mlsanityData to 14 Oct 2026

Accurate Shapley Values for explaining tree-based models

arXiv 2106.03820 ↗First posted 7 Jun 2021stat.ML cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

Shapley Values (SV) are widely used in explainable AI, but their estimation and interpretation can be challenging, leading to inaccurate inferences and explanations. As a starting point, we remind an invariance principle for SV and derive the correct approach for computing the SV of categorical variables that are particularly sensitive to the encoding used. In the case of tree-based models, we introduce two estimators of Shapley Values that exploit the tree structure efficiently and are more accurate than state-of-the-art methods. Simulations and comparisons are performed with state-of-the-art algorithms and show the practical gain of our approach. Finally, we discuss the limitations of Shapley Values as a local explanation. These methods are available as a Python package.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

14 of 31 references.

References · 14

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018On the Robustness of Interpretability Methodscited by 314
  4. 2020Explaining by Removing: A Unified Framework for Model Explanationcited by 218
  5. 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 208
  6. 2019Feature relevance quantification in explainable AI: A causal problemcited by 167
  7. 2020Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regressioncited by 154
  8. 2020True to the Model or True to the Data?cited by 126
  9. 2020Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Modelscited by 107
  10. 2014Consistency of random forestscited by 85
  11. 2020Efficient nonparametric statistical inference on population feature importance using Shapley valuescited by 61
  12. 2020Interpretable Random Forests via Rule Extractioncited by 23
2 more
  1. 2020Understanding Global Feature Contributions With Additive Importance Measurescited by 10
  2. 2021Explaining predictive models using Shapley values and non-parametric vine copulascited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 2; 2026: 1.

9 papers.

  1. 2024Provably Accurate Shapley Value Estimation via Leverage Score Samplingcited by 23
  2. 2021Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification modelscited by 4
  3. 2023On marginal feature attributions of tree-based modelscited by 3
  4. 2025Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Modelscited by 3
  5. 2023Interpretable Ensembles of Hyper-Rectangles as Base Modelscited by 2
  6. 2023Self-Reinforcement Attention Mechanism For Tabular Learningcited by 1
  7. 2025Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discoverycited by 1
  8. 2026Regional Explanations: Bridging Local and Global Variable Importancecited by 1
  9. 2022Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmarkcited by 0