In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets.

翻译：本文旨在连接两种现有的反事实推导方法：基于因果图的适应方法与最优传输方法。我们将"Knothe重排"与"三角传输"扩展至概率图模型，并利用这种称为序列传输的反事实方法来探讨个体层面的公平性问题。在建立所提方法的理论基础后，我们通过合成数据集与真实数据集的数值实验展示了其应用效果。