This paper presents an approach for identifying the root causes of collective anomalies given observational time series and an acyclic summary causal graph which depicts an abstraction of causal relations present in a dynamic system at its normal regime. The paper first shows how the problem of root cause identification can be divided into many independent subproblems by grouping related anomalies using d-separation. Further, it shows how, under this setting, some root causes can be found directly from the graph and from the time of appearance of anomalies. Finally, it shows, how the rest of the root causes can be found by comparing direct causal effects in the normal and in the anomalous regime. To this end, temporal adaptations of the back-door and the single-door criterions are introduced. Extensive experiments conducted on both simulated and real-world datasets demonstrate the effectiveness of the proposed method.

翻译：本文提出了一种方法，用于在给定观测时间序列和描述动态系统正常状态下因果关系的无环总结因果图时，识别集体异常的根因。文章首先展示了如何通过d-分离原则将相关异常分组，从而将根因识别问题分解为多个独立的子问题。进一步，它说明了在此设定下，部分根因可直接从图结构和异常出现时间中推断出来。最后，文章展示了如何通过比较正常与异常状态下的直接因果效应来识别剩余根因。为此，引入了后门准则和单门准则的时间适应性扩展。在模拟数据集和真实数据集上进行的大量实验证明了所提方法的有效性。