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arXiv · 2608.21117

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

Anouk Ruer·Timothée Loranchet·Daria Bystrova·Charles K. Assaad
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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.

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Anouk Ruer, Timothée Loranchet, Daria Bystrova, Charles K. Assaad. 2026-08-21. Root cause analysis via difference graph discovery from linear time-series data. https://arxiv.org/abs/2608.21117

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