Su Yeon Kim | Searcharxiv

arXiv2026

When Do Traditional and Causal Decomposition Methods Diverge? A Practical Guide for Studying Health Disparities

Identifying malleable factors that can reduce social disparities is a central objective among researchers across disciplines. Traditionally, researchers have relied on the difference-in-coefficients and Kitagawa-Oaxaca-Blinder frameworks. More recently, methods grounded in the potential outcomes framework have emerged. While these methods share the same goal of identifying drivers of disparity, they frequently yield divergent results depending on the underlying confounding structures and research settings. Despite these significant differences, applied researchers lack clear guidance on selecting appropriate methods for their specific research contexts. To address this gap, this study provides a systematic review and offer an intuitive guidance through Directed Acyclic Graphs and comparative simulation studies. We begin by reviewing each method assuming no unmeasured confounding, which is often violated in observational settings. Consequently, we extend our analysis to two realistic scenarios: 1) unmeasured confounding exists in the relationship between intermediate confounders and the mediator, and 2) unmeasured confounding exists in the relationship between the mediator and the outcome. Finally, we illustrate these recommendations through a case study examining the role of educational attainment in explaining racial disparities in later-life cognition.

stat.AP↗

arXiv2026

Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities

Educational disparities are rooted in and perpetuate social inequalities across multiple dimensions such as race, socioeconomic status, and geography. To reduce disparities, most intervention strategies focus on a single domain and frequently evaluate their effectiveness by using causal decomposition analysis. However, a growing body of research suggests that single-domain interventions may be insufficient for individuals marginalized on multiple fronts. While interventions across multiple domains are increasingly proposed, there is limited guidance on appropriate methods for evaluating their effectiveness. To address this gap, we develop an extended causal decomposition analysis that simultaneously targets multiple causally ordered intervening factors, allowing for the assessment of their synergistic effects. These scenarios often involve challenges related to model misspecification due to complex interactions among group categories, intervening factors, and their confounders with the outcome. To mitigate these challenges, we introduce a triply robust estimator that leverages machine learning techniques to address potential model misspecification. We apply our method to a cohort of students from the High School Longitudinal Study, focusing on math achievement disparities between Black, Hispanic, and White high schoolers. Specifically, we examine how two sequential interventions - equalizing the proportion of students who attend high-performing schools and equalizing enrollment in Algebra I by 9th grade across racial groups - may reduce these disparities.

stat.ME↗