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Revisiting the Synthetic Control Estimator

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

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We analyze the conditions under which the Synthetic Control (SC) estimator is unbiased. We show that the SC estimator is generally biased if treatment assignment is correlated with time-varying unobserved confounders, even when the number of pre-treatment periods goes to infinity and in settings where one should expect to have an almost perfect pre-treatment fit. While we also show that a modified version of 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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