  Surging interest in deep learning from high-stakes domains has precipitated
concern over the inscrutable nature of black box neural networks. Explainable
AI (XAI) research has led to an abundance of explanation algorithms for these
black boxes. Such post hoc explainers produce human-comprehensible
explanations, however, their fidelity with respect to the model is not well
understood - explanation evaluation remains one of the most challenging issues
in XAI. In this paper, we ask a targeted but important question: can popular
feature-additive explainers (e.g., LIME, SHAP, SHAPR, MAPLE, and PDP) explain
feature-additive predictors? Herein, we evaluate such explainers on ground
truth that is analytically derived from the additive structure of a model. We
demonstrate the efficacy of our approach in understanding these explainers
applied to symbolic expressions, neural networks, and generalized additive
models on thousands of synthetic and several real-world tasks. Our results
suggest that all explainers eventually fail to correctly attribute the
importance of features, especially when a decision-making process involves
feature interactions.
