AI 中文总结
本文针对部分识别下治疗选择问题，提出新渐近框架，将简化型参数以最不利配置为中心，表征极限决策问题为正态位置偏移模型，并将结果应用于三类相关问题。
AI 中文摘要
当数据的抽样噪声因部分识别导致的基础不确定性而加剧时，我们提供一种新的渐近框架以推导近似最优的治疗分配。我们将简化型参数以其最不利配置为中心进行重新中心化，并考虑漂移参数序列，该序列既使抽样不确定性水平递减，也使部分识别程度递减。我们将极限决策问题表征为具有适当极限识别集的正态位置偏移模型。我们将所得结果应用于含污染结果的治疗选择问题、部分识别消费者剩余的稳健福利分析，以及用于政策采纳的实验估计值汇总问题。
英文摘要
We provide a new asymptotic framework to derive approximately optimal treatment assignments when sampling noise from data is compounded by fundamental uncertainty due to partial identification. We recenter the reduced-form parameter around its \emph{least-favorable} configuration and consider drifting parameter sequences that yield both diminishing levels of sampling uncertainty and of partial identification. We characterize the limiting decision problem as a normal location shift model with a suitable limiting identified set. We apply our results to treatment choice problems with contaminated outcomes, to robust welfare analyses with partially identified consumer surplus, and to the problem of aggregating experimental estimates for policy adoption.
CommentsThis version is identical to the previous one except for correcting a typo in the title (on arXiv, not in the document)
