论文标题
非线性遗憾的治疗选择
Treatment Choice with Nonlinear Regret
论文作者
论文摘要
文献集中于福利遗憾的平均值，由于对采样不确定性的敏感性，这可能导致不良的治疗选择。我们建议最大程度地减少遗憾的非线性转变的平均值，并表明单身规则对于非线性遗憾本质上并不完整。为了关注Mean Square遗憾，我们为有限样品贝叶斯和最小值最佳规则提供了封闭形式的分数。我们的方法基于决策理论，并扩展以限制实验。治疗部分可以看作是有利于治疗的证据的优势。我们将框架应用于正常的回归模型和样本量计算。
The literature focuses on the mean of welfare regret, which can lead to undesirable treatment choice due to sensitivity to sampling uncertainty. We propose to minimize the mean of a nonlinear transformation of regret and show that singleton rules are not essentially complete for nonlinear regret. Focusing on mean square regret, we derive closed-form fractions for finite-sample Bayes and minimax optimal rules. Our approach is grounded in decision theory and extends to limit experiments. The treatment fractions can be viewed as the strength of evidence favoring treatment. We apply our framework to a normal regression model and sample size calculation.
