Prediction via Shapley Value Regression

Amr Alkhatib; Roman Bresson; Henrik Boström; Michalis Vazirgiannis

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arXiv · 2505.04775

Prediction via Shapley Value Regression

Amr Alkhatib·Roman Bresson·Henrik Boström·Michalis Vazirgiannis

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Abstract

Shapley values have several desirable, theoretically well-supported, properties for explaining black-box model predictions. Traditionally, Shapley values are computed post-hoc, leading to additional computational cost at inference time. To overcome this, a novel method, called ViaSHAP, is proposed, that learns a function to compute Shapley values, from which the predictions can be derived directly by summation. Two approaches to implement the proposed method are explored; one based on the universal approximation theorem and the other on the Kolmogorov-Arnold representation theorem. Results from a large-scale empirical investigation are presented, showing that ViaSHAP using Kolmogorov-Arnold Networks performs on par with state-of-the-art algorithms for tabular data. It is also shown that the explanations of ViaSHAP are significantly more accurate than the popular approximator FastSHAP on both tabular data and images.

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Amr Alkhatib, Roman Bresson, Henrik Boström, Michalis Vazirgiannis. 2025-05-07. Prediction via Shapley Value Regression. https://arxiv.org/abs/2505.04775

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