mlsanityData to 7 Oct 2026

Causality-based Explanation of Classification Outcomes

arXiv 2003.06868 ↗First posted 15 Mar 2020cs.LG cs.AI cs.DBcited by 25

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Abstract (arXiv)

We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality. We compare it with previously proposed notions of explanation, and study their complexity. We conduct an experimental evaluation with two real datasets from the financial domain.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2020.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 3; 2024: 5; 2025: 3; 2026: 1.

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  1. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  2. 2021On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Resultscited by 32
  3. 2021Interpretable Data-Based Explanations for Fairness Debuggingcited by 30
  4. 2024On the Tractability of SHAP Explanations under Markovian Distributionscited by 10
  5. 2021Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classificationcited by 9
  6. 2022XInsight: eXplainable Data Analysis Through The Lens of Causalitycited by 9
  7. 2025SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelismcited by 8
  8. 2024Causality from Bottom to Top: A Surveycited by 5
  9. 2021Seven challenges for harmonizing explainability requirementscited by 4
  10. 2022Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spacescited by 4
  11. 2023Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilationcited by 3
  12. 2024Automated Discovery of Functional Actual Causes in Complex Environmentscited by 3
13 more
  1. 2021Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysiscited by 2
  2. 2022Causal Explanations for Sequential Decision Making Under Uncertaintycited by 2
  3. 2023Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligencecited by 2
  4. 2023Attribution-Scores in Data Management and Explainable Machine Learningcited by 2
  5. 2024A Shapley Value Estimation Speedup for Efficient Explainable Quantum AIcited by 2
  6. 2024The Distributional Uncertainty of the SHAP score in Explainable Machine Learningcited by 1
  7. 2025The Causal-Effect Score in Data Managementcited by 1
  8. 2025Feature Relevancy, Necessity and Usefulness: Complexity and Algorithmscited by 1
  9. 2021Reasoning about Counterfactuals and Explanations: Problems, Results and Directionscited by 0
  10. 2022Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detectioncited by 0
  11. 2022ExMo: Explainable AI Model using Inverse Frequency Decision Rulescited by 0
  12. 2022Answer-Set Programs for Repair Updates and Counterfactual Interventionscited by 0
  13. 2026Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretabilitycited by 0