Identifying Causally-Robust Mediators of Health Disparities: A Review and Simulation Studies With Directed Acyclic Graphs

Abstract: Background Traditionally researchers have used linear approaches such as difference in coefficients DIC and Kitagawa Oaxaca Blinder KOB decomposition to identify risk factors or resources referred to as mediators underlying health disparities Recently causal decomposition analysis CDA has gained popularity by defining clear causal effects of interest and estimating them without modeling restrictions Methods We begin with a brief review of each method under the assumption of no unmeasured confounders followed by two realistic scenarios where unmeasured confounders affect first the relationship between intermediate confounders and the mediator and second the relationship between the mediator and the outcome For each scenario we generate simulated data apply all three methods compare estimates and interpret results using directed acyclic graphs Results The DIC approach performs well only when no intermediate confounders are present a condition unlikely in real world health disparities that arise from many factors over the life course The KOB decomposition is appropriate only when baseline covariates such as age need not be controlled When unmeasured confounding exists DIC yields biased estimates in both scenarios while both KOB and CDA yield biased estimates in the second scenario however CDA supplemented by sensitivity analysis can reveal how robust its estimates are to unmeasured confounding Conclusions We recommend against using DIC for investigating drivers of health disparities and instead advise applying CDA combined with sensitivity analysis as a robust strategy for identifying mediators of health disparities