mlsanityData to 7 Oct 2026

Root Cause Analysis of Outliers with Missing Structural Knowledge

arXiv 2406.05014 ↗First posted 7 Jun 2024stat.ML cs.LGcited by 19

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Abstract (arXiv)

The goal of Root Cause Analysis (RCA) is to explain why an anomaly occurred by identifying where the fault originated. Several recent works model the anomalous event as resulting from a change in the causal mechanism at the root cause, i.e., as a soft intervention. RCA is then the task of identifying which causal mechanism changed. In real-world applications, one often has either few or only a single sample from the post-intervention distribution: a severe limitation for most methods, which assume one knows or can estimate the distribution. However, even those that do not are statistically ill-posed due to the need to probe regression models in regions of low probability density. In this paper, we propose simple, efficient methods to overcome both difficulties in the case where there is a single root cause and the causal graph is a polytree. When one knows the causal graph, we give guarantees for a traversal algorithm that requires only marginal anomaly scores and does not depend on specifying an arbitrary anomaly score cut-off. When one does not know the causal graph, we show that the heuristic of identifying root causes as the variables with the highest marginal anomaly scores is causally justified. To this end, we prove that anomalies with small scores are unlikely to cause those with larger scores in polytrees and give upper bounds for the likelihood of causal pathways with non-monotonic anomaly scores.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 26 of its 69 references that are in the map. First posted on arXiv 7 Jun 2024.

Builds on

26 of 69 references.

References · 26

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  3. 2020A review on outlier/anomaly detection in time series datacited by 187
  4. 2019A Meta-Transfer Objective for Learning to Disentangle Causal Mechanismscited by 158
  5. 2020Algorithmic Fairnesscited by 143
  6. 2021Testing for Outliers with Conformal p-valuescited by 133
  7. 2013Counterfactual Probabilities: Computational Methods, Bounds and Applicationscited by 67
  8. 2019Causal structure based root cause analysis of outlierscited by 65
  9. 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 55
  10. 2013Causal Discovery from Changescited by 52
  11. 2022Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Modelcited by 38
  12. 2021MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systemscited by 36
14 more
  1. 2024Root Cause Analysis In Microservice Using Neural Granger Causal Discoverycited by 31
  2. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  3. 2017Contextual Outlier Interpretationcited by 19
  4. 2019Causal discovery in heavy-tailed modelscited by 18
  5. 2023Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localizationcited by 18
  6. 2022Backtracking Counterfactualscited by 15
  7. 2023The PetShop Dataset -- Finding Causes of Performance Issues across Microservicescited by 14
  8. 2017Explaining Anomalies in Groups with Characterizing Subspace Rulescited by 13
  9. 2020Quantifying intrinsic causal contributions via structure preserving interventionscited by 12
  10. 2023Toward Falsifying Causal Graphs Using a Permutation-Based Testcited by 11
  11. 2021Structure Learning for Directed Treescited by 9
  12. 2023Counterfactual Formulation of Patient-Specific Root Causes of Diseasecited by 8
  13. 2022Learning Linear Non-Gaussian Polytree Modelscited by 7
  14. 2023Learning bounded-degree polytrees with known skeletoncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 15.

19 papers.

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  1. 2025Robust Root Cause Diagnosis using In-Distribution Interventionscited by 4
  2. 2025Root Cause Analysis of Outliers in Unknown Cyclic Graphscited by 4
  3. 2025Toward Universal Laws of Outlier Propagationcited by 3
  4. 2025ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarioscited by 3
  5. 2026Graph-Free Root Cause Analysiscited by 3
  6. 2026Score-based Integrated Gradient for Root Cause Explanations of Outlierscited by 0
  7. 2026Root Cause Analysis of Measurement and Mechanistic Anomaliescited by 0
  8. 2026Causal explanations of outliers in systems with lagged time-dependenciescited by 0
  9. 2026PRIM: Meta-Learned Bayesian Root Cause Analysiscited by 0
  10. 2026ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysiscited by 0
  11. 2026Formalizing and falsifying causal pathways of rare eventscited by 0
  12. 2026StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysiscited by 0
7 more
  1. 2026Graphical Causal Reasoning for Root Cause Analysis in Cloud Networkscited by 0
  2. 2026NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networkscited by 0
  3. 2026Root Cause Analysis with Latent Confounders using Partial Ancestral Graphscited by 0
  4. 2026CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistencycited by 0
  5. 2026Falsifying Causal Graphs With Outlier Eventscited by 0
  6. 2026Root cause analysis via difference graph discovery from linear time-series datacited by 0
  7. 2026Beyond Conditional Independence: Root Cause Analysis with Deep Causal Modelscited by 0