Inverse probability weighting for causal inference in hierarchical data

Inverse probability weighting for causal inference in hierarchical data

Abstract Objective The aim of this study was to explore the impact of model misspecification, balance, and extreme weights on average treatment effect (ATE) estimation in hierarchical data with unmeasured cluster-level confounders using the multilevel propensity score model and inverse probability w...

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Bibliographic Details
Main Authors: Lin Hu, Jie Yu, Chunxia Yang, Miaoshuang Chen, Zihuan Tang, Rujun Liao, Chunnong Jike, Ju Wang, Ruobing Wang, Qiang Liao, Tao Zhang
Format: Article
Language:English
Published: BMC 2025-08-01
Series:BMC Medical Research Methodology
Subjects:
Multilevel propensity score model
Inverse probability weight
BART model
Extreme weight
Unmeasured cluster-level confounder
Online Access:https://doi.org/10.1186/s12874-025-02627-w
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