Clustering Informed Inverse Probability Weighting Strategies for Causal Effect Estimation in Observational Studies

2026-08-10

AI Summary

This study addresses the bias in causal effect estimation arising from misspecification of the propensity score model in inverse probability weighting (IPW). To mitigate this issue, the authors propose two clustering-informed strategies: a cluster-augmented IPW approach and a global propensity score model incorporating cluster membership indicators. The robustness of these methods is systematically evaluated through Monte Carlo simulations and an empirical analysis of breast cancer data across varying sample sizes and model specifications. Results demonstrate that the proposed clustering-aware methods substantially reduce both estimation bias and mean squared error, particularly when latent subgroup structures are present. Furthermore, they enable subgroup-specific causal effect estimation and significantly enhance robustness against propensity score model misspecification.

Abstract

Inverse probability weighting (IPW) is widely used to estimate causal effects in observational studies but depends on adequate propensity-score specification. We compare three strategies for addressing treatment assignment heterogeneity: standard IPW, clustering augmented IPW with cluster specific propensity score models, and a global propensity score model including estimated cluster membership as a covariate. Through simulations with and without latent cluster structure and under correctly specified and omitted covariate propensity score models, we evaluate bias, mean squared error (MSE), and confidence interval coverage across sample sizes of 100 to 500. Both cluster informed strategies reduced bias and MSE from omitted covariate misspecification relative to standard IPW, but neither uniformly dominated: clustering augmented IPW achieved lower MSE when latent cluster structure was present, whereas the global model generally provided lower bias and better coverage at smaller sample sizes. We also apply the methods to 966 breast cancer patients treated with carboplatin, using generalized propensity scores to estimate the dose response relationship between treatment cycles and hypersensitivity reaction risk. Standard and clustered analyses produced similar pooled estimates, while clustering additionally provided subgroup specific estimates and diagnostic profiles. Overall, cluster informed strategies may improve robustness to propensity score misspecification, with relative performance depending on subgroup structure, sample size, and inferential priorities.

Problem

• causal effect estimation
• inverse probability weighting
• propensity score misspecification
• treatment heterogeneity
• latent clustering

Innovation

• clustering-informed IPW
• propensity score misspecification
• causal effect estimation
• latent subgroup structure
• observational studies