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Mediation analysis is appealing for its ability to improve understanding ofthe mechanistic drivers of causal effects, but real-world data complexitieschallenge its successful implementation, including: 1) the existence ofpost-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. Interventionaldirect and indirect effects (IDE/IIE) accommodate post-exposure variables thatconfound the mediator-outcome relationship, but currently, no estimator forIDE/IIE exists that allows for both multivariate mediators and multivariatepost-exposure intermediate confounders. This, again, represents a significantlimitation for real-world analyses. We address this gap by extending tworecently developed nonparametric estimators -- one that estimates the IDE/IIEand another that estimates the IDE/IIE transported to a new, target population-- to allow for multivariate mediators and multivariate intermediateconfounders simultaneously. We use simulation to examine finite sampleperformance, and apply these estimators to longitudinal data from the Moving toOpportunity trial. In the application, we walk through a strategy forseparating indirect effects into mediator- or mediator-group-specific indirecteffects, while appropriately accounting for other, possibly co-occurringintermediate variables.

Nonparametric estimators of interventional (transported) direct and  indirect effects that accommodate multiple mediators and multiple  intermediate confounders