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April 10, 2026Statistical Methods in Medical Research

Beyond weighting: Propensity score modeling for causal inference

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Authors

RZRong J.B. Zhu

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Overview

This article demonstrates improved causal inference through propensity score modeling, indicating enhanced reliability.

Key Points

  • The aim is to improve causal inference by addressing challenges in propensity score weighting.
  • Identified potential outcomes as a function of the propensity score.
  • Utilized spline regression to estimate the function.
  • Derived treatment effect estimation from spline regression's asymptotic normality.
  • Extended method to regression-based adjustment for better efficiency.
  • Conducted extensive simulations to compare variance and bias.
  • Achieved lower variance compared to inverse probability weighting methods.
  • Maintained low bias and robustness against propensity score misspecification.
  • Real-data application confirmed the method's reliability in inference.

Cite This Study

Rong J.B. Zhu (2026) studied this question.

synapsesocial.com/papers/69d896676c1944d70ce07c25https://doi.org/10.1177/09622802261436960
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  1. 1Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study2004 · 1,708 citations
  2. 2A Generalization of Sampling Without Replacement from a Finite Universe1952 · 3,066 citations
  3. 3Bias associated with using the estimated propensity score as a regression covariate2013 · 83 citations
  4. 4Extensions of the Penalized Spline of Propensity Prediction Method of Imputation2008 · 61 citations
  5. 5Causal Inference for Statistics, Social, and Biomedical Sciences2015 · 4,315 citations