change, and see how output changes.\n",
    "\n",
    "This opens the door for many more possibilities in various use cases. Especially, Causal Analytics !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b743d58b-2678-4e17-9947-a8fe4ed03e21",
   "metadata": {},
   "source": [
    "## Authors\n",
    "- Authored by [Shekhar Khandelwal](https://github.com/shekharkhandelwal1983) in August 2023\n",
    "- Updated by Osvaldo Martin in February 2026 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closed-frank",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "https://medium.com/@khandelwal-shekhar/counterfactuals-for-causal-analysis-via-pymc-do-operator-234ba04e4e80\n",
    "\n",
    "https://www.pymc-labs.io/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0717070c-04aa-4836-ab95-6b3eff0dcaaf",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "sound-calculation",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Thu, 12 Feb 2026\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.13.11\n",
      "IPython version      : 8.29.0\n",
      "\n",
      "pytensor: 2.37.0\n",
      "\n",
      "arviz : 0.23.1\n",
      "numpy : 2.2.6\n",
      "pandas: 2.2.3\n",
      "pymc  : 5.27.1\n",
      "\n",
      "Watermark: 2.6.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4386fc-4de9-4535-a160-d929315633ef",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
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