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Due to a wide spectrum of applications in the real world, such as security,financial surveillance, and health risk, various deep anomaly detection modelshave been proposed and achieved state-of-the-art performance. However, besidesbeing effective, in practice, the practitioners would further like to know whatcauses the abnormal outcome and how to further fix it. In this work, we proposeRootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigationfrom a causal perspective. Especially, we formulate anomalies caused byexternal interventions on the normal causal mechanism and aim to locate theabnormal features with external interventions as root causes. After that, wefurther propose an anomaly mitigation approach that aims to recommendmitigation actions on abnormal features to revert the abnormal outcomes suchthat the counterfactuals guided by the causal mechanism are normal. Experimentson three datasets show that our approach can locate the root causes and furtherflip the abnormal labels.

On Root Cause Localization and Anomaly Mitigation through Causal Inference