Recent approaches to causal inference have focused on the identification and estimation of \textit{causal effects}, defined as (properties of) the distribution of counterfactual outcomes under hypothetical actions that alter the nodes of a graphical model. In this article we explore an alternative approach using the concept of \textit{causal influence}, defined through operations that alter the information propagated through the edges of a directed acyclic graph. Causal influence may be more useful than causal effects in settings in which interventions on the causal agents are infeasible or of no substantive interest, for example when considering gender, race, or genetics as a causal agent. Furthermore, the "information transfer" interventions proposed allow us to solve a long-standing problem in causal mediation analysis, namely the non-parametric identification of path-specific effects in the presence of treatment-induced mediator-outcome confounding. We propose efficient non-parametric estimators for a covariance version of the proposed causal influence measures, 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：近期因果推断方法聚焦于因果效应的识别与估计，其定义为在改变图形模型中节点的假设干预下反事实结果的分布（或分布特性）。本文探索一种替代方法，利用"因果影响"概念，该概念通过改变有向无环图中边所传播的信息来实现操作。当对因果因素的干预不可行或无实质意义时（例如将性别、种族或遗传作为因果因素），因果影响可能比因果效应更为实用。此外，本文提出的"信息传递"干预方法解决了因果中介分析中的长期难题：在存在干预诱导型中介-结局混杂的情况下，对路径特异性效应进行非参数识别。我们提出了一种协方差版本的因果影响度量的高效非参数估计量，通过结合数据自适应回归与半参数效率理论，在保持$\sqrt{n}$一致性和渐近正态性的同时，解决了模型误设偏差问题。我们使用公开数据通过两个示例展示了该方法的实际应用。