On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
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Abstract (arXiv)
This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems. When root causes are not causally related, the method is proven to be correct; while an extension is proposed based on the intervention of an agent to relax this assumption. Our algorithm and its agent-based extension leverage causal discovery from offline data and engage in subgraph traversal when encountering new anomalies in online data. Our extensive experiments demonstrate the superior performance of our methods, even when applied to data generated from alternative structural causal models or real IT monitoring data.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 9 Feb 2024.
Builds on
6 of 25 references.
References · 6
- 2017Detecting causal associations in large nonlinear time series datasetscited by 240
- 2019Causal structure based root cause analysis of outlierscited by 65
- 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 55
- 2021Why did the distribution change?cited by 37
- 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
- 2023Case Studies of Causal Discovery from IT Monitoring Time Seriescited by 14
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Citing papers per year: 2026: 2.
2 papers.