InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly
James Enouen Yan Liu
In recent years, the Shapley value and SHAP explanations have emerged as one
of the most dominant paradigms for providing post-hoc explanations of blackbox models. Despite their well-founded theoretical properties, many recent works
have focused on the limitations in both their computational efficiency and their
representation power. The underlying connection with additive models, however,
is left critically under-emphasized in the current literature. In this work, we find
that a variational perspective linking GAM models and SHAP explanations is able
to provide deep insights into nearly all recent developments. In light of this connection, we borrow in the other direction to develop a new method to train interpretable GAM models which are automatically purified to compute the Shapley
value in a single forward pass. Finally, we provide theoretical results showing the
limited representation power of GAM models is the same Achilles’ heel existing
in SHAP and discuss the implications for SHAP’s modern usage in CV and NLP.
additive models, GAM, SHAP, Shapley