From Shapley Values to Generalized Additive Models and back

• Published in: arXiv.org 2023-02
• Main Authors: Bordt, Sebastian, Ulrike von Luxburg
• Format: Article
• Language: English
• Subjects: Algorithms; Decomposition; Empirical analysis; Machine learning
• ISSN: 2331-8422

In explainable machine learning, local post-hoc explanation algorithms and inherently interpretable models are often seen as competing approaches. This work offers a partial reconciliation between the two by establishing a correspondence between Shapley Values and Generalized Additive Models (GAMs). We introduce \(n\)-Shapley Values, a parametric family of local post-hoc explanation algorithms that explain individual predictions with interaction terms up to order \(n\). By varying the parameter \(n\), we obtain a sequence of explanations that covers the entire range from Shapley Values up to a uniquely determined decomposition of the function we want to explain. The relationship between \(n\)-Shapley Values and this decomposition offers a functionally-grounded characterization of Shapley Values, which highlights their limitations. We then show that \(n\)-Shapley Values, as well as the Shapley Taylor- and Faith-Shap interaction indices, recover GAMs with interaction terms up to order \(n\). This implies that the original Shapely Values recover GAMs without variable interactions. Taken together, our results provide a precise characterization of Shapley Values as they are being used in explainable machine learning. They also offer a principled interpretation of partial dependence plots of Shapley Values in terms of the underlying functional decomposition. A package for the estimation of different interaction indices is available at \url{https://github.com/tml-tuebingen/nshap}.