Efficiently transporting causal (in)direct effects to new populations under intermediate confounding and with multiple mediators

TL;DR

This paper introduces new nonparametric estimators for transporting causal effects across populations, accommodating multiple high-dimensional mediators, and providing robust, efficient, and adaptable analysis of effect differences.

Contribution

It develops novel estimators that handle multiple mediators and intermediate variables, extending previous methods limited to a single binary mediator.

Findings

Estimators are multiply robust and asymptotically normal.

They can incorporate data-adaptive estimation of nuisance parameters.

Applicable to cross-site treatment effect analysis and prediction.

Abstract

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.