| dc.description.abstract | Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approachesfor multiple mediators are often based on parametric models and thus may suffer
from model misspecification. Also, much of the existing literature either only allow estimation of the joint
mediation effect or estimate the joint mediation effect just as the sum of individual mediator effects, ignoring
the interaction among the mediators. In this article, we propose a novel Bayesian nonparametric method
that overcomes the two aforementioned drawbacks. We model the joint distribution of the observed data
(outcome, mediators, treatment, and confounders) flexibly using an enriched Dirichlet process mixture
with three levels. We use standardization (g-computation) to compute all possible mediation effects,
including pairwise and all other possible interaction among the mediators. We thoroughly explore our
method via simulations and apply our method to a mental health data from Wisconsin Longitudinal Study,
where we estimate how the effect of births from unintended pregnancies on later life mental depression
(CES-D) among the mothers is mediated through lack of self-acceptance and autonomy, employment
instability, lack of social participation, and increased family stress. Our method identified significant
individual mediators, along with some significant pairwise effects. | en_US |
