The synthetic control method, introduced in Abadie and Gardeazabal (2003), has emerged as a popular empirical methodology for estimating a causal effects with observational data, when the “gold standard” of a randomized control trial is not feasible. In a recent survey on causal inference and program evaluation methods in economics, Athey and Imbens (2015) describe the synthetic control method as “arguably the most important innovation in the evaluation literature in the last fifteen years”. While many of the most prominent application of the method, as well as its genesis, were initially circumscribed to the policy evaluation literature, synthetic controls have found their way more broadly to social sciences, biological sciences, engineering and even sports.

In this talk, we will touch upon the literature on methodical aspects, mathematical foundations and empirical case studies of synthetic controls. We will provide guidance for empirical practice, with special emphasis on feasibility and data requirements, and characterize the practical settings where synthetic controls may be useful and those where they may fail. We will describe empirical case-studies from policy evaluation, retail, and sports. We will utilize connections of synthetic controls to matrix and tensor estimation, high-dimensional regression, and time series analysis.

Based on a joint works with A Agarwal, M Amjad, D Shen (all at MIT), R Cosson (Ecole Polytechnique) and V Misra (Columbia).