Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects. We present an analogous decomposition of the population intervention effect , defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for continuous and categorical exposures. We show that identification of direct and indirect effects for the population intervention effect requires weaker assumptions than its average treatment effect counterpart, under the assumption of no mediator–outcome confounders affected by exposure. In particular, identification of direct effects is guaranteed in experiments that randomize the exposure and the mediator. We propose various estimators of the direct and indirect effects, including substitution, reweighted and efficient estimators based on flexible regression techniques, allowing for multivariate mediators. Our efficient estimator is asymptotically linear under a condition requiring n ^1/4‐consistency of certain regression functions. We perform a simulation study in which we assess the finite sample properties of our proposed estimators. We present the results of an illustrative study where we assess the effect of participation in a sports team on the body mass index among children, using mediators such as exercise habits, daily consumption of snacks and overweight status.

中文翻译：

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随机干预的因果中介分析

因果推理中的调解分析传统上一直侧重于二元暴露和确定性干预措施，以及平均治疗效果在直接和间接影响方面的分解。我们提出了人口干预作用的类似分解，是通过对暴露的随机干预定义的。人口干预效应提供了一个通用框架，可以在其中定义各种有趣的因果对比，包括对连续和分类暴露的影响。我们表明，在没有介导者-结果混杂因素受暴露影响的假设下，确定对人群干预作用的直接和间接作用需要比其平均治疗作用对应物更弱的假设。尤其是，在使接触和介体随机化的实验中，可以确保直接效应的识别。我们提出了直接和间接影响的各种估计量，包括基于灵活回归技术的替代，加权和有效估计量，并允许使用多变量介体。Ñ ^1/4 -consistency的某些回归函数。我们进行了仿真研究，在其中评估了我们提出的估计量的有限样本属性。我们提供了一项说明性研究的结果，其中我们使用运动习惯，日常零食的摄入量和超重状态等中介因素，评估了参加运动队对儿童体重指数的影响。