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

2026-08-21

AI Summary

本文通过差异图发现方法，从线性时间序列数据中识别导致异常的根本原因，特别是在正常与异常状态之间因果系数变化的变量。

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.

Problem

• root cause analysis
• linear time-series
• difference graph discovery
• anomalous behavior

Innovation

• difference graph discovery
• linear time-series data
• root cause analysis
• effect-defying root causes
• discrete-time dynamic structural causal models