Issue #137 - From Randomized to Natural Experiments

A practical guide to causal inference estimation methods, covering the full toolkit from randomized controlled trials (the gold standard) through adjustment-based methods (regression, matching, propensity scores, doubly robust AIPW) to quasi-experimental designs like Difference-in-Differences. Explains when each method applies, the assumptions each requires, and provides a decision guide for choosing among them. Part 2 of a 3-part series; Part 3 will cover ML-based causal inference including meta-learners, Double Machine Learning, causal forests, and uplift modeling.