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On the statistical role of inexact matching in observational studies

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  • Kevin Guo
  • Dominik Rothenhäusler

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.

Suggested Citation

  • Kevin Guo & Dominik Rothenhäusler, 2023. "On the statistical role of inexact matching in observational studies," Biometrika, Biometrika Trust, vol. 110(3), pages 631-644.
    • Handle: RePEc:oup:biomet:v:110:y:2023:i:3:p:631-644.
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    File URL: http://hdl.handle.net/10.1093/biomet/asac066
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