Randomization-based confidence sets for the local average treatment effect

• 成果类型: Article
• 署名作者: Aronow, P. M.; Chang, Haoge; Lopatto, Patrick
• 署名单位: Yale University; Columbia University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444; 1464-3510
• DOI: 10.1093/biomet/asag010
• 发表日期: 2026
• 页码: asag010
• 关键词: Analysis of experiments; noncompliance; randomization inference; INSTRUMENTAL VARIABLES REGRESSION; permutation tests; weak instruments; inference; identification; models; robust; size

摘要

We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens & Rosenbaum (2005) has desirable properties. Specifically, we show that using a studentized Anderson-Rubin statistic as a test statistic yields confidence sets that are finite-sample exact under treatment effect homogeneity and remain asymptotically valid for the local average treatment effect when the treatment effects are heterogeneous. We provide a uniform analysis of this procedure and efficient algorithms to construct the confidence sets.