Unlock: Causal Inference for Policy Evaluation

Quasi-experimental methods for recovering policy effects without randomization. Difference-in-differences identifies the average treatment effect on the treated under parallel trends; regression discontinuity identifies a local average treatment effect under continuity at the cutoff; instrumental variables identifies a local average treatment effect for compliers under monotonicity (Imbens-Angrist 1994). Synthetic control and double/debiased ML extend these designs to single-unit and high-dimensional settings.