mlsanityData to 7 Oct 2026

Why did the distribution change?

arXiv 2102.13384 ↗First posted 26 Feb 2021stat.ME cs.AI cs.LGcited by 37

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Abstract (arXiv)

We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables. After factorizing the joint distribution into conditional distributions of each variable, given its parents (the "causal mechanisms"), we attribute the change to changes of these causal mechanisms. This attribution analysis accounts for the fact that mechanisms often change independently and sometimes only some of them change. Through simulations, we study the performance of our distribution change attribution method. We then present a real-world case study identifying the drivers of the difference in the income distribution between men and women.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 3 of its 38 references that are in the map. First posted on arXiv 26 Feb 2021.

Builds on

3 of 38 references.

References · 3

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  3. 2019Feature relevance quantification in explainable AI: A causal problemcited by 164

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 11; 2024: 12; 2025: 4; 2026: 2.

37 papers.

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  1. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 348
  2. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40
  3. 2022Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Modelcited by 38
  4. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  5. 2022Towards Explaining Distribution Shiftscited by 26
  6. 2024A Survey on Evaluation of Out-of-Distribution Generalizationcited by 20
  7. 2022"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shiftscited by 18
  8. 2023The PetShop Dataset -- Finding Causes of Performance Issues across Microservicescited by 14
  9. 2022Identifying Patient-Specific Root Causes of Diseasecited by 8
  10. 2023Counterfactual Formulation of Patient-Specific Root Causes of Diseasecited by 8
  11. 2022Sample-Specific Root Causal Inference with Latent Variablescited by 6
  12. 2023On the Interventional Kullback-Leibler Divergencecited by 5
25 more
  1. 2023Explanation Shift: How Did the Distribution Shift Impact the Model?cited by 5
  2. 2023Dataflow graphs as complete causal graphscited by 5
  3. 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Modelscited by 5
  4. 2024A hierarchical decomposition for explaining ML performance discrepanciescited by 5
  5. 2023The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Researchcited by 4
  6. 2024Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performancecited by 4
  7. 2024Identifying General Mechanism Shifts in Linear Causal Representationscited by 4
  8. 2022Explaining the root causes of unit-level changescited by 3
  9. 2023Root Cause Explanation of Outliers under Noisy Mechanismscited by 3
  10. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
  11. 2024On the Fly Detection of Root Causes from Observed Data with Application to IT Systemscited by 2
  12. 2024Counterfactual-based Root Cause Analysis for Dynamical Systemscited by 2
  13. 2024Causality-Driven Audits of Model Robustnesscited by 2
  14. 2025Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomenacited by 2
  15. 2023Causal fault localisation in dataflow systemscited by 1
  16. 2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 1
  17. 2026Order-based Causal Discovery for Multistage Processescited by 1
  18. 2022Causal Domain Adaptation with Copula Entropy based Conditional Independence Testcited by 0
  19. 2024Root Causing Prediction Anomalies Using Explainable AIcited by 0
  20. 2024Spurious Correlations in Concept Drift: Can Explanatory Interaction Help?cited by 0
  21. 2024Control+Shift: Generating Controllable Distribution Shiftscited by 0
  22. 2024Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Datacited by 0
  23. 2025"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Driftcited by 0
  24. 2025Causal Attribution of Model Performance Gaps in Medical Imaging Under Distribution Shiftscited by 0
  25. 2026PRIM: Meta-Learned Bayesian Root Cause Analysiscited by 0