Evaluating Policy and Quantifying Uncertainty with Few (or One) Treated Unit(s): An Introduction to Synthetic Control Methods and Falsification Analyses


Amidst the push for data-driven decision making, policymakers increasingly rely on statisticians to evaluate program effectiveness before allocating additional resources to policy expansion. To estimate the effect of a policy, one must infer what would have happened to the treated unit had it not received treatment. This causal inference problem is further complicated by the hallmarks of many policy problems: observational data, few or one treated unit(s), site-selection bias, and an imperfect pool of naturally-occurring controls. We introduce synthetic control methods, an important advancement that aims to alleviate these problems by estimating a synthetic control, a combination of control units constructed to mirror the treated unit in terms of pre-treatment characteristics. However, with so few treated units, researchers must carefully justify model-based decisions and quantify uncertainty in communicating final results to clients. Using a recent application in community policing, we implement the augmented synthetic control method and demonstrate how falsification tests can supplement model output to contextualize the substantive significance of results.