The Synthetic Control Method is an increasingly popular approach to quasi-experimental causal inference and policy evaluation. The method involves the construction of a control time
series which optimally approximates the characteristics of the treated series up to the point of the
intervention as a weighted combination of less-than-ideal, but uncontaminated “donor pool”
units. Because the synthetic control is constructed from a set of uncontaminated controls, the post intervention synthetic series is intended to approximate the treated series "had the
intervention never occurred". After briefly situating the method within causal inference and
quasi-experimental literatures, an example analysis will be conducted in R replicating one of the foundational synthetic control studies. The talk will then shift to spatial considerations and how recent applications of the design have, with varying success, mitigated threats to validity such as spillover, spatial autocorrelation, and regional heterogeneity, all of which complicate causal inference in spatial contexts.