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This paper presents an approach for identifying the root causes of collectiveanomalies given observational time series and an acyclic summary causal graphwhich depicts an abstraction of causal relations present in a dynamic system atits normal regime. The paper first shows how the problem of root causeidentification can be divided into many independent subproblems by groupingrelated anomalies using d-separation. Further, it shows how, under thissetting, some root causes can be found directly from the graph and from thetime of appearance of anomalies. Finally, it shows, how the rest of the rootcauses can be found by comparing direct effects in the normal and in theanomalous regime. To this end, an adjustment set for identifying direct effectsis introduced. Extensive experiments conducted on both simulated and real-worlddatasets demonstrate the effectiveness of the proposed method.

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