Constrained Classification and Policy Learning
受约束分类与政策学习AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确将个体化处理规则的因果政策学习表述为加权分类问题。
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受约束分类与政策学习
- 研究问题
- 当可解释性或公平性约束使分类器集合无法包含最优分类器时，替代损失最小化还能否一致地找到受约束集合中的最佳规则？
- 方法与思路
- 作者区分仅限制预测集合与同时限制函数形式的约束，分析替代损失的一致性，并据此构造基于 hinge 损失的单调分类程序。
- 材料陈述的贡献
- 摘要称，仅限制预测集合时 hinge 损失具有唯一的一致性保持性质；限制函数形式后仍需额外条件，并给出相应条件刻画。
- 阅读关注点
- 应核对两类约束的精确定义、唯一性结论覆盖哪些替代损失，以及加权分类结果如何适用于个体化处理规则的政策学习。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未列明全部条件与损失类别，不能将 hinge 损失视为普遍一致。
原文摘要
Modern machine learning approaches to classification, including AdaBoost, support vector machines, and deep neural networks, utilize surrogate loss techniques to circumvent the computational complexity of minimizing empirical classification risk. These techniques are also useful for causal policy learning problems, since estimation of individualized treatment rules can be cast as a weighted (cost-sensitive) classification problem. Consistency of the surrogate loss approaches studied in Zhang (2004) and Bartlett et al. (2006) relies on the assumption of correct specification, which means that the specified set of classifiers is rich enough to contain a first-best classifier. This assumption is, however, less credible when interpretability or fairness constraints restrict the set of classifiers. Consequently, the applicability of surrogate-loss-based algorithms in such second-best scenarios remains unknown. This paper studies the consistency of surrogate loss procedures under a constrained set of classifiers without assuming correct specification. We show that in settings where the constraint restricts the classifier's prediction set only, hinge losses (i.e., $\ell_1$-support vector machines) are the only surrogate losses that preserve consistency in second-best scenarios. If the constraint additionally restricts the functional form of the classifier, consistency of a surrogate loss approach is not guaranteed, even with hinge loss. We therefore characterize conditions on the constrained set of classifiers that can guarantee consistency of hinge-risk-minimizing classifiers. Exploiting our theoretical results, we develop robust and computationally attractive hinge-loss-based procedures for a monotone classification problem.