mlsanityData to 7 Oct 2026

Causal explanations of outliers in systems with lagged time-dependencies

arXiv 2602.04667 ↗First posted 4 Feb 2026stat.ML cs.LGcited by 0

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Abstract (arXiv)

Root-cause analysis in controlled time dependent systems poses a major challenge in applications. Especially energy systems are difficult to handle as they exhibit instantaneous as well as delayed effects and if equipped with storage, do have a memory. In this paper we adapt the causal root-cause analysis method of Budhathoki et al. [2022] to general time-dependent systems, as it can be regarded as a strictly causal definition of the term "root-cause". Particularly, we discuss two truncation approaches to handle the infinite dependency graphs present in time-dependent systems. While one leaves the causal mechanisms intact, the other approximates the mechanisms at the start nodes. The effectiveness of the different approaches is benchmarked using a challenging data generation process inspired by a problem in factory energy management: the avoidance of peaks in the power consumption. We show that given enough lags our extension is able to localize the root-causes in the feature and time domain. Further the effect of mechanism approximation is discussed.

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References · 10

  1. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  2. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 92
  3. 2019Causal structure based root cause analysis of outlierscited by 65
  4. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40
  5. 2021MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systemscited by 36
  6. 2024Root Cause Analysis In Microservice Using Neural Granger Causal Discoverycited by 31
  7. 2024Root Cause Analysis of Outliers with Missing Structural Knowledgecited by 19
  8. 2023Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localizationcited by 18
  9. 2023Counterfactual Formulation of Patient-Specific Root Causes of Diseasecited by 8
  10. 2025ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarioscited by 3

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