Correlation vs. Causation: Measuring True Impact with Propensity Score Matching

A practical guide to implementing causal inference using propensity score matching (PSM) in Python. PSM addresses selection bias in observational data by finding 'statistical twins' — control group members with nearly identical characteristics to treated subjects — enabling fair comparison without a randomized experiment. The tutorial walks through the full pipeline: computing propensity scores via logistic regression, matching pairs with nearest neighbors and a caliper threshold, evaluating balance using standardized mean difference (SMD), and measuring treatment effect with t-tests and Cohen's D.