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

• URL: http://arxiv.org/abs/2608.21117v1
• Date: Fri, 21 Aug 2026 13:59:39 GMT
• Title: Root cause analysis via difference graph discovery from linear time-series data
• Abstract summary: We study root cause analysis in linear time-series through the lens of difference graph discovery.<n>We formalize this problem using linear discrete-time dynamic structural causal models.<n>We show how difference graph discovery can help localize causal mechanisms responsible for anomalous behavior.
• Score: 4.479834103607383
• License: http://creativecommons.org/licenses/by/4.0/
• 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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