Adverse health outcomes arise from the complex interplay between multiple socio-demographic, behavior, lifestyle, genetic susceptibility factors and their interactions with various environmental contaminants. The complex set of risk factors includes dietary nutrients, physical activity, infectious agents, air pollutants and metal exposures at both the individual and community levels. The relative contributions of individual potential risk factors have not been well studied. Furthermore, it is unclear how the effects of exposures transmitted through intermediate biological pathways to the health outcome endpoint. To elucidate the causal effects of multiple interacting factors at different levels poses great analytical challenges. We consider a counterfactual potential outcome framework and propose causal inference methods that simultaneously model multiple mediators in the presence of interaction effects. We demonstrate the performance of the proposed approach through extensive simulation studies and application to a real dataset that has measured participants’ exposures to toxic metals, socio-demographic status, and adverse health outcomes.