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On propensity score matching with a diverging number of matches

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  • Yihui He
  • Fang Han

Abstract

This paper reexamines Abadie and Imbens (2016)'s work on propensity score matching for average treatment effect estimation. We explore the asymptotic behavior of these estimators when the number of nearest neighbors, $M$, grows with the sample size. It is shown, hardly surprising but technically nontrivial, that the modified estimators can improve upon the original fixed-$M$ estimators in terms of efficiency. Additionally, we demonstrate the potential to attain the semiparametric efficiency lower bound when the propensity score achieves "sufficient" dimension reduction, echoing Hahn (1998)'s insight about the role of dimension reduction in propensity score-based causal inference.

Suggested Citation

  • Yihui He & Fang Han, 2023. "On propensity score matching with a diverging number of matches," Papers 2310.14142, arXiv.org, revised Nov 2023.
    • Handle: RePEc:arx:papers:2310.14142
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    References listed on IDEAS

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