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Estimation of average treatment effect based on a semiparametric propensity score

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  • Yu Sun
  • Karen X. Yan
  • Qi Li

Abstract

This paper considers the estimation of average treatment effect using propensity score method. We propose to use a semiparametric single-index model to estimate the propensity score. This avoids the curse of dimensionality problem with the nonparametric method based propensity score estimator. We establish the asymptotic distribution of the average treatment effect estimator. Monte Carlo simulation results show that the proposed method works well in finite samples and outperforms the conventional nonparametric kernel approach. We apply the proposed method to an empirical data examining the efficacy of right heart catheterization on medical outcomes.

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  • Yu Sun & Karen X. Yan & Qi Li, 2021. "Estimation of average treatment effect based on a semiparametric propensity score," Econometric Reviews, Taylor & Francis Journals, vol. 40(9), pages 852-866, October.
    • Handle: RePEc:taf:emetrv:v:40:y:2021:i:9:p:852-866
    DOI: 10.1080/07474938.2021.1889206
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    Cited by:

    1. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Papers 2206.08503, arXiv.org, revised Oct 2022.

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