Tractable Shapley Values and Interactions via Tensor Networks

Farzaneh Heidari; Chao Li; Guillaume Rabusseau

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arXiv · 2510.22138

Tractable Shapley Values and Interactions via Tensor Networks

Farzaneh Heidari·Chao Li·Guillaume Rabusseau

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Abstract

We show how to replace the O(2^n) coalition enumeration over n features behind Shapley values and Shapley-style interaction indices with a few-evaluation scheme on a tensor-network (TN) surrogate: TN-SHAP. The key idea is to represent a predictor's local behavior as a factorized multilinear map, so that coalitional quantities become linear probes of a coefficient tensor. TN-SHAP replaces exhaustive coalition sweeps with just a small number of targeted evaluations to extract order-k Shapley interactions. In particular, both order-1 (single-feature) and order-2 (pairwise) computations have cost O(n*poly(chi) + n^2), where chi is the TN's maximal cut rank. We provide theoretical guarantees on the approximation error and tractability of TN-SHAP. On UCI datasets, our method matches enumeration on the fitted surrogate while reducing evaluation by orders of magnitude and achieves 25-1000x wall-clock speedups over KernelSHAP-IQ at comparable accuracy, while amortizing training across local cohorts.

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Farzaneh Heidari, Chao Li, Guillaume Rabusseau. 2026-05-31. Tractable Shapley Values and Interactions via Tensor Networks. https://arxiv.org/abs/2510.22138

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