14 Path-specific causal decomposition analysis with multiple correlated mediator variables

Oregon Convention Center 

A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. Existing methods either focus on one mediator variable or assume that each is conditionally independent given the group label and the mediator-outcome confounders. In this work, we propose a flexible method that can accommodate multiple correlated and interacting mediator variables, which are frequently seen in studies of health behaviors and environmental pollutants. Further, we state the causal assumptions needed to identify both joint and path-specific decomposition effects through each mediator variable. To illustrate the reduction in bias and confidence interval width of the decomposition effects, we perform a simulation study and apply our approach to examine whether differences in smoking status and dietary inflammation score explain any of the Black-White differences in incident diabetes.