This paper introduces a new structural causal model tailored for representing
threshold-based IT systems and presents a new algorithm designed to rapidly
detect root causes of anomalies in such systems. When root causes are not
causally related, the method is proven to be correct; while an extension is
proposed based on the intervention of an agent to relax this assumption. Our
algorithm and its agent-based extension leverage causal discovery from offline
data and engage in subgraph traversal when encountering new anomalies in online
data. Our extensive experiments demonstrate the superior performance of our
methods, even when applied to data generated from alternative structural causal
models or real IT monitoring data.

引入了一种新的针对阈值性 IT 系统的结构因果模型，并提出了一种新的算法，用于快速检测此类系统中异常的根本原因，方法基于离线数据进行因果性发现，通过代理的干预提出了扩展来放宽根因非因果相关的假设，对于在线数据中的新异常，则利用子图遍历，实验证明了该方法的卓越性能。