mlsanityData to 7 Oct 2026

Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis

arXiv 2411.06990 ↗First posted 11 Nov 2024stat.ML cs.LGcited by 3

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Abstract (arXiv)

Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain "black boxes", making prediction error diagnosis challenging, especially with outliers. This lack of transparency hinders trust and reliability in industrial applications. Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. Various root-cause analysis methods have been developed using Shapley values, yet they typically require predefined causal graphs, limiting their applicability for prediction errors in machine learning models. To address these limitations, we introduce the Causal-Discovery-based Root-Cause Analysis (CD-RCA) method that estimates causal relationships between the prediction error and the explanatory variables, without needing a pre-defined causal graph. By simulating synthetic error data, CD-RCA can identify variable contributions to outliers in prediction errors by Shapley values. Extensive experiments show CD-RCA outperforms current heuristic attribution methods.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 44 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

19 of 44 references.

References · 19

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  6. 2020Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Modelscited by 107
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  8. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 82
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  10. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40
  11. 2021You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Predictioncited by 23
  12. 2020Predictive and Causal Implications of using Shapley Value for Model Interpretationcited by 16
7 more
  1. 2013Graded Causation and Defaultscited by 13
  2. 2020A Unified Taylor Framework for Revisiting Attribution Methodscited by 13
  3. 2020Quantifying intrinsic causal contributions via structure preserving interventionscited by 12
  4. 2023Bootstrap aggregation and confidence measures to improve time series causal discoverycited by 8
  5. 2022Anomaly Attribution with Likelihood Compensationcited by 7
  6. 2015Optimal model-free prediction from multivariate time seriescited by 6
  7. 2023Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attributioncited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecuritycited by 6
  2. 2025Causal Additive Models with Unobserved Causal Paths and Backdoor Pathscited by 5
  3. 2026Causal Discovery via Transformed Low-Rank Quantile Surfacescited by 0