On Root Cause Localization and Anomaly Mitigation through Causal Inference
On Root Cause Localization and Anomaly Mitigation through Causal Inference
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DOI：
10.1145/3583780.3614995
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Xiao Han;Lu Zhang;Yongkai Wu;Shuhan Yuan
中科院分区：
文献类型：
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作者：
Xiao Han;Lu Zhang;Yongkai Wu;Shuhan Yuan
由于在真实的世界中的广泛应用，例如安全、金融监控和健康风险，已经提出了各种深度异常检测模型，并且实现了最先进的性能。然而，除了有效之外，在实践中，从业者还想知道导致异常结果的原因以及如何进一步修复它。在这项工作中，我们提出了RootCLAM，旨在从因果角度实现根本原因定位和异常缓解。特别是，我们制定了正常的因果机制上的外部干预所造成的异常，旨在定位与外部干预的根本原因的异常功能。之后，我们进一步提出了一个异常缓解方法，旨在建议缓解行动的异常功能，以恢复异常的结果，使反事实的因果机制是正常的。在三个数据集上的实验表明，该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.