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A comparative analysis of different adjustment sets using propensity score based estimators

Author

Listed:
  • Luo, Shanshan
  • Min, Jiaqi
  • Li, Wei
  • Wang, Xueli
  • Geng, Zhi

Abstract

Propensity score based estimators are commonly employed in observational studies to address baseline confounders, without explicitly modeling their association with the outcome. In this paper, to fully leverage these estimators, we consider a series of regression models for improving estimation efficiency. The proposed estimators rely solely on a properly modeled propensity score and do not require the correct specification of outcome models. In addition, we consider a comparative analysis by applying the proposed estimators to four different adjustment sets, each consisting of background covariates. The theoretical results imply that incorporating predictive covariates into both propensity score and regression model demonstrates the lowest asymptotic variance. However, including instrumental variables in the propensity score may decrease the estimation efficiency of the proposed estimators. To evaluate the performance of the proposed estimators, we conduct simulation studies and provide a real data example.

Suggested Citation

  • Luo, Shanshan & Min, Jiaqi & Li, Wei & Wang, Xueli & Geng, Zhi, 2025. "A comparative analysis of different adjustment sets using propensity score based estimators," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
  • Handle: RePEc:eee:csdana:v:203:y:2025:i:c:s0167947324001634
    DOI: 10.1016/j.csda.2024.108079
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    References listed on IDEAS

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    1. Alberto Abadie & Guido W. Imbens, 2016. "Matching on the Estimated Propensity Score," Econometrica, Econometric Society, vol. 84, pages 781-807, March.
    2. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    3. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    4. Xinran Li & Peng Ding, 2017. "General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1759-1769, October.
    5. Jinyong Hahn, 2004. "Functional Restriction and Efficiency in Causal Inference," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 73-76, February.
    6. L. Liu & M. G. Hudgens & S. Becker-Dreps, 2016. "On inverse probability-weighted estimators in the presence of interference," Biometrika, Biometrika Trust, vol. 103(4), pages 829-842.
    7. Xavier De Luna & Ingeborg Waernbaum & Thomas S. Richardson, 2011. "Covariate selection for the nonparametric estimation of an average treatment effect," Biometrika, Biometrika Trust, vol. 98(4), pages 861-875.
    8. Yinghao Pan & Ying-Qi Zhao, 2021. "Improved Doubly Robust Estimation in Learning Optimal Individualized Treatment Rules," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 283-294, March.
    9. Fan Li & Kari Lock Morgan & Alan M. Zaslavsky, 2018. "Balancing Covariates via Propensity Score Weighting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 390-400, January.
    Full references (including those not matched with items on IDEAS)

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