不依赖图结构，定位复杂循环系统中的异常根源。
Root Cause Analysis of Outliers in Unknown Cyclic Graphs
- 基于线性因果模型反向追踪异常传播路径，定位潜在根源节点。
- 强扰动下可准确识别真实根源及关联父节点组成的短列表。
- 无需先验图结构知识，适用于生物与云计算等真实场景。
我们研究了在具有线性结构方程的循环因果图中异常的传播，旨在追溯其至一个或多个“根源”节点。研究表明，在扰动足够强且遵循正常模式下相同结构方程的前提下，可识别出一个简短的潜在根源候选列表。该列表包含真实根源及其位于与根源构成环路的父节点。值得注意的是，该方法无需事先知晓因果图结构，并在模拟数据以及生物和云计算领域的实际数据上取得了令人鼓舞的结果。
原文摘要 · Abstract (English)
We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our method does not require prior knowledge of the causal graph and yields encouraging results on simulated data and real data from biology and cloud computing.
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