标题： 二元近端结果在微随机试验中的稳定权重双重稳健估计 显示英文标题

标题： Doubly Robust Estimation with Stabilized Weights for Binary Proximal Outcomes in Micro-Randomized Trials

摘要： 微随机试验（MRTs）越来越多地用于评估具有二元近期结果的移动健康干预措施。 标准逆概率加权（IPW）估计量是无偏的，但在小样本或极端随机化情况下不稳定。 估计均值偏离效应（EMEE）提高了效率，但缺乏双重稳健性。 我们提出了一种具有稳定和截断权重的双重稳健EMEE（DR-EMEE），结合了每个决策的IPW和结果回归。 我们证明了双重稳健性、渐近效率，并提供了有限样本方差校正，扩展到机器学习的干扰估计量。 在模拟中，DR-EMEE减少了均方根误差，提高了覆盖率，并在IPW上实现了两倍的效率提升，在EMEE上实现了五到十个百分点的提升。 对HeartSteps、PAMAP2和mHealth数据集的应用证实了在随机和观察性设置中稳定的高效推断。 显示英文摘要

摘要： Micro-randomized trials (MRTs) are increasingly used to evaluate mobile health interventions with binary proximal outcomes. Standard inverse probability weighting (IPW) estimators are unbiased but unstable in small samples or under extreme randomization. Estimated mean excursion effect (EMEE) improves efficiency but lacks double robustness. We propose a doubly robust EMEE (DR-EMEE) with stabilized and truncated weights, combining per-decision IPW and outcome regression. We prove double robustness, asymptotic efficiency, and provide finite-sample variance corrections, with extensions to machine learning nuisance estimators. In simulations, DR-EMEE reduces root mean squared error, improves coverage, and achieves up to twofold efficiency gains over IPW and five to ten percent over EMEE. Applications to HeartSteps, PAMAP2, and mHealth datasets confirm stable and efficient inference across both randomized and observational settings.