让预测集合具备反事实公平性，无需训练即可保证准确率与公平性兼顾。
Counterfactually Fair Conformal Prediction
- 通过保护属性干预对称化符合度分数，实现反事实公平的预测集
- 在合成与真实数据上均达到目标覆盖率，预测集大小增加极少
- 适用于需要公平性保障的回归与分类任务，无需额外训练
尽管点预测的反事实公平性已得到充分研究，但其向不确定性下的预测集合——公平决策的核心——的扩展仍不充分。另一方面，置信预测（CP）能提供高效、无需分布假设、有限样本下有效的预测集合，但无法保证反事实公平性。本文提出反事实公平置信预测（CF-CP），生成具有反事实公平性的预测集合。通过在受保护属性干预下对称化符合度分数，证明了CF-CP在保持边际覆盖性的同时，可实现反事实公平性。实证结果表明，在合成与真实数据集上，跨回归与分类任务，CF-CP均达到预期反事实公平性，并以最小增益维持目标覆盖率。该方法为反事实公平不确定性量化提供了简单、无需训练的路径。
原文摘要 · Abstract (English)
While counterfactual fairness of point predictors is well studied, its extension to prediction sets--central to fair decision-making under uncertainty--remains underexplored. On the other hand, conformal prediction (CP) provides efficient, distribution-free, finite-sample valid prediction sets, yet does not ensure counterfactual fairness. We close this gap by developing Counterfactually Fair Conformal Prediction (CF-CP) that produces counterfactually fair prediction sets. Through symmetrization of conformity scores across protected-attribute interventions, we prove that CF-CP results in counterfactually fair prediction sets while maintaining the marginal coverage property. Furthermore, we empirically demonstrate that on both synthetic and real datasets, across regression and classification tasks, CF-CP achieves the desired counterfactual fairness and meets the target coverage rate with minimal increase in prediction set size. CF-CP offers a simple, training-free route to counterfactually fair uncertainty quantification.
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