  Explainable Artificial Intelligence (XAI) has received widespread interest in
recent years, and two of the most popular types of explanations are feature
attributions, and counterfactual explanations. These classes of approaches have
been largely studied independently and the few attempts at reconciling them
have been primarily empirical. This work establishes a clear theoretical
connection between game-theoretic feature attributions, focusing on but not
limited to SHAP, and counterfactuals explanations. After motivating operative
changes to Shapley values based feature attributions and counterfactual
explanations, we prove that, under conditions, they are in fact equivalent. We
then extend the equivalency result to game-theoretic solution concepts beyond
Shapley values. Moreover, through the analysis of the conditions of such
equivalence, we shed light on the limitations of naively using counterfactual
explanations to provide feature importances. Experiments on three datasets
quantitatively show the difference in explanations at every stage of the
connection between the two approaches and corroborate the theoretical findings.

Artificial Intelligence;Computer Vision and Pattern Recognition;Computer Science and Game Theory;Human-Computer Interaction;Machine Learning