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Introduction to the Special Section on Synthetic Control Methods

In June of 2010, JASA published the article “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program” (Abadie et al., 2010). This article proposed a way to estimate the effects of aggregate interventions (i.e., interventions that happen at the level of aggregate units, such as cities, states, or countries) using weighted averages of untreated units to approximate the counterfactual outcome that the treated units would have experienced in the absence of the intervention. The method had originally appeared in a study of the effect of terrorism on economic growth in the Basque Country in Spain (Abadie and Gardeazabal, 2003). The specific characteristics of the empirical setting in Abadie and Gardeazabal (2003), where the units of analysis were entire regional entities, made alternative methods inappropriate and motivated the development of the synthetic control machinery. But it was not until after the publication of the 2010 JASA article that synthetic controls became a mainstay of empirical research in economics, the social sciences, and beyond. Today, synthetic controls are widely applied in the social sciences to study the effects of policy interventions and other treatments or events of interest. They have also been adopted as a research design in other disciplines, including the biomedical disciplines (especially in epidemiology) and engineering. Synthetic controls are also used by government agencies, multilateral organizations, NGOs, and business analytics units, and both the method and its applications have garnered the attention of the popular press and social media (see, e.g., Abadie, 2021).