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

Abstract:Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis in linear time-series through the lens of difference graph discovery. We focus on effect-defying root causes, corresponding to variables whose causal coefficients change between a normal and an anomalous regime. We formalize this problem using linear discrete-time dynamic structural causal models and adapt several methods originally introduced for discovering difference graphs between two populations to the time-series setting, where the two populations are replaced by a normal and an anomalous regime. We first evaluate the proposed approaches on simulated data, and then demonstrate their practical relevance on real-world datasets from IT monitoring and intensive care monitoring. Our results show how difference graph discovery can help localize causal mechanisms responsible for anomalous behavior.

Artificial Intelligence (cs.AI)

arXiv:2608.21117 [cs.AI]

(or arXiv:2608.21117v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.21117 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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