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Related Experiment Video

Updated: Sep 13, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Inverse probability weighting for causal inference in hierarchical data.

Lin Hu¹, Jie Yu¹, Chunxia Yang¹

¹Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.

BMC Medical Research Methodology

|August 2, 2025

Summary

Estimating average treatment effects in hierarchical data requires careful consideration of cluster characteristics to manage unmeasured confounders. Using cluster-mean stabilized weights or Bayesian additive regression trees (BART) improves estimation accuracy.

Keywords:

BART model Extreme weight Inverse probability weight Multilevel propensity score model Unmeasured cluster-level confounder

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Last Updated: Sep 13, 2025

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Published on: September 16, 2022

Area of Science:

Biostatistics
Epidemiology
Health Services Research

Background:

Hierarchical data with unmeasured cluster-level confounders present challenges for accurate average treatment effect (ATE) estimation.
Propensity score methods, including inverse probability weighting (IPW), are commonly used but can be sensitive to model misspecification and extreme weights.

Purpose of the Study:

To evaluate the impact of model misspecification, balance, and extreme weights on ATE estimation in hierarchical data with unmeasured cluster-level confounders.
To compare different strategies for constructing multilevel propensity score models and applying IPW.

Main Methods:

Simulated 48 hierarchical data scenarios with unmeasured cluster-level confounders.
Applied nine ATE estimation strategies using IPW with marginal stabilized weights and cluster-mean stabilized weights.
Handled extreme weights via truncation and applied models to HIV-TB co-infected patient data.

Main Results:

Bayesian additive regression trees (BART) with marginal propensity scores (BART-FE-Marginal) reduced extreme weights effectively.
Cluster-mean stabilized weights yielded smaller bias and RMSE compared to marginal stabilized weights when the positivity assumption was met.
TB treatment delay was identified as a risk factor for adverse treatment outcomes in HIV-TB co-infected patients.

Conclusions:

Accounting for cluster characteristics is crucial for controlling unmeasured cluster-level confounders in ATE estimation.
Recommends using BART for multilevel propensity score models or cluster-mean stabilized weights for improved accuracy.
Emphasizes the need for extreme weight handling when using marginal stabilized weights and highlights the importance of reducing weight variability and model misspecification.