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 weight (IPW).

Methods

We simulated 48 hierarchical data scenarios with unmeasured cluster-level confounders, fitting nine ATE estimation strategies. These strategies were combined with IPW, which used both marginal stabilized weights and cluster-mean stabilized weights. Extreme weights were handled by truncation. Moreover, these models were applied to data from patients co-infected with Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Liangshan Prefecture, Sichuan, China, to esti

SUBMITTER: Hu L 

PROVIDER: S-EPMC12317457 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature