An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

• 成果类型: Article
• 署名作者: Chernozhukov, Victor; Wuthrich, Kaspar; Zhu, Yinchu
• 署名单位: Massachusetts Institute of Technology (MIT); University of California System; University of California San Diego; Brandeis University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1920957
• 发表日期: 2021
• 页码: 1849-1864
• 关键词: permutation tests; factor models; prediction; regression; rates; selection; RECOVERY; number

摘要

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. Our approach demonstrates an excellent small-sample performance in simulations and is taken to a data application where we re-evaluate the consequences of decriminalizing indoor prostitution. Open-source software for implementing our conformal inference methods is available.