Causal Machine Learning & Counterfactual Inference

Standard machine learning and static causal models fail in multi-period sequential systems where time-varying telemetry acts simultaneously as a mediator and a confounder.

Naive feature flattening or standard conditioning triggers mediator-blocking bias and collider stratification bias—severely distorting downstream policy evaluation.

This guide explores the mathematics of sequential exchangeability, time-varying inverse propensity weighting (IPTW), and Marginal Structural Models (MSMs) with a production-ready Python implementation.

Standard machine learning models fail when observational data is poisoned by selection bias—mistaking baseline confounding for true treatment impact.

Inverse Propensity Scoring (IPS) provides an offline mathematical simulator that transforms biased operational logs into synthetic Randomized Controlled Trials (RCTs).

This guide breaks down the underlying propensity mathematics, positivity constraints, and production Python code to calculate unconfounded Average Treatment Effects (ATE).

Standard machine learning models excel at empirical risk minimization on static observational data, but fail catastrophically when an operational policy actively alters the data-generating distribution.

This advanced guide exposes the feature flattening trap—where models mistake downstream symptoms for upstream causes—and details how to deploy structurally invariant causal models using Judea Pearls Backdoor Criterion and Off-Policy Evaluation (OPE).