We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization of plausibility within a unified framework. Compared to reference approaches, our method enforces plausibility by directly optimizing the explicit density function without assuming a particular family of parametrized distributions. This ensures CFs are not only valid (i.e., achieve class change) but also align with the underlying data's probability density. For that purpose, our approach leverages normalizing flows as powerful density estimators to capture the complex high-dimensional data distribution. Furthermore, we introduce a novel loss that balances the trade-off between achieving class change and maintaining closeness to the original instance while also incorporating a probabilistic plausibility term. PPCEF's unconstrained formulation allows for efficient gradient-based optimization with batch processing, leading to orders of magnitude faster computation compared to prior methods. Moreover, the unconstrained formulation of PPCEF allows for the seamless integration of future constraints tailored to specific counterfactual properties. Finally, extensive evaluations demonstrate PPCEF's superiority in generating high-quality, probabilistically plausible counterfactual explanations in high-dimensional tabular settings. This makes PPCEF a powerful tool for not only interpreting complex machine learning models but also for improving fairness, accountability, and trust in AI systems.

翻译：本文提出PPCEF，一种生成概率可信反事实解释（CFs）的新方法。PPCEF通过将利用数据分布的概率化表述与可信度优化结合在统一框架中，超越了现有方法。相较于参考方法，本方法通过直接优化显式密度函数（无需假设特定参数化分布族）来保证可信度，确保反事实解释不仅有效（即实现类别转换），同时符合底层数据的概率密度分布。为此，我们利用归一化流作为强大的密度估计器来捕捉复杂的高维数据分布。此外，我们提出一种新型损失函数，在实现类别转换与保持原始实例接近度之间进行权衡，同时纳入概率可信度项。PPCEF的无约束公式化设计支持基于梯度的批量优化，计算速度较现有方法提升数个数量级。该无约束框架还可无缝集成针对特定反事实属性定制的未来约束。最终，大量评估实验证明PPCEF在高维表格数据场景中生成高质量概率可信反事实解释的优越性，使其成为解释复杂机器学习模型、提升人工智能系统公平性、可问责性与可信度的有力工具。