让用户自由指定可变特征，生成更灵活的反事实解释。
Flexible Counterfactual Explanations with Generative Models
- 用模板动态定义可修改特征，无需重训练模型。
- 在经济与医疗数据上，解释有效性显著优于传统方法。
- 适用于无法访问模型内部的黑盒场景，支持个性化需求。
反事实解释通过建议最小化输入特征的改变来提供实现期望结果的可行路径。然而，现有方法依赖固定的可变特征集，难以适应用户多样化的现实约束。本文提出灵活反事实解释框架，引入反事实模板，使用户可在推理时动态指定可变特征。实现中采用生成对抗网络（FCEGAN），在不需模型重训练或额外优化的情况下，使解释符合用户设定约束。此外，FCEGAN适用于黑盒场景，仅利用历史预测数据生成解释，无需访问模型内部。在经济与医疗数据集上的实验表明，相较于传统基准方法，FCEGAN显著提升了反事实解释的有效性。通过整合用户驱动的灵活性与黑盒兼容性，反事实模板支持针对用户约束的个性化解释。
原文摘要 · Abstract (English)
Counterfactual explanations provide actionable insights to achieve desired outcomes by suggesting minimal changes to input features. However, existing methods rely on fixed sets of mutable features, which makes counterfactual explanations inflexible for users with heterogeneous real-world constraints. Here, we introduce Flexible Counterfactual Explanations, a framework incorporating counterfactual templates, which allows users to dynamically specify mutable features at inference time. In our implementation, we use Generative Adversarial Networks (FCEGAN), which align explanations with user-defined constraints without requiring model retraining or additional optimization. Furthermore, FCEGAN is designed for black-box scenarios, leveraging historical prediction datasets to generate explanations without direct access to model internals. Experiments across economic and healthcare datasets demonstrate that FCEGAN significantly improves counterfactual explanations' validity compared to traditional benchmark methods. By integrating user-driven flexibility and black-box compatibility, counterfactual templates support personalized explanations tailored to user constraints.
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