  Many methods to explain black-box models, whether local or global, are
additive. In this paper, we study global additive explanations for non-additive
models, focusing on four explanation methods: partial dependence, Shapley
explanations adapted to a global setting, distilled additive explanations, and
gradient-based explanations. We show that different explanation methods
characterize non-additive components in a black-box model's prediction function
in different ways. We use the concepts of main and total effects to anchor
additive explanations, and quantitatively evaluate additive and non-additive
explanations. Even though distilled explanations are generally the most
accurate additive explanations, non-additive explanations such as tree
explanations that explicitly model non-additive components tend to be even more
accurate. Despite this, our user study showed that machine learning
practitioners were better able to leverage additive explanations for various
tasks. These considerations should be taken into account when considering which
explanation to trust and use to explain black-box models.
