  We propose a sensitivity analysis for Synthetic Control (SC) treatment effect
estimates to interrogate the assumption that the SC method is well-specified,
namely that choosing weights to minimize pre-treatment prediction error yields
accurate predictions of counterfactual post-treatment outcomes. Our data-driven
procedure recovers the set of treatment effects consistent with the assumption
that the misspecification error incurred by the SC method is at most the
observable misspecification error incurred when using the SC estimator to
predict the outcomes of some control unit. We show that under one definition of
misspecification error, our procedure provides a simple, geometric motivation
for comparing the estimated treatment effect to the distribution of placebo
residuals to assess estimate credibility. When we apply our procedure to
several canonical studies that report SC estimates, we broadly confirm the
conclusions drawn by the source papers.
