Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values

The paper introduces a novel method that merges Monte Carlo sampling with linear regression to estimate probabilistic values such as Shapley and Banzhaf values. It generalizes previous algorithms by allowing any efficiently computable function family, enabling the use of tree-based models like XGBoost and cross‑fitting for unbiased estimates. Experiments on eight datasets show the approach achieves state‑of‑the‑art accuracy, reducing error by up to 11× for Shapley values and 390× for general probabilistic values compared to existing methods.

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