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：最近因果推断的方法集中在所谓的因果效应上，即在一个图形模型的节点上进行假设性干预，通过对比不同情况下统计量的分布来定义因果效应。在这篇论文中，我们开发了一种针对不同类型干预的因果效应理论，这种干预改变图形模型边上传播的信息。与对节点进行干预的方法相比，在一些因果作用难以操纵的情况下，例如考虑种族或遗传因素作为因果代理时，使用信息传递干预可能更加有用。此外，信息传递干预使我们能够定义路径特定的分解，并在治疗引起的介质器官混淆存在时被鉴定。这是一个实际问题，其一般解决方案仍然难以找到。我们证明了所提出的效应提供有效的机制检验统计方法，这与基于介质的随机干预的普及方法不同。我们提出了一个有效的无参数的协方差版本的方法来评估所提出的效应 - 使用自适应回归技术和半参数效率理论来应对模型规范偏差，同时保持根号n-一致和渐进正态性。我们通过两个公开数据示例说明了我们的方法的使用。