Debiasing and t-tests for synthetic control inference on average causal effects

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• github.com/kwuthrich/scinference
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Abstract

We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls. We develop a K-fold cross-fitting procedure for bias correction. To avoid the difficult estimation of the long-run variance, inference is based on a self-normalized t-statistic, which has an asymptotically pivotal t-distribution. Our t-test is easy to implement, provably robust against misspecification, and valid with stationary and non-stationary data. It demonstrates an excellent small sample performance in application-based simulations and performs well relative to other methods. We illustrate the usefulness of the t-test by revisiting the effect of carbon taxes on emissions.

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