论文标题
有效地将因果（IN）直接运输到中间混杂的新人群，并与多个调解人
Efficiently transporting causal (in)direct effects to new populations under intermediate confounding and with multiple mediators
论文作者
论文摘要
相同的干预措施可以在不同的地点产生不同的影响。运输中介估计器可以估计由组成因子的差异以及产生介导或中间变量的机制来解释这种差异的程度；但是，它们仅限于考虑一个二进制调解人。我们提出了转运的随机（IN）直接效应的新型非参数估计量，该效应考虑了多个高维介体和中间变量。它们是繁重，有效，渐近正常的倍数，并且可以结合滋扰参数的数据自适应估计。它们可以应用于了解基于源场所的结果数据的目标部位的治疗效果的差异和/或预测目标部位的治疗效果。
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.
