提出新方法提升因果概率计算效率，用于系统性定位根本原因。
Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models
- 用准马尔可夫模型简化因果概率计算
- 能更高效地给出因果概率的紧致边界
- 适合故障诊断与系统可靠性分析场景
因果概率为评估因果关系提供了理论依据，但受限于部分可识别性和潜在混杂因素，面临计算复杂性挑战。本文提出算法简化方案，显著降低计算因果概率紧界所需的时间成本；并构建一种新型根因分析方法论框架，系统性地利用这些因果度量对完整因果路径进行排序，从而实现对根本原因的精准定位。
原文摘要 · Abstract (English)
Probabilities of causation provide principled ways to assess causal relationships but face computational challenges due to partial identifiability and latent confounding. This paper introduces both algorithmic simplifications, significantly reducing the computational complexity of calculating tighter bounds for these probabilities, and a novel methodological framework for Root Cause Analysis that systematically employs these causal metrics to rank entire causal paths.
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