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Falling Rule Lists

arXiv 1411.5899 ↗First posted 21 Nov 2014cs.AI cs.LGcited by 58

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Abstract (arXiv)

Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2014.

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Citing papers per year: 2021: 16; 2022: 17; 2023: 10; 2024: 6; 2025: 7; 2026: 2.

58 papers.

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  1. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  2. 2022OpenXAI: Towards a Transparent Evaluation of Model Explanationscited by 125
  3. 2022From Shapley Values to Generalized Additive Models and backcited by 59
  4. 2021On Guaranteed Optimal Robust Explanations for NLP Modelscited by 54
  5. 2022Logic-Based Explainability in Machine Learningcited by 47
  6. 2021SAT-Based Rigorous Explanations for Decision Listscited by 40
  7. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  8. 2023Learning Transformer Programscited by 40
  9. 2022First is Better Than Last for Language Data Influencecited by 34
  10. 2022Computing Abductive Explanations for Boosted Treescited by 23
  11. 2022Concept Gradient: Concept-based Interpretation Without Linear Assumptioncited by 20
  12. 2023Concept Learning for Interpretable Multi-Agent Reinforcement Learningcited by 18
38 more
  1. 2021How to Find a Good Explanation for Clustering?cited by 16
  2. 2021Neurosymbolic Transformers for Multi-Agent Communicationcited by 13
  3. 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligencecited by 13
  4. 2022Computing the Collection of Good Models for Rule Listscited by 12
  5. 2023FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysiscited by 12
  6. 2021Interpretable Machine Learning: Moving From Mythos to Diagnosticscited by 9
  7. 2021Model Learning with Personalized Interpretability Estimation (ML-PIE)cited by 9
  8. 2022A Human-Centric Take on Model Monitoringcited by 9
  9. 2025Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisionscited by 9
  10. 2024Explainable Bayesian Optimizationcited by 8
  11. 2022A Sea of Words: An In-Depth Analysis of Anchors for Text Datacited by 6
  12. 2023Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objectivecited by 6
  13. 2024Understanding Disparities in Post Hoc Machine Learning Explanationcited by 6
  14. 2021An Exploration And Validation of Visual Factors in Understanding Classification Rule Setscited by 5
  15. 2022Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Setscited by 5
  16. 2022Efficient Learning of Interpretable Classification Rulescited by 5
  17. 2023Efficient learning of large sets of locally optimal classification rulescited by 5
  18. 2021Fair Decision Rules for Binary Classificationcited by 4
  19. 2024Trust Regions for Explanations via Black-Box Probabilistic Certificationcited by 4
  20. 2022A Generic Approach for Reproducible Model Distillationcited by 3
  21. 2025Less or More: Towards Glanceable Explanations for LLM Recommendations Using Ultra-Small Devicescited by 3
  22. 2022Transcending XAI Algorithm Boundaries through End-User-Inspired Designcited by 2
  23. 2023Explainable AI using expressive Boolean formulascited by 2
  24. 2023Faithful and Robust Local Interpretability for Textual Predictionscited by 2
  25. 2021Assessment of the influence of features on a classification problem: an application to COVID-19 patientscited by 1
  26. 2021Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systemscited by 1
  27. 2021Case-based off-policy policy evaluation using prototype learningcited by 1
  28. 2022Harnessing Interpretable Machine Learning for Holistic Inverse Design of Origamicited by 1
  29. 2023Understanding Post-hoc Explainers: The Case of Anchorscited by 1
  30. 2024XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensicscited by 1
  31. 2024Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approachcited by 1
  32. 2025CFIRE: A General Method for Combining Local Explanationscited by 1
  33. 2021Visualizing Rule Sets: Exploration and Validation of a Design Spacecited by 0
  34. 2021Information-theoretic Evolution of Model Agnostic Global Explanationscited by 0
  35. 2021Interpretable Machine Learning Classifiers for Brain Tumour Survival Predictioncited by 0
  36. 2022ExMo: Explainable AI Model using Inverse Frequency Decision Rulescited by 0
  37. 2022TE2Rules: Explaining Tree Ensembles using Rulescited by 0
  38. 2023Learning Locally Interpretable Rule Ensemblecited by 0

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