A1053
Title: Nonparametric mediation estimators that accommodate multiple mediators and multiple intermediate confounders
Authors: Kara Rudolph - Columbia University (United States) [presenting]
Abstract: Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including 1) the existence of post-exposure variables that also affect mediators and outcomes (thus confounding the mediator-outcome relationship), that may also be 2) multivariate, and 3) the existence of multivariate mediators. All three challenges are present in the mediation analysis considered. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. This gap is addressed by extending a recently developed nonparametric estimator for the IDE/IIE to allow for multivariate mediators and multivariate post-exposure confounders simultaneously. The proposed estimation approach is applied to the analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.
