# Accurate Shapley Values for explaining tree-based models

arXiv: https://arxiv.org/abs/2106.03820 · HTML: https://mlsanity.com/paper/2106.03820/

## 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 (most cited first, 14 of 14)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [On the Robustness of Interpretability Methods](https://mlsanity.com/paper/1806.08049/) (2018)
- [Explaining by Removing: A Unified Framework for Model Explanation](https://mlsanity.com/paper/2011.14878/) (2020)
- [Explaining individual predictions when features are dependent: More accurate approximations to Shapley values](https://mlsanity.com/paper/1903.10464/) (2019)
- [Feature relevance quantification in explainable AI: A causal problem](https://mlsanity.com/paper/1910.13413/) (2019)
- [Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression](https://mlsanity.com/paper/2012.01536/) (2020)
- [True to the Model or True to the Data?](https://mlsanity.com/paper/2006.16234/) (2020)
- [Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models](https://mlsanity.com/paper/2011.01625/) (2020)
- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Efficient nonparametric statistical inference on population feature importance using Shapley values](https://mlsanity.com/paper/2006.09481/) (2020)
- [Interpretable Random Forests via Rule Extraction](https://mlsanity.com/paper/2004.14841/) (2020)
- [Understanding Global Feature Contributions With Additive Importance Measures](https://mlsanity.com/paper/2004.00668/) (2020)
- [Explaining predictive models using Shapley values and non-parametric vine copulas](https://mlsanity.com/paper/2102.06416/) (2021)

## Built on by (most cited first, 9 of 9)

- [Provably Accurate Shapley Value Estimation via Leverage Score Sampling](https://mlsanity.com/paper/2410.01917/) (2024)
- [Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models](https://mlsanity.com/paper/2111.04658/) (2021)
- [On marginal feature attributions of tree-based models](https://mlsanity.com/paper/2302.08434/) (2023)
- [Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models](https://mlsanity.com/paper/2506.13900/) (2025)
- [Interpretable Ensembles of Hyper-Rectangles as Base Models](https://mlsanity.com/paper/2303.08625/) (2023)
- [Self-Reinforcement Attention Mechanism For Tabular Learning](https://mlsanity.com/paper/2305.11684/) (2023)
- [Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery](https://mlsanity.com/paper/2509.00846/) (2025)
- [Regional Explanations: Bridging Local and Global Variable Importance](https://mlsanity.com/paper/2604.11223/) (2026)
- [Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark](https://mlsanity.com/paper/2207.14160/) (2022)

## 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.