Counterfactual explanations (CE) are the de facto method for providing insights into black-box decision-making models by identifying alternative inputs that lead to different outcomes. However, existing CE approaches, including group and global methods, focus predominantly on specific input modifications, lacking the ability to capture nuanced distributional characteristics that influence model outcomes across the entire input-output spectrum. This paper proposes distributional counterfactual explanation (DCE), shifting focus to the distributional properties of observed and counterfactual data, thus providing broader insights. DCE is particularly beneficial for stakeholders making strategic decisions based on statistical data analysis, as it makes the statistical distribution of the counterfactual resembles the one of the factual when aligning model outputs with a target distribution\textemdash something that the existing CE methods cannot fully achieve. We leverage optimal transport (OT) to formulate a chance-constrained optimization problem, deriving a counterfactual distribution aligned with its factual counterpart, supported by statistical confidence. The efficacy of this approach is demonstrated through experiments, highlighting its potential to provide deeper insights into decision-making models.

翻译：反事实解释（CE）是通过识别导致不同结果的替代输入来为黑盒决策模型提供洞见的实际标准方法。然而，现有的CE方法（包括群体和全局方法）主要侧重于特定的输入修改，缺乏捕捉在整个输入-输出谱上影响模型结果的细微分布特征的能力。本文提出分布反事实解释（DCE），将焦点转向观测数据与反事实数据的分布特性，从而提供更广泛的洞见。当将模型输出与目标分布对齐时，DCE使反事实的统计分布与事实分布相似——这是现有CE方法无法完全实现的，因此对于基于统计数据分析做出战略决策的利益相关者尤为有益。我们利用最优传输（OT）构建了一个机会约束优化问题，推导出一个与其事实对应物对齐、并具有统计置信度支持的反事实分布。通过实验证明了该方法的有效性，突显了其为决策模型提供更深入洞见的潜力。