mlsanityData to 14 Oct 2026

SHAFF: Fast and consistent SHApley eFfect estimates via random Forests

arXiv 2105.11724 ↗First posted 25 May 2021stat.ML cs.LGcited by 22

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Abstract (arXiv)

Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects are now widely used to interpret both tree ensembles and neural networks, as they can efficiently handle dependence and interactions in the data, as opposed to most other variable importance measures. However, estimating Shapley effects is a challenging task, because of the computational complexity and the conditional expectation estimates. Accordingly, existing Shapley algorithms have flaws: a costly running time, or a bias when input variables are dependent. Therefore, we introduce SHAFF, SHApley eFfects via random Forests, a fast and accurate Shapley effect estimate, even when input variables are dependent. We show SHAFF efficiency through both a theoretical analysis of its consistency, and the practical performance improvements over competitors with extensive experiments. An implementation of SHAFF in C++ and R is available online.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 25 May 2021.

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17 of 47 references.

References · 17

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  3. 2019The many Shapley values for model explanationcited by 344
  4. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 285
  5. 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 208
  6. 2015ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and Rcited by 172
  7. 2019Feature relevance quantification in explainable AI: A causal problemcited by 167
  8. 2020Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regressioncited by 154
  9. 2019Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainabilitycited by 114
  10. 2020Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Modelscited by 107
  11. 2014Consistency of random forestscited by 85
  12. 2020Efficient nonparametric statistical inference on population feature importance using Shapley valuescited by 61
5 more
  1. 2014Understanding Random Forests: From Theory to Practicecited by 57
  2. 2020Shapley Flow: A Graph-based Approach to Interpreting Model Predictionscited by 51
  3. 2017Iterative Random Forests to detect predictive and stable high-order interactionscited by 42
  4. 2020Understanding Global Feature Contributions With Additive Importance Measurescited by 10
  5. 2021MDA for random forests: inconsistency, and a practical solution via the Sobol-MDAcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 7; 2023: 4; 2024: 2; 2025: 2; 2026: 3.

22 papers.

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  1. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  2. 2022Algorithms to estimate Shapley value feature attributionscited by 115
  3. 2022WeightedSHAP: analyzing and improving Shapley based feature attributionscited by 36
  4. 2022Variance reduced Shapley value estimation for trustworthy data valuationcited by 16
  5. 2023A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Themcited by 15
  6. 2021Decorrelated Variable Importancecited by 13
  7. 2022Ultra-marginal Feature Importance: Learning from Data with Causal Guaranteescited by 6
  8. 2025Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Explorationcited by 6
  9. 2021Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification modelscited by 4
  10. 2022Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning modelscited by 4
  11. 2023Efficient SAGE Estimation via Causal Structure Learningcited by 4
  12. 2021Attention-like feature explanation for tabular datacited by 3
10 more
  1. 2024Fast Calculation of Feature Contributions in Boosting Treescited by 3
  2. 2022Shapley Curves: A Smoothing Perspectivecited by 2
  3. 2023Interpretable Ensembles of Hyper-Rectangles as Base Modelscited by 2
  4. 2023MMD-based Variable Importance for Distributional Random Forestcited by 2
  5. 2026Regional Explanations: Bridging Local and Global Variable Importancecited by 1
  6. 2022Local and Regional Counterfactual Rules: Summarized and Robust Recoursescited by 0
  7. 2024Dual feature-based and example-based explanation methodscited by 0
  8. 2025Provable Recovery of Locally Important Signed Features and Interactions from Random Forestcited by 0
  9. 2026Unity Forests: Improving Interaction Modelling and Interpretability in Random Forestscited by 0
  10. 2026qshap: Fast Shapley Decomposition of R2 for Gradient-Boosted Treescited by 0