mlsanityData to 7 Oct 2026

FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles

arXiv 1911.12199 ↗First posted 27 Nov 2019cs.LG cs.AIcited by 43

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Abstract (arXiv)

Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans. Counterfactual explanations can help users understand not only why ML models make certain decisions, but also how these decisions can be changed. We frame the problem of finding counterfactual explanations as a gradient-based optimization task and extend previous work that could only be applied to differentiable models. In order to accommodate non-differentiable models such as tree ensembles, we use probabilistic model approximations in the optimization framework. We introduce an approximation technique that is effective for finding counterfactual explanations for predictions of the original model and show that our counterfactual examples are significantly closer to the original instances than those produced by other methods specifically designed for tree ensembles.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Nov 2019.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 6; 2024: 5; 2025: 9; 2026: 7.

43 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  2. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 118
  3. 2022Robust Counterfactual Explanations for Tree-Based Ensemblescited by 49
  4. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
  5. 2021Optimal Counterfactual Explanations in Tree Ensemblescited by 36
  6. 2023GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanationscited by 27
  7. 2021ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Modelscited by 25
  8. 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
  9. 2022GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendationscited by 22
  10. 2021A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Datacited by 19
  11. 2022Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanationscited by 11
  12. 2022Global Counterfactual Explanations: Investigations, Implementations and Improvementscited by 9
31 more
  1. 2022Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactionscited by 7
  2. 2023Explainable Data-Driven Optimization: From Context to Decision and Back Againcited by 7
  3. 2024Learning Decision Trees and Forests with Algorithmic Recoursecited by 7
  4. 2024Sparse and Faithful Explanations Without Sparse Modelscited by 5
  5. 2023Very fast, approximate counterfactual explanations for decision forestscited by 4
  6. 2024Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processescited by 4
  7. 2021An exact counterfactual-example-based approach to tree-ensemble models interpretabilitycited by 3
  8. 2024Debiasing Machine Unlearning with Counterfactual Examplescited by 3
  9. 2025FUTURE: Flexible Unlearning for Tree Ensemblecited by 3
  10. 2022Don't Explain Noise: Robust Counterfactuals for Randomized Ensemblescited by 2
  11. 2026TRACER: Token ReAssignment for Concept ERasure in Generative Recommendationcited by 2
  12. 2023Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposalcited by 1
  13. 2024Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamicscited by 1
  14. 2026Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanationscited by 1
  15. 2026Counterfactual Maps: What They Are and How to Find Themcited by 1
  16. 2022Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Userscited by 0
  17. 2022Local and Regional Counterfactual Rules: Summarized and Robust Recoursescited by 0
  18. 2022Decomposing Counterfactual Explanations for Consequential Decision Makingcited by 0
  19. 2023CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisionscited by 0
  20. 2025Countering Overfitting with Counterfactual Examplescited by 0
  21. 2025Analysis of Customer Journeys Using Prototype Detection and Counterfactual Explanations for Sequential Datacited by 0
  22. 2025Generalizability vs. Counterfactual Explainability Trade-Offcited by 0
  23. 2025An Explainable Gaussian Process Auto-encoder for Tabular Datacited by 0
  24. 2025Explaining Fine Tuned LLMs via Counterfactuals A Knowledge Graph Driven Frameworkcited by 0
  25. 2025From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environmentscited by 0
  26. 2025CONFEX: Uncertainty-Aware Counterfactual Explanations with Conformal Guaranteescited by 0
  27. 2025Interpretable Model-Aware Counterfactual Explanations for Random Forestcited by 0
  28. 2026Counterfactual Explanations for Hypergraph Neural Networkscited by 0
  29. 2026Optimal Counterfactual Search in Tree Ensembles: A Study Across Modeling and Solution Paradigmscited by 0
  30. 2026Target-confidence Recourse Using tSeTlin machines: TRUSTcited by 0
  31. 2026Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensemblescited by 0