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We propose a sensitivity analysis for Synthetic Control (SC) treatment effectestimates to interrogate the assumption that the SC method is well-specified,namely that choosing weights to minimize pre-treatment prediction error yieldsaccurate predictions of counterfactual post-treatment outcomes. Our data-drivenprocedure recovers the set of treatment effects consistent with the assumptionthat the misspecification error incurred by the SC method is at most theobservable misspecification error incurred when using the SC estimator topredict the outcomes of some control unit. We show that under one definition ofmisspecification error, our procedure provides a simple, geometric motivationfor comparing the estimated treatment effect to the distribution of placeboresiduals to assess estimate credibility. When we apply our procedure toseveral canonical studies that report SC estimates, we broadly confirm theconclusions drawn by the source papers.

Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error