  Researchers have proposed a wide variety of model explanation approaches, but
it remains unclear how most methods are related or when one method is
preferable to another. We describe a new unified class of methods,
removal-based explanations, that are based on the principle of simulating
feature removal to quantify each feature's influence. These methods vary in
several respects, so we develop a framework that characterizes each method
along three dimensions: 1) how the method removes features, 2) what model
behavior the method explains, and 3) how the method summarizes each feature's
influence. Our framework unifies 26 existing methods, including several of the
most widely used approaches: SHAP, LIME, Meaningful Perturbations, and
permutation tests. This newly understood class of explanation methods has rich
connections that we examine using tools that have been largely overlooked by
the explainability literature. To anchor removal-based explanations in
cognitive psychology, we show that feature removal is a simple application of
subtractive counterfactual reasoning. Ideas from cooperative game theory shed
light on the relationships and trade-offs among different methods, and we
derive conditions under which all removal-based explanations have
information-theoretic interpretations. Through this analysis, we develop a
unified framework that helps practitioners better understand model explanation
tools, and that offers a strong theoretical foundation upon which future
explainability research can build.

Machine Learning;Machine Learning