重新定义反事实解释的距离度量，让生成的解释更符合数据分布。
Rethinking Distance Metrics for Counterfactual Explainability
- 将反事实样本与原始样本视为联合采样，而非独立生成。
- 提出的新度量能更好捕捉变量间的复杂依赖关系。
- 适用于各类机器学习解释场景，提升解释合理性。
反事实解释是机器学习中一种流行的后验可解释性方法，通过生成与参考样本相似但预测结果更优的新数据点来解释分类器决策。本文提出一种新框架，将反事实样本与参考样本视作从底层数据分布中联合采样的结果，而非独立地在参考点周围生成。基于此框架，我们推导出一种专用于反事实相似性的距离度量，适用于广泛的应用场景。通过定量与定性分析，验证该框架能更细致地表达协变量之间的复杂依赖关系。
原文摘要 · Abstract (English)
Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by generating new data points that are similar to a given reference, while receiving a more desirable prediction. In this work, we investigate a framing for counterfactual generation methods that considers counterfactuals not as independent draws from a region around the reference, but as jointly sampled with the reference from the underlying data distribution. Through this framing, we derive a distance metric, tailored for counterfactual similarity that can be applied to a broad range of settings. Through both quantitative and qualitative analyses of counterfactual generation methods, we show that this framing allows us to express more nuanced dependencies among the covariates.
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