  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.
