Estimation Strategies for Causal Decomposition Analysis with Allowability Specifications

2026-02-08

AI Summary

This study addresses the limitations of causal decomposition analysis (CDA) in health disparities research, particularly its reliance on challenging density modeling and its disconnect from mainstream causal estimation methods. To overcome these issues, the authors propose two novel classes of estimators: a “bridge” estimator that avoids explicit density modeling and a weighted sequential regression estimator that enjoys multiple robustness properties. Building upon the potential outcomes framework, they develop an interpretable causal decomposition system that integrates nonparametric estimation, weighted regression, and diagnostic tools for density assessment. Under fairness-motivated covariate constraints, the approach enables robust estimation of causal effects. Empirical evaluations using real-world electronic health records—both in simulations and applied analyses of hypertension control disparities—demonstrate that the proposed methods substantially outperform existing approaches in both accuracy and robustness.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

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
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Causal decomposition analysis (CDA) is an approach for modeling the impact of hypothetical interventions to reduce disparities. It is useful for identifying foci that future interventions, including multilevel and multimodal interventions, could focus on to reduce disparities. Based within the potential outcomes framework, CDA has a causal interpretation when the identifying assumptions are met. CDA also allows an analyst to consider which covariates are allowable (i.e., fair) for defining the disparity in the outcome and in the point of intervention, so that its interpretation is also meaningful. While the incorporation of causal inference and allowability promotes robustness, transparency, and dialogue in disparities research, it can lead to challenges in estimation such as the need to correctly model densities. Also, how CDA differs from commonly used estimators may not be clear, which may limit its uptake. To address these challenges, we provide a tour of estimation strategies for CDA, reviewing existing proposals and introducing novel estimators that overcome key estimation challenges. Among them we introduce what we call"bridging"estimators that avoid directly modeling any density, and weighted sequential regression estimators that are multiply robust. Additionally, we provide diagnostics to assess the quality of the nuisance density models and weighting functions they rely on. We formally establish the estimators'robustness to model mis-specification, demonstrate their performance through a simulation study based on real data, and apply them to study disparities in hypertension control using electronic health records in a large healthcare system.

Problem

• causal decomposition analysis
• estimation challenges
• density modeling
• disparities research
• allowability specifications

Innovation

• causal decomposition analysis
• bridging estimators
• multiply robust estimation
• allowability specifications
• nuisance model diagnostics