diff --git a/examples/causal_inference/counterfactuals_do_operator.ipynb b/examples/causal_inference/counterfactuals_do_operator.ipynb new file mode 100644 index 000000000..94a1645a7 --- /dev/null +++ b/examples/causal_inference/counterfactuals_do_operator.ipynb @@ -0,0 +1,2931 @@ +{ + "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", + "
\n", + "
\n", + "
arviz.InferenceData
\n", + "
\n", + "
    \n", + " \n", + "
  • \n", + " \n", + " \n", + "
    \n", + "
    \n", + "
      \n", + "
      \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
      <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

      \n", + "
    \n", + "
    \n", + "
    • \n", + " \n", + "
  • \n", + " \n", + " \n", + "
    \n", + "
    \n", + "
      \n", + "
      \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
      <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

      \n", + "
    \n", + "
    \n", + "
    • \n", + " \n", + "
\n", + "
\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": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
abcy
00.1686090.965789-1.5838810.615137
1-0.6548160.045357-1.119634-1.397617
20.3302620.955123-0.1152520.939636
3-0.919746-0.6290551.350298-1.482930
4-0.5274990.046205-0.387889-1.003153
\n", + "
" + ], + "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": [ + "
\n"
+      ],
+      "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": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
abcyb_scenario_1y_scenario_1
00.1686090.965789-1.5838810.6151370-0.257788
1-0.6548160.045357-1.119634-1.3976170-1.374868
20.3302620.955123-0.1152520.93963600.473174
3-0.919746-0.6290551.350298-1.4829300-0.973051
4-0.5274990.046205-0.387889-1.0031530-0.938561
\n",
+       "
"
+      ],
+      "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": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6db6f2d022124836a956bf459978ba98",
+       "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": "277438af40b340ed956569886307eaa2",
+       "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"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
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",
+       "
"
+      ],
+      "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",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eabm",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/causal_inference/counterfactuals_do_operator.myst.md b/examples/causal_inference/counterfactuals_do_operator.myst.md
new file mode 100644
index 000000000..41f03d1e6
--- /dev/null
+++ b/examples/causal_inference/counterfactuals_do_operator.myst.md
@@ -0,0 +1,300 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: eabm
+  language: python
+  name: python3
+---
+
+(counterfactuals_do_operator)=
+# Counterfactual generation using pymc do-operator
+
+:::{post} August, 2023
+:tags: causality, causal inference, do-operator, counterfactuals
+:category: beginner, reference
+:author: Shekhar Khandelwal
+:::
+
+```{code-cell} ipython3
+import warnings
+
+import arviz.preview as az
+import numpy as np
+import pandas as pd
+import pymc as pm
+
+warnings.filterwarnings("ignore")
+```
+
+```{code-cell} ipython3
+%config InlineBackend.figure_format = 'retina'  # high resolution figures
+az.style.use("arviz-variat")
+rng = np.random.default_rng(42)
+SEED = 8927
+```
+
+# Introduction
+
+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.”
+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.
+
+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.
+
+- Step 1. Build a pymc model skeleton
+- 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 ;) )
+- Step 3. Use observe-operator to assign generated data on the model skeleton
+- 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 ;) )
+- Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios (basically mimic A/B testing…programatically)
+
++++
+
+### Step 1. Build a pymc model skeleton
+
+For this demo, we are building a very simple Linear Regression model.
+- Predictor — ‘a’, ‘b’, ‘c’
+- Target Variable — ‘y’
+- Coefficients —
+>- ‘beta_ay’ -> coefficient of |a|
+>- ‘beta_by’ -> coefficient of |b|
+>- ‘beta_cy’ -> coefficient of |c|
+
+```{code-cell} ipython3
+with pm.Model(coords={"i": [0]}) as model_generative:
+    # priors
+    beta_y0 = pm.Normal("beta_y0")
+    beta_ay = pm.Normal("beta_ay")
+    beta_by = pm.Normal("beta_by")
+    beta_cy = pm.Normal("beta_cy")
+    # observation noise on Y
+    sigma_y = pm.HalfNormal("sigma_y")
+    # core nodes and causal relationships
+    a = pm.Normal("a", mu=0, sigma=1, dims="i")
+    b = pm.Normal("b", mu=0, sigma=1, dims="i")
+    c = pm.Normal("c", mu=0, sigma=1, dims="i")
+    y_mu = pm.Deterministic(
+        "y_mu", beta_y0 + (beta_ay * a) + (beta_by * b) + (beta_cy * c), dims="i"
+    )
+    y = pm.Normal("y", mu=y_mu, sigma=sigma_y, dims="i")
+
+
+pm.model_to_graphviz(model_generative)
+```
+
+### 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.
+
+Let’s first define the predictors relationship with target variable.
+
+```{code-cell} ipython3
+true_values = {"beta_ay": 1.5, "beta_by": 0.7, "beta_cy": 0.3, "sigma_y": 0.2, "beta_y0": 0.0}
+```
+
+Basically what we are saying here is, we are intentionally defining the coefficient values, which we expect predictive model to predict later on.
+
+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.
+
+```{code-cell} ipython3
+model_simulate = pm.do(model_generative, true_values)
+```
+
+This will create a new model object with the coefficent variables values infused. 
+
+```{code-cell} ipython3
+model_simulate.to_graphviz()
+```
+
+The gray shades on the coefficient variables depicts the tale. Check the previous model graph, it was all white.
+
+Now, all we have to do is generate samples, the known pymc way.
+
+Lets generate 100 samples.
+
+```{code-cell} ipython3
+N = 100
+
+with model_simulate:
+    simulate = pm.sample_prior_predictive(samples=N)
+```
+
+We know that this generates an Arviz object, and since we have called sample_prior_predictive, hence the object will only contain priors.
+
+```{code-cell} ipython3
+simulate
+```
+
+Extract the sampled prior data into a pandas dataframe.
+
+```{code-cell} ipython3
+observed = {
+    "a": simulate.prior["a"].values.flatten(),
+    "b": simulate.prior["b"].values.flatten(),
+    "c": simulate.prior["c"].values.flatten(),
+    "y": simulate.prior["y"].values.flatten(),
+}
+
+df = pd.DataFrame(observed)
+print(df.shape)
+df.head(5)
+```
+
+Ok, so now we are all set with a sample data.
+
++++
+
+### Step 3. Use observe-operator to assign generated data on the model skeleton
+
+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.
+
+```{code-cell} ipython3
+data_dict = {"a": df["a"], "b": df["b"], "c": df["c"], "y": df["y"]}
+model_inference = pm.observe(model_generative, data_dict)
+model_inference.set_dim("i", N, coord_values=np.arange(N))
+pm.model_to_graphviz(model_inference)
+```
+
+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.
+
+So, lets sample.
+
+### Step 4. Create samples and validate that the infused relationship between predictors and target variable are captured by the model samples
+
+```{code-cell} ipython3
+with model_inference:
+    idata = pm.sample(random_seed=SEED)
+```
+
+Now, lets validate if model captured the infused coefficient values in the data.
+
+```{code-cell} ipython3
+pc = az.plot_dist(
+    idata,
+    var_names=list(true_values.keys()),
+)
+az.add_lines(pc, true_values);
+```
+
+BAM ! Pretty nice fit !
+
+Now, lets do what we are supposed to do ! Counterfactuals.
+
+Basically, this is about generating target variable values with different predictor values. Basically, answering what if questions !
+
+_What-if there was all ‘b’ values as 0 ?_
+
+_What-if all ‘b’ values were double ?_
+
+How to do this ? Here you go..
+
+### Step 5. Use do-operator to time travel, and generate target variable with different ‘what-if’ scenarios.
+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 !
+
+```{code-cell} ipython3
+model_counterfactual = pm.do(model_inference, {"a": df["a"], "c": df["c"]})
+```
+
+Now, lets begin the fun part. Let’s generate counterfactuals.
+
+### _Scenario 1 :- What if all values for ‘b’ were 0 ?_
+
+```{code-cell} ipython3
+model_b0 = pm.do(model_counterfactual, {"b": np.zeros(N, dtype="int32")}, prune_vars=True)
+model_b1 = pm.do(model_counterfactual, {"b": df["b"]}, prune_vars=True)
+```
+
+Just sample.
+
+```{code-cell} ipython3
+# Sample when 'b' was 0: P(y | (a,c), do(b=0))
+idata_b0 = pm.sample_posterior_predictive(
+    idata,
+    model=model_b0,
+    predictions=True,
+    var_names=["y_mu"],
+    random_seed=SEED,
+)
+# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))
+idata_b1 = pm.sample_posterior_predictive(
+    idata,
+    model=model_b1,
+    predictions=True,
+    var_names=["y_mu"],
+    random_seed=SEED,
+)
+```
+
+Some basic python and here we have the counterfactuals.
+
+```{code-cell} ipython3
+df["b_scenario_1"] = 0
+df["y_scenario_1"] = (
+    idata_b0.predictions.y_mu.mean(("chain", "draw")).values.reshape(1, -1).flatten()
+)
+df.head(5)
+```
+
+### _Scenario 2: What if ‘b’ was 5 times as observed_
+
+```{code-cell} ipython3
+model_b0 = pm.do(model_counterfactual, {"b": 5 * df["b"]}, prune_vars=True)
+model_b1 = pm.do(model_counterfactual, {"b": df["b"]}, prune_vars=True)
+```
+
+Sample.
+
+```{code-cell} ipython3
+# Sample when 'b' was 5 times b: P(y | (a,c), do(b=5*b))
+idata_b0 = pm.sample_posterior_predictive(
+    idata,
+    model=model_b0,
+    predictions=True,
+    var_names=["y_mu"],
+    random_seed=SEED,
+)
+# Sample when 'b' was as observed: P(y | (a,c), do(b=observed))
+idata_b1 = pm.sample_posterior_predictive(
+    idata,
+    model=model_b1,
+    predictions=True,
+    var_names=["y_mu"],
+    random_seed=SEED,
+)
+
+df["b_scenario_2"] = 5 * df["b"]
+df["y_scenario_2"] = (
+    idata_b0.predictions.y_mu.mean(("chain", "draw")).values.reshape(1, -1).flatten()
+)
+df.head(5)
+```
+
+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.
+
+This opens the door for many more possibilities in various use cases. Especially, Causal Analytics !
+
++++
+
+## Authors
+- Authored by [Shekhar Khandelwal](https://github.com/shekharkhandelwal1983) in August 2023
+- Updated by Osvaldo Martin in February 2026 
+
++++
+
+## References
+
+https://medium.com/@khandelwal-shekhar/counterfactuals-for-causal-analysis-via-pymc-do-operator-234ba04e4e80
+
+https://www.pymc-labs.io/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/
+
++++
+
+## Watermark
+
+```{code-cell} ipython3
+%load_ext watermark
+%watermark -n -u -v -iv -w -p pytensor
+```
+
+:::{include} ../page_footer.md
+:::