Debiasing and $t$-tests for synthetic control inference on average causal effects
Econometrics
2025-05-26 v9
Abstract
We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls. We develop a -fold cross-fitting procedure for bias correction. To avoid the difficult estimation of the long-run variance, inference is based on a self-normalized -statistic, which has an asymptotically pivotal -distribution. Our -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 -test by revisiting the effect of carbon taxes on emissions.
Cite
@article{arxiv.1812.10820,
title = {Debiasing and $t$-tests for synthetic control inference on average causal effects},
author = {Victor Chernozhukov and Kaspar Wuthrich and Yinchu Zhu},
journal= {arXiv preprint arXiv:1812.10820},
year = {2025}
}