发表机构
University of Pennsylvania; Massachusetts Institute of Technology（宾夕法尼亚大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出保形策略学习（CPL），通过阈值化保形p值控制处理分配对个体造成伤害的概率，在随机实验和观察性研究中提供无分布安全保证，并实证应用于减少阴谋信念的AI干预。
AI 中文摘要
策略学习旨在根据个体特征确定谁应接受处理。在医学和公共政策等安全至关重要的高风险场景中，仅改善平均结果可能不够：决策者还可能希望保护个体免受伤害，符合“不伤害”的希波克拉底原则。本文提出保形策略学习（CPL），一种具有新的无分布安全保证的策略学习程序，该保证控制将处理分配给相对于对照会受到伤害的个体的概率。CPL将每个处理决策视为检验反事实伤害的假设，并通过阈值化保形p值来分配处理。这些p值使用可观测的代理变量和选择性校准，以应对比较下的潜在结果从未同时观测到的挑战。对于随机实验，在标准可交换性条件下，CPL在用户指定水平下提供有限样本安全保证，无需施加任何结果建模假设。此外，当结果模型被一致估计时，CPL在安全约束下实现渐近最优福利。在观察性研究中，具有学习后平衡权重的CPL实现双重稳健安全保证。我们通过大量模拟评估CPL，并将其应用于旨在减少阴谋信念的AI驱动干预的实证研究。
英文摘要
Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the average outcomes alone may not be sufficient: decision makers may also seek to protect individuals from harm, in line with the Hippocratic principle of ``do no harm.'' In this paper, we propose \textit{conformal policy learning} (CPL), a policy learning procedure with a new distribution-free safety guarantee that controls the probability of assigning treatment to an individual who would be harmed relative to control. CPL views each treatment decision as testing a hypothesis of counterfactual harm and assigns treatment by thresholding conformal p-values. These p-values use observable proxies and selective calibration to address the challenge that the potential outcomes under comparison are never simultaneously observed. For randomized experiments, under standard exchangeability conditions, CPL provides finite-sample safety guarantee at a user-specified level, without imposing any outcome modeling assumptions. Moreover, when the outcome model is consistently estimated, CPL achieves asymptotically optimal welfare subject to the safety constraint. In observational studies, CPL with learn-then-balance weights achieves doubly robust safety guarantees. We evaluate CPL through extensive simulations and apply it to an empirical study of AI-powered interventions designed to reduce conspiracy beliefs.
