Debiasing and t-Tests for Synthetic Control Inference on Average Causal Effects

Victor Chernozhukov, Kaspar Wüthrich and Yinchu Zhu

Journal of Political Economy, 2026, vol. 134, issue 9, 2740 - 2777

Abstract: We propose a practical and robust method for making inference 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 nonstationary 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.

Date: 2026
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