具有启动机制的最优序贯决策制定

中文摘要 AI 辅助

考虑一个从大规模、完美执行的序贯随机实验中正确识别出的最优动态治疗方案 $g^{\ extbf{opt}}$。即使实验结果可推广至未来的目标人群，也无法保证 $g^{\ extbf{opt}}$ 优于人类决策者；当人类专家能够获取实验记录协变量之外的相关信息时，他们可以做得比 $g^{\ extbf{opt}}$ 更好。基于这一观察，我们推导了一类称为启动机制的新机制的结果，该机制推广了关于超优机制的现有结果。这些机制在启动序贯最优机制变得更为有益之前遵循人类决策者的决策，并且保证优于纯人类和纯算法决策规则（例如，基于强化学习算法的规则）。此外，我们提出了能够识别最优启动机制的修正实验设计，展示了如何在明确假设下从经典观测数据中识别最优启动机制，并给出了这些机制的估计和统计推断方法。为了说明这些方法的实际效用，我们在一个关于下背痛治疗的案例研究中考虑了启动机制。

英文摘要

Consider an optimal dynamic treatment regime, $g^{\textbf{opt}}$ correctly identified from a large, perfectly executed sequentially randomized experiment. Even when the experimental results are generalizable to a future target population, there is no guarantee that $g^{\textbf{opt}}$ outperforms human decision-makers; human experts can do better than $g^{\textbf{opt}}$ whenever they have access to relevant information beyond the covariates recorded in the experiment. Motivated by this observation, we derive results on a new class of regimes called initiation regimes, which generalize existing results on superoptimal regimes. These regimes follow human decision-makers up to the point where it becomes more beneficial to initiate a sequential optimal regime, and are guaranteed to outperform both purely human and purely algorithmic decision rules, e.g., based on reinforcement learning algorithms. Furthermore, we present modified experimental designs that identify the best initiation regimes, show how the best initiation regime can be identified from classical observational data under explicit assumptions, and give estimation and statistical inference methodology for these regimes. To illustrate the practical utility of the methods, we consider initiation regimes in a case study on treatment of lower back pain.

发表机构

• Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
• University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）

机构由 AI 辅助整理，请以论文原文为准。