SHAP values via sparse Fourier representation

SHAP (SHapley Additive exPlanations) values are a widely used method for local feature attribution in interpretable and explainable AI. We propose an efficient two-stage algorithm for computing SHAP values in both black-box setting and tree-based models. We assume the black-box predictor or tree model accepts binary (zero-one) features.