Synthetic Control for Health Policy

The synthetic control method evaluates the effect of a population-health policy implemented in a single aggregate unit — a state, country, or region — by building a data-driven comparator from a pool of untreated units. When a policy such as a tobacco tax, an alcohol-pricing law, a smoking ban, or a health-insurance expansion is enacted in one place, no single other place is a perfect counterfactual. The method instead forms a synthetic version of the treated unit as a weighted average of donor units chosen so that the synthetic closely tracks the treated unit's outcome and predictors before the policy. The post-intervention gap between the real unit and its synthetic twin estimates the policy's effect. Introduced by Abadie and Gardeazabal and formalized by Abadie, Diamond and Hainmueller — whose canonical application is California's Proposition 99 tobacco-control program — it has become a leading design for evaluating health policies at the population level, with placebo tests providing inference.