Prescriptions, or actionable recommendations, are commonly generated across various fields to influence key outcomes such as improving public health, enhancing economic policies, or increasing business efficiency. While traditional association-based methods may identify correlations, they often fail to reveal the underlying causal factors needed for informed decision-making. On the other hand, in decision-making for tasks with significant societal or economic impact, it is crucial to provide recommendations that are justifiable and equitable in terms of the outcome for both the protected and non-protected groups. Motivated by these two goals, this paper introduces a fairness-aware framework leveraging causal reasoning for generating a set of actionable prescription rules (ruleset) toward betterment of an outcome while preventing exacerbating inequalities for protected groups. By considering group and individual fairness metrics from the literature, we ensure that both protected and non-protected groups benefit from these recommendations, providing a balanced and equitable approach to decision-making. We employ efficient optimizations to explore the vast and complex search space considering both fairness and coverage of the ruleset. Empirical evaluation and case study on real-world datasets demonstrates the utility of our framework for different use cases.

翻译：处方，即可操作的推荐，在诸多领域中普遍用于影响关键结果，如改善公共卫生、优化经济政策或提升商业效率。传统的基于关联的方法虽能识别相关性，却往往无法揭示决策所需的内在因果因素。另一方面，在对社会或经济影响重大的任务进行决策时，提供对受保护群体与非受保护群体在结果上均具有合理性与公平性的推荐至关重要。基于这两项目标，本文提出了一种利用因果推理的公平感知框架，用于生成一组旨在改善结果、同时防止加剧受保护群体不平等的可操作处方规则（规则集）。通过考虑文献中的群体与个体公平性度量，我们确保受保护群体与非受保护群体均能从这些推荐中受益，从而提供一种平衡且公平的决策方法。我们采用高效优化技术，在兼顾规则集公平性与覆盖度的前提下，探索广阔而复杂的搜索空间。在真实世界数据集上的实证评估与案例研究展示了本框架在不同应用场景中的实用性。