Twin revolutions in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness of the mHealth intervention and introduce a novel class of causal estimands called "causal excursion effects." These estimands enable the evaluation of how intervention effects change over time and are influenced by individual characteristics or context. However, existing analysis methods for causal excursion effects require prespecified features of the observed high-dimensional history to build a working model for a critical nuisance parameter. Machine learning appears ideal for automatic feature construction, but their naive application can lead to bias under model misspecification. 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 supervised learning algorithms used to estimate nuisance parameters. We present the bidirectional asymptotic properties of the proposed estimators and compare them both theoretically and through extensive simulations. The results show relative efficiency gains and support the suggestion of a doubly robust alternative to existing methods. Finally, the proposed methods' practical utilities are demonstrated by analyzing data from a multi-institution cohort of first-year medical residents in the United States (NeCamp et al., 2020).

翻译：可穿戴技术和智能手机提供的健康干预的双重革命极大地提高了移动健康（mHealth）干预的可及性。微随机试验（MRTs）旨在评估mHealth干预的有效性，并引入了一类称为“因果偏移效应”的新型因果估计量。这些估计量能够评估干预效果如何随时间变化，以及如何受个体特征或情境影响。然而，现有的因果偏移效应分析方法需要预先指定观测到的高维历史数据的特征，以便为关键干扰参数构建工作模型。机器学习似乎是自动特征构建的理想选择，但其简单应用在模型设定错误的情况下可能导致偏差。为了解决这个问题，本文从元学习器的角度重新审视了因果偏移效应的估计，其中分析者对用于估计干扰参数的监督学习算法保持不可知论。我们提出了所提估计量的双向渐近性质，并从理论和广泛的模拟两方面进行了比较。结果显示了相对效率的提升，并支持了现有方法的双重稳健替代方案的建议。最后，通过分析美国多机构一年级住院医师队列的数据（NeCamp等人，2020年），证明了所提方法的实际效用。