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.

本文提出一类新的模型解释方法 - 基于删除的解释，并提出一个三个方面的框架来描述和统一 26 种已有的模型解释方法。通过这个分析，我们发展了一个统一的框架，帮助实践者更好地理解模型解释工具，并为未来的可解释性研究提供了坚实的理论基础。

去除来解释：模型解释的统一框架

Explaining by Removing: A Unified Framework for Model Explanation

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 examine the literature and find that many methods are
based on a shared principle of explaining by removing - essentially, measuring
the impact of removing sets of features from a model. These methods vary in
several respects, so we develop a framework for removal-based explanations 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, permutation tests). Exposing the fundamental similarities
between these methods empowers users to reason about which tools to use, and
suggests promising directions for ongoing model explainability research.

本研究着眼于解释模型，提出一种基于特征删除的解释框架，包括 26 种方法（如 SHAP 和 LIME 等），揭示这些方法间的相似性，为选择和使用工具提供了有力支持，并且为进一步的可解释 AI 研究提供有益方向。