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

Abstract

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.