Twin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of mobile health (mHealth) interventions across various health science domains. Sequentially randomized experiments called micro-randomized trials (MRTs) have grown in popularity to empirically evaluate the effectiveness of these mHealth intervention components. MRTs have given rise to a new class of causal estimands known as "causal excursion effects", which enable health scientists to assess how intervention effectiveness changes over time or is moderated by individual characteristics, context, or responses in the past. However, current data analysis methods for estimating causal excursion effects require pre-specified features of the observed high-dimensional history to construct a working model of an important nuisance parameter. While machine learning algorithms are ideal for automatic feature construction, their naive application to causal excursion estimation can lead to bias under model misspecification, potentially yielding incorrect conclusions about intervention effectiveness. To address this issue, this paper revisits the estimation of causal excursion effects from a meta-learner perspective, where the analyst remains agnostic to the choices of supervised learning algorithms used to estimate nuisance parameters. The paper presents asymptotic properties of the novel estimators and compares them theoretically and through extensive simulation experiments, demonstrating relative efficiency gains and supporting the recommendation for a doubly robust alternative to existing methods. Finally, the practical utility of the proposed methods is demonstrated by analyzing data from a multi-institution cohort of first-year medical residents in the United States (NeCamp et al., 2020).

翻译：可穿戴技术与智能手机提供的数字健康干预的双重革命，显著扩大了移动健康（mHealth）干预在各个健康科学领域的可及性和应用率。称为微随机试验（MRTs）的序贯随机实验在实证评估这些mHealth干预组件的有效性方面日益流行。MRTs催生了一类新的因果估计量——“因果波动效应”，使健康科学家能够评估干预有效性如何随时间变化，或如何受到个体特征、情境或过去反应的调节。然而，当前用于估计因果波动效应的数据分析方法需预先指定观测到的高维历史特征，以构建重要干扰参数的工作模型。尽管机器学习算法适用于自动特征构建，但其在因果波动估计中的朴素应用可能导致模型错误指定下的偏差，从而得出关于干预有效性的错误结论。为解决这一问题，本文从元学习视角重新审视因果波动效应的估计，其中分析者对用于估计干扰参数的监督学习算法选择保持不可知。本文展示了新估计量的渐近性质，并通过理论比较与大量仿真实验，证明了相对效率的提升，并支持推荐现有方法的双重稳健替代方案。最后，通过分析美国多机构一年级医学生队列数据（NeCamp et al., 2020）展示了所提方法的实际应用价值。