Propensity Score Weighting to Ensure Balance in Key Subgroups or Strata: A Practical Guide

2026-04-15

AI Summary

This study addresses the challenge of substantial heterogeneity across patient subgroups when estimating treatment effects using electronic health records, which often undermines covariate balance in key clinical subpopulations under conventional propensity score weighting. To overcome this limitation, the authors propose a stratified propensity score weighting approach that first partitions patients based on clinical indications, reasons for admission, or risk factors, and then constructs separate propensity score models within each stratum to compute weights. This strategy prioritizes comparability within clinically meaningful subgroups and systematically accounts for differences in prognosis, heterogeneity in exposure probabilities, and covariate–subgroup interactions. Empirical analyses demonstrate that the proposed framework markedly improves covariate balance and enhances the accuracy of causal effect estimates within critical subgroups, offering a more robust method for causal inference in complex hospitalized populations.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Propensity score weighting approaches have been widely implemented in clinical research to estimate the effects of a treatment or exposure while mitigating the risk of confounding in the absence of random assignment. In practice, when working with large electronic health records (EHR) or administrative datasets to evaluate health quality outcomes at the institutional level, or evaluate supportive care interventions for a wide range of hospitalized patients, it may be advisable to stratify the propensity score weighting approach by indication, reason for admission, or other clinical risk factors due to the potential for substantial heterogeneity across subgroups of patients with complex care needs. A stratified approach may be appropriate if (i) prognosis differs substantially between patient subgroups such that achieving balance in the composition of these strata between exposure/treatment groups should be prioritized, (ii) likelihood of exposure differs substantially across clinical subgroups, or (iii) the covariate-exposure associations are expected to differ substantially between subgroups (i.e. there are covariate-subgroup interactions in the exposure/treatment propensity model). For example, we may want to evaluate the impact of prophylactic anticoagulant use for venous thromboembolism prevention in elderly patients admitted to hospital for a wide array of conditions. The purpose of this article is to outline an approach to implementing propensity score weighting with stratification by clinical groups. We also provide guidance on best practices with particular focus on EHR and administrative medical data, and population health settings.

Problem

• propensity score weighting
• subgroup balance
• clinical heterogeneity
• electronic health records
• stratification

Innovation

• propensity score weighting
• stratification
• subgroup balance
• electronic health records
• confounding control