提出新因果概率度量，可区分治疗的必要与充分性。
Mediation Analysis for Probabilities of Causation
- 定义了控制直接、自然直接和间接路径的因果概率新指标
- 通过识别定理实现从观察数据中估计这些指标
- 在心理学真实数据上验证了方法有效性
因果概率（PoC）为决策提供重要参考。本文提出全新的PoC控制直接、自然直接及自然间接概率必要性与充分性（PNS）变体。这些度量能量化治疗对结果产生的必要性和充分性，同时考虑不同因果路径。我们推导出这些新PoC度量的识别定理，使它们可基于观测数据进行估计。通过分析一个真实心理学数据集，展示了方法的实际应用价值。
原文摘要 · Abstract (English)
Probabilities of causation (PoC) offer valuable insights for informed decision-making. This paper introduces novel variants of PoC-controlled direct, natural direct, and natural indirect probability of necessity and sufficiency (PNS). These metrics quantify the necessity and sufficiency of a treatment for producing an outcome, accounting for different causal pathways. We develop identification theorems for these new PoC measures, allowing for their estimation from observational data. We demonstrate the practical application of our results through an analysis of a real-world psychology dataset.
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