Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops

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 effects in the normal and in the anomalous regime. To this end, an adjustment set for identifying direct effects is introduced. Extensive experiments conducted on both simulated and real-world datasets demonstrate the effectiveness of the proposed method.Comment: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023, Valencia, Spai