  The same intervention can produce different effects in different sites.
Transport mediation estimators can estimate the extent to which such
differences can be explained by differences in compositional factors and the
mechanisms by which mediating or intermediate variables are produced; however,
they are limited to consider a single, binary mediator. We propose novel
nonparametric estimators of transported stochastic (in)direct effects that
consider multiple, high-dimensional mediators and intermediate variables. They
are multiply robust, efficient, asymptotically normal, and can incorporate
data-adaptive estimation of nuisance parameters. They can be applied to
understand differences in treatment effects across sites and/or to predict
treatment effects in a target site based on outcome data in source sites.
