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

• 成果类型: Article; Early Access
• 署名作者: Chernozhukov, Victor; Wuthrich, Kaspar; Zhu, Yinchu
• 署名单位: Massachusetts Institute of Technology (MIT); University of London; University College London; Brandeis University
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/742424
• 发表日期: 2026
• 关键词: Asymptotic Theory; economic costs; heteroskedasticity; selection; RISK

摘要

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.