On the statistical role of inexact matching in observational studies

Autor:	Guo, Kevin, Rothenhäusler, Dominik
Rok vydání:	2022
Předmět:	Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. This paper shows 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 this robustness is the primary statistical role of inexact matching. Comment: To appear in Biometrika
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.13193 Zobrazit plný text záznamu View this record from Arxiv