用因果推理追踪异常源头，精准定位复杂系统故障根因。
ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios
- 基于因果推断构建路径追踪框架，融合异常评分与归因分析。
- 在模拟异常中准确识别触发点，按重要性排序根因。
- 适合需要可解释性故障诊断的工业级系统运维人员。
随着现代系统复杂度、数据量和依赖关系的增加，根本原因分析（RCA）变得日益关键。传统基于相关性或规则的方法在高度动态、多层环境中往往失效。本文提出一个基于DoWhy因果推断库的路径追踪工具包ProRCA，整合条件异常评分、基于噪声的归因分析和深度优先路径探索，以揭示多跳因果链。通过系统追踪从观测异常回溯到初始触发源的完整因果路径，该方法提供端到端的RCA解决方案。在合成异常注入实验中，该工具包能准确识别触发因素，并按整体重要性对根因进行排序。
原文摘要 · Abstract (English)
Root Cause Analysis (RCA) is becoming ever more critical as modern systems grow in complexity, volume of data, and interdependencies. While traditional RCA methods frequently rely on correlation-based or rule-based techniques, these approaches can prove inadequate in highly dynamic, multi-layered environments. In this paper, we present a pathway-tracing package built on the DoWhy causal inference library. Our method integrates conditional anomaly scoring, noise-based attribution, and depth-first path exploration to reveal multi-hop causal chains. By systematically tracing entire causal pathways from an observed anomaly back to the initial triggers, our approach provides a comprehensive, end-to-end RCA solution. Experimental evaluations with synthetic anomaly injections demonstrate the package's ability to accurately isolate triggers and rank root causes by their overall significance.
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