On the statistical role of inexact matching in observational studies

Kevin Guo and Dominik Rothenhäusler

Biometrika, 2023, vol. 110, issue 3, 631-644

Abstract: SummaryIn 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.

Keywords: Matching; Observational causal inference; Randomization test (search for similar items in EconPapers)
Date: 2023
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