Many existing methods of counterfactual explanations ignore the intrinsic relationships between data attributes and thus fail to generate realistic counterfactuals. Moreover, the existing models that account for relationships require domain knowledge, which limits their applicability in complex real-world applications. In this paper, we propose a novel approach to realistic counterfactual explanations that preserve the relationships and minimise experts' interventions. The model directly learns the relationships by a variational auto-encoder with minimal domain knowledge and then learns to perturb the latent space accordingly. We conduct extensive experiments on both synthetic and real-world datasets. The experimental results demonstrate that the proposed model learns relationships from the data and preserves these relationships in generated counterfactuals. In particular, it outperforms other methods in terms of Mahalanobis distance, and the constraint feasibility score.

翻译：现有的许多反事实解释方法忽视了数据属性之间的内在关系,因而未能产生现实的反事实。此外,目前反映关系的现有模型需要领域知识,这限制了这些模型在复杂的现实世界应用中的适用性。在本文件中,我们提出了一种新颖的方法,以现实的反事实解释来维护这些关系并最大限度地减少专家的干预。模型直接通过具有最低域知识的变式自动编码器学习各种关系,然后学习相应地扰动潜在空间。我们在合成和现实世界数据集上进行了广泛的实验。实验结果显示,拟议的模型从数据中学习关系,并将这些关系保留在产生的反事实中。特别是,该模型在马哈拉诺比斯距离和限制可行性评分方面优于其他方法。