Recent approaches to causal inference have focused on causal effects defined as contrasts between the distribution of counterfactual outcomes under hypothetical interventions on the nodes of a graphical model. In this article we develop theory for causal effects defined with respect to a different type of intervention, one which alters the information propagated through the edges of the graph. These information transfer interventions may be more useful than node interventions in settings in which causes are non-manipulable, for example when considering race or genetics as a causal agent. Furthermore, information transfer interventions allow us to define path-specific decompositions which are identified in the presence of treatment-induced mediator-outcome confounding, a practical problem whose general solution remains elusive. We prove that the proposed effects provide valid statistical tests of mechanisms, unlike popular methods based on randomized interventions on the mediator. We propose efficient non-parametric estimators for a covariance version of the proposed effects, using data-adaptive regression coupled with semi-parametric efficiency theory to address model misspecification bias while retaining $\sqrt{n}$-consistency and asymptotic normality. We illustrate the use of our methods in two examples using publicly available data.

翻译：近期因果推断方法主要关注基于图形模型中节点假设干预下反事实结果分布对比定义的因果效应。本文针对一类不同干预类型（即改变图中边传递信息的干预）的因果效应发展理论。在原因不可操纵的情景下（如将种族或遗传因素视为因果变量时），信息传递干预可能比节点干预更具实用性。此外，信息传递干预使我们能够定义在存在治疗诱发中介-结果混杂时的路径特定分解——这一实际问题的一般性解决方案仍难以实现。我们证明，与基于中介随机化干预的流行方法不同，所提出的效应能为机制检验提供有效的统计检验。我们提出所提出效应的协方差版本的有效非参数估计方法，通过结合数据自适应回归与半参数效率理论解决模型误设偏差，同时保持$\sqrt{n}$一致性和渐近正态性。通过两个公开数据集实例展示方法的应用价值。