Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level. Although many variants have been proposed, few provide a simple way to produce high fidelity explanations that are also stable and intuitive. In this work, we provide a novel perspective by proposing a model agnostic local explanation method inspired by the invariant risk minimization (IRM) principle -- originally proposed for (global) out-of-distribution generalization -- to provide such high fidelity explanations that are also stable and unidirectional across nearby examples. Our method is based on a game theoretic formulation where we theoretically show that our approach has a strong tendency to eliminate features where the gradient of the black-box function abruptly changes sign in the locality of the example we want to explain, while in other cases it is more careful and will choose a more conservative (feature) attribution, a behavior which can be highly desirable for recourse. Empirically, we show on tabular, image and text data that the quality of our explanations with neighborhoods formed using random perturbations are much better than LIME and in some cases even comparable to other methods that use realistic neighbors sampled from the data manifold. This is desirable given that learning a manifold to either create realistic neighbors or to project explanations is typically expensive or may even be impossible. Moreover, our algorithm is simple and efficient to train, and can ascertain stable input features for local decisions of a black-box without access to side information such as a (partial) causal graph as has been seen in some recent works.

翻译：局部可解释模型无关解释（LIME）方法是最流行的逐样本解释黑箱模型的方法之一。尽管已提出众多变体，但鲜有方法能简单生成兼具高保真度、稳定性和直观性的解释。本研究提出一种受不变性风险最小化（IRM）原则启发的模型无关局部解释方法，该原则最初用于（全局）分布外泛化，旨在生成既高保真又能在邻近样本间保持稳定且单向的解释。我们的方法基于博弈论框架，理论证明该方法具有强烈倾向消除黑箱函数梯度在待解释样本局部区域急剧变号的特征；而在其他情况下则更为谨慎，选择更保守的（特征）归因——这种行为对可逆性决策具有高度可取性。实验表明，在表格、图像和文本数据上，使用随机扰动构建邻域时，我们解释的质量远超LIME方法，某些情况下甚至可比肩采用数据流形中真实邻域采样的其他方法。鉴于学习流形以生成真实邻域或投影解释通常成本高昂甚至不可行，这一特性尤为可贵。此外，我们的算法简单高效，无需访问侧信息（如近期工作中所需的局部因果图）即可为黑箱模型的局部决策确定稳定输入特征。