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arXiv AI By Anouk Ruer, Timoth\'ee Loranchet, Daria Bystrova, Charles K. Assaad

Root cause analysis via difference graph discovery from linear time-series data

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The paper investigates root cause analysis for anomalies in linear time-series data by using difference graph discovery. It focuses on effect-defying root causes—variables whose causal coefficients differ between normal and anomalous regimes—within linear discrete-time dynamic structural causal models. The authors adapt existing difference graph methods to the time-series context, evaluate them on simulated data, and apply them to real-world IT and intensive care monitoring datasets to localize causal mechanisms behind anomalous behavior.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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