提出双中介下因果必要性与充分性概率的分解方法
Decomposition of Probabilities of Causation with Two Mediators
- 基于双中介构建路径特异性因果概率分解框架
- 给出可识别性定理并验证有限样本估计性能
- 适用于教育等领域的因果机制分析
因果中介分析中的因果概率（PoC）为评估治疗在通过不同因果路径引发事件时的必要性和充分性提供了基础框架。因果中介分析的核心目标之一是将总效应分解为特定路径成分。本文研究路径特异性的必要性与充分性概率（PNS），以分解治疗与结果间经由两条中介路径的总PNS，提出路径特异性PNS定义并建立识别定理。进一步通过数值实验评估有限样本下估计器的性质，并利用真实教育数据集展示其实际应用价值。
原文摘要 · Abstract (English)
Mediation analysis for probabilities of causation (PoC) provides a fundamental framework for evaluating the necessity and sufficiency of treatment in provoking an event through different causal pathways. One of the primary objectives of causal mediation analysis is to decompose the total effect into path-specific components. In this study, we investigate the path-specific probability of necessity and sufficiency (PNS) to decompose the total PNS into path-specific components along distinct causal pathways between treatment and outcome, incorporating two mediators. We define the path-specific PNS for decomposition and provide an identification theorem. Furthermore, we conduct numerical experiments to assess the properties of the proposed estimators from finite samples and demonstrate their practical application using a real-world educational dataset.
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