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：近期因果推断方法侧重于通过图模型节点上的假设干预定义反事实结果分布对比的因果效应。本文针对另一种干预类型（改变通过图边传播的信息）所定义的因果效应展开理论研究。在原因不可操控的场景中（例如将种族或遗传因素视为因果因子时），此类信息传递干预可能比节点干预更具实用价值。此外，信息传递干预使我们得以定义在存在治疗诱导型中介-结局混杂时可识别的路径特异性分解——该类混杂的通用解决方案至今仍未明确。我们证明，相较于基于中介随机化干预的流行方法，所提出的效应能为机制检验提供有效的统计检验。通过结合数据自适应回归与半参数效率理论，我们为所提效应的协方差形式构建了高效非参数估计量，可在保持$\sqrt{n}$一致性和渐近正态性的同时解决模型误设偏差。最后通过两个公开数据集实例说明方法的应用。