用反事实模拟分析网络故障根源，提升运维决策准确性。
NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks
- 基于图时序建模与反事实推理，自动推断故障传播路径。
- 在31个真实案例中比规则基线提升16.1%定位准确率。
- 适合云服务商运维团队快速诊断复杂网络故障。
现有根因分析方法多依赖静态规则、相关性启发或拓扑局部推理，在动态环境中难以泛化。本文提出NetCause，一种自监督学习框架，将网络事件建模为图-时序过程，通过反事实模拟对候选根因进行可解释排序，并可自然集成运维操作。模型在某头部云厂商生产网络中超过1500个故障事件上训练，评估在31个专家标注事件上表现优异。在最贴近实际运维决策的场景中，根因排名质量显著优于规则基线，准确率提升16.1%。尽管训练耗时，但推理仅需数秒GPU时间（远低于典型遥测采集延迟），具备实用部署潜力。
原文摘要 · Abstract (English)
Can a learned model capture how faults propagate through a large-scale network and use this knowledge to causally attribute customer impact to its underlying root cause? Existing root cause analysis techniques often rely on static rules, correlation heuristics, or topology-local reasoning, which struggle to generalize in dynamic environments where faults propagate across complex physical and logical dependencies. We present NetCause, a self-supervised learning-based framework that models network incidents as graph-temporal processes and uses counterfactual simulation to rank candidate root causes. This approach produces an interpretable ranking of root cause hypotheses and integrates naturally with operator-defined mitigation and remediation actions. We train the model on over 1,500 incidents collected over six months from a leading cloud provider's production network and evaluate it on 31 expert-labeled incidents. NetCause consistently improves root cause ranking quality in the regime most relevant to operational decision-making, achieving a 16.1% accuracy improvement over a rule-based heuristic baseline. While training is computationally intensive, inference is lightweight, requiring only seconds of GPU runtime per incident (well below typical telemetry collection latencies).
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