To increase the adoption of counterfactual explanations in practice, several criteria that these should adhere to have been put forward in the literature. We propose counterfactual explanations using optimization with constraint learning (CE-OCL), a generic and flexible approach that addresses all these criteria and allows room for further extensions. Specifically, we discuss how we can leverage an optimization with constraint learning framework for the generation of counterfactual explanations, and how components of this framework readily map to the criteria. We also propose two novel modeling approaches to address data manifold closeness and diversity, which are two key criteria for practical counterfactual explanations. We test CE-OCL on several datasets and present our results in a case study. Compared against the current state-of-the-art methods, CE-OCL allows for more flexibility and has an overall superior performance in terms of several evaluation metrics proposed in related work.
翻译:为了在实践中更多地采用反事实解释,文献中已经提出了其中应该遵守的若干标准。我们提议采用限制学习的优化(CE-OCL)来反事实解释,这是一种处理所有这些标准的通用和灵活办法,并允许进一步扩展。具体地说,我们讨论如何利用限制学习框架来优化反事实解释的产生,以及这一框架的组成部分如何容易地与标准相匹配。我们还提出了两种新颖的模型方法,以解决数据多重密切性和多样性问题,这是实际反事实解释的两个主要标准。我们在若干数据集中测试CE-OCL,并在案例研究中介绍我们的结果。与目前的最新方法相比,CE-OCL允许更大的灵活性,并在相关工作中提议的若干评价指标方面总体优于业绩。