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Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score

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  • Jiangang Liao
  • Charles Rohde

Abstract

The propensity methodology is widely used in medical research to compare different treatments in designs with a nonrandomized treatment allocation. The inverse probability weighted (IPW) estimators are a primary tool for estimating the average treatment effect but the large variance of these estimators is often a significant concern for their reliable use in practice. Inspired by Rao‐Blackwellization, this paper proposes a method to smooth an IPW estimator by replacing the weights in the original estimator by their mean over a distribution of the potential treatment assignment. In our simulation study, the smoothed IPW estimator achieves a substantial variance reduction over its original version with only a small increased bias, for example two‐to‐sevenfold variance reduction for the three IPW estimators in Lunceford and Davidian [Statistics in Medicine, 23(19), 2937–2960]. In addition, our proposed smoothing can also be applied to the locally efficient and doubly robust estimator for added protection against model misspecification. An implementation in R is provided.

Suggested Citation

  • Jiangang Liao & Charles Rohde, 2022. "Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score," Biometrics, The International Biometric Society, vol. 78(2), pages 660-667, June.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:2:p:660-667
    DOI: 10.1111/biom.13454
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    References listed on IDEAS

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