因果中介概率
Mediated probabilities of causation
AI总结：
本文提出“因果中介概率”估计量，量化负面结果通过中介路径或直接路径诱导的概率，给出识别条件与双重稳健投影估计策略，在法律和医学因果归因场景中比因果概率更具相关性。
摘要：
我们提出了一组因果估计量，称之为“因果中介概率”。这些估计量量化了在包含二元暴露或干预、单一二元中介变量和二元结果的典型场景中，观察到的负面结果是通过中介路径还是直接路径诱导的概率。我们概述了在给定观测数据下识别这些效应的一组充分条件，并提出了一种基于双重稳健投影的估计策略，该策略允许使用灵活的非参数和机器学习方法进行估计。我们认为，这些效应可能比因果概率更相关，特别是在同时观察到负面结果和负面中介事件，且希望区分结果是通过暴露诱导中介变量还是暴露直接诱导结果的场景中。我们通过讨论其在法律和医学因果归因问题中的应用来论证这些效应的合理性。
英文摘要：
We propose a set of causal estimands that we call the "mediated probabilities of causation." These estimands quantify the probabilities that an observed negative outcome was induced via a mediating pathway versus a direct pathway in a stylized setting involving a binary exposure or intervention, a single binary mediator, and a binary outcome. We outline a set of conditions sufficient to identify these effects given observed data, and propose a doubly-robust projection based estimation strategy that allows for the use of flexible non-parametric and machine learning methods for estimation. We argue that these effects may be more relevant than the probability of causation, particularly in settings where we observe both some negative outcome and negative mediating event, and we wish to distinguish between settings where the outcome was induced via the exposure inducing the mediator versus the exposure inducing the outcome directly. We motivate these estimands by discussing applications to legal and medical questions of causal attribution.
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