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A causal decomposition analysis allows researchers to determine whether thedifference in a health outcome between two groups can be attributed to adifference in each group's distribution of one or more modifiable mediatorvariables. With this knowledge, researchers and policymakers can focus ondesigning interventions that target these mediator variables. Existing methodsfor causal decomposition analysis either focus on one mediator variable orassume that each mediator variable is conditionally independent given the grouplabel and the mediator-outcome confounders. In this paper, we propose aflexible causal decomposition analysis method that can accommodate multiplecorrelated and interacting mediator variables, which are frequently seen instudies of health behaviors and studies of environmental pollutants. We extenda Monte Carlo-based causal decomposition analysis method to this setting byusing a multivariate mediator model that can accommodate any combination ofbinary and continuous mediator variables. Furthermore, we state the causalassumptions needed to identify both joint and path-specific decompositioneffects through each mediator variable. To illustrate the reduction in bias andconfidence interval width of the decomposition effects under our proposedmethod, we perform a simulation study. We also apply our approach to examinewhether differences in smoking status and dietary inflammation score explainany of the Black-White differences in incident diabetes using data from anational cohort study.

Path-specific causal decomposition analysis with multiple correlated mediator variables