On the statistical role of inexact matching in observational studies

• 成果类型: Article
• 署名作者: Guo, Kevin; Rothenhausler, Dominik
• 署名单位: Stanford University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asac066
• 发表日期: 2023
• 页码: 631644
• 关键词: Causal Inference; regression adjustment; medical literature; critical-appraisal; bias reduction; MULTIVARIATE; estimators; DESIGN

摘要

In observational causal inference, exact covariate matching plays two statistical roles: (i) it effectively controls for bias due to measured confounding; (ii) it justifies assumption-free inference based on randomization tests. In this paper we show that inexact covariate matching does not always play these same roles. We find that inexact matching often leaves behind statistically meaningful bias, and that this bias renders standard randomization tests asymptotically invalid. We therefore recommend additional model-based covariate adjustment after inexact matching. In the framework of local misspecification, we prove that matching makes subsequent parametric analyses less sensitive to model selection or misspecification. We argue that gaining such robustness is the primary statistical role of inexact matching.