The application of Algorithmic Recourse in decision-making is a promising field that offers practical solutions to reverse unfavorable decisions. However, the inability of these methods to consider potential dependencies among variables poses a significant challenge due to the assumption of feature independence. Recent advancements have incorporated knowledge of causal dependencies, thereby enhancing the quality of the recommended recourse actions. Despite these improvements, the inability to incorporate the temporal dimension remains a significant limitation of these approaches. This is particularly problematic as identifying and addressing the root causes of undesired outcomes requires understanding time-dependent relationships between variables. In this work, we motivate the need to integrate the temporal dimension into causal algorithmic recourse methods to enhance recommendations' plausibility and reliability. The experimental evaluation highlights the significance of the role of time in this field.

翻译：算法补救在决策制定中的应用是一个前景广阔的领域，它为逆转不利决策提供了实用的解决方案。然而，这些方法因假设特征独立而无法考虑变量间的潜在依赖关系，这构成了重大挑战。近期的进展已纳入因果依赖知识，从而提升了所推荐补救措施的质量。尽管有这些改进，无法融入时间维度仍然是这些方法的一个显著局限。这一问题尤为关键，因为识别并解决不良结果的根源需要理解变量之间的时间依赖关系。本文中，我们论证了将时间维度整合到因果算法补救方法中的必要性，以增强推荐结果的可信性和可靠性。实验评估强调了时间在该领域中的重要意义。