论文标题
在根本原因上定位和通过因果推理缓解异常
On Root Cause Localization and Anomaly Mitigation through Causal Inference
论文作者
论文摘要
由于在现实世界中的广泛应用，例如安全性，金融监视和健康风险，因此已经提出并实现了各种深度异常检测模型。但是，除了有效，实际上，从业者还想进一步知道是什么原因导致异常结果以及如何进一步解决它。在这项工作中，我们提出了rootclam，旨在从因果的角度实现根本原因的定位和缓解异常。特别是，我们制定了由正常因果机制外部干预引起的异常，并旨在将异常特征定位为外部干预措施作为根本原因。之后，我们进一步提出了一种缓解异常方法，旨在建议对异常特征进行缓解行动，以恢复异常结果，以使以因果关系机制为指导的反事实是正常的。三个数据集的实验表明，我们的方法可以定位根本原因并进一步翻转异常标签。
Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.
