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Revisiting the Synthetic Control Estimator

Ferman, Bruno and Pinto, Cristine (2016): Revisiting the Synthetic Control Estimator.

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Abstract

We analyze the conditions under which the Synthetic Control (SC) estimator is asymptotically unbiased when the number of pre-treatment periods goes to infinity. We show that the SC estimator is generally asymptotically biased if treatment assignment is correlated with time-varying unobserved confounders, and this may be true even if the pre-treatment fit is almost perfect. While we also show that the SC method can substantially improve relative to standard methods, our results suggest that researchers should be more careful in interpreting the identification assumptions required for this method.

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