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:近期因果推断方法侧重于确定和估计对因果剂的干预不可行或无实质性兴趣的情况下的因果关系,即:在改变图形模型节点的假设行动下,反事实结果的分布(拥有),改变图形模型的节点。在本条中,我们探索了一种使用\textit{causimpresidence}概念的替代方法,即通过改变通过定向循环图边缘传播的信息的行动,对因果剂干预不可行或无实质性兴趣的情况下,对因果关系的影响可能比对因果关系的影响更为有用,例如,在考虑性别、种族或遗传学作为因果剂时。此外,“信息转移”的干预提议使我们能够解决因果调解分析中长期存在的问题,即:在出现治疗导致的调解人-结果的混淆时,对特定路径的影响进行非参数识别。我们建议对拟议因果影响措施的共性采用有效的非相对性估算,使用数据适应性回归加上半定量效率理论,用以解决模型的因果性偏差性,同时保留现有的数据方法。