Counterfactual explanations are a common approach to providing recourse to data subjects. However, current methodology can produce counterfactuals that cannot be achieved by the subject, making the use of counterfactuals for recourse difficult to justify in practice. Though there is agreement that plausibility is an important quality when using counterfactuals for algorithmic recourse, ground truth plausibility continues to be difficult to quantify. In this paper, we propose using longitudinal data to assess and improve plausibility in counterfactuals. In particular, we develop a metric that compares longitudinal differences to counterfactual differences, allowing us to evaluate how similar a counterfactual is to prior observed changes. Furthermore, we use this metric to generate plausible counterfactuals. Finally, we discuss some of the inherent difficulties of using counterfactuals for recourse.

翻译：反事实解释是一种为数据主体提供补救途径的常见方法。然而，当前的方法可能生成主体无法实现的反事实，这使得在实践中难以证明使用反事实进行补救的合理性。尽管业界普遍认为，将反事实用于算法补救时，合理性是一个重要品质，但真实世界中的合理性仍难以量化。本文提出利用纵向数据评估和改进反事实的合理性。具体而言，我们开发了一种度量标准，通过比较纵向差异与反事实差异，从而评估反事实与先前观察到的变化之间的相似程度。此外，我们利用这一度量标准生成合理的反事实。最后，我们讨论了使用反事实进行补救时固有的一些难点。