Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. To this end, we introduce a model-agnostic modular framework, TraCE (Trajectory Counterfactual Explanation) scores, which is able to distill and condense progress in highly complex scenarios into a single value. We demonstrate TraCE's utility across domains by showcasing its main properties in two case studies spanning healthcare and climate change.

翻译：反事实解释及其相关的算法回溯通常用于理解、解释及可能改变黑箱分类器的预测结果。本文提出将反事实解释的应用扩展至序列决策任务的进度评估。为此，我们引入一个与模型无关的模块化框架——TraCE（轨迹反事实解释）评分，该框架能够将高度复杂场景中的进度提炼并浓缩为单一数值。我们通过医疗和气候变化两个案例研究展示了TraCE的主要特性，证明了其跨领域的实用性。