mlsanityData to 14 Oct 2026

Case Studies of Causal Discovery from IT Monitoring Time Series

arXiv 2307.15678 ↗First posted 28 Jul 2023cs.LG cs.AI stat.APcited by 14

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Abstract (arXiv)

Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their proper functioning and efficiency, as it allows collecting extensive observational time series data for analysis. The interest in causal discovery is growing in IT monitoring systems as knowing causal relations between different components of the IT system helps in reducing downtime, enhancing system performance and identifying root causes of anomalies and incidents. It also allows proactive prediction of future issues through historical data analysis. Despite its potential benefits, applying causal discovery algorithms on IT monitoring data poses challenges, due to the complexity of the data. For instance, IT monitoring data often contains misaligned time series, sleeping time series, timestamp errors and missing values. This paper presents case studies on applying causal discovery algorithms to different IT monitoring datasets, highlighting benefits and ongoing challenges.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 12 of its 39 references that are in the map. First posted on arXiv 28 Jul 2023.

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12 of 39 references.

References · 12

  1. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  2. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
  3. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  4. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
  5. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
  6. 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 77
  7. 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 56
  8. 2018Causal Discovery in the Presence of Missing Datacited by 47
  9. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  10. 2020Reconstructing regime-dependent causal relationships from observational time seriescited by 28
  11. 2021Causal Discovery from Conditionally Stationary Time Seriescited by 12
  12. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 4.

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  1. 2023Identifiability of total effects from abstractions of time series causal graphscited by 14
  2. 2024Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insightcited by 12
  3. 2024Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterioncited by 5
  4. 2023Causal Temporal Regime Structure Learningcited by 2
  5. 2024On the Fly Detection of Root Causes from Observed Data with Application to IT Systemscited by 2
  6. 2024Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphscited by 1
  7. 2025CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledgecited by 1
  8. 2024Scalable Time-Series Causal Discovery with Approximate Causal Orderingcited by 0
  9. 2025Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effectscited by 0
  10. 2025Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributionscited by 0
  11. 2026DMCD: Semantic-Statistical Framework for Causal Discoverycited by 0
  12. 2026Learning Temporal Causal Structure via Smooth Differentiable Optimizationcited by 0
2 more
  1. 2026EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysiscited by 0
  2. 2026Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blanketscited by 0