A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation
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评估时变调节作用的因果偏离效应元学习方法
- 研究问题
- 移动健康微随机试验中，面对随机化概率不确定、观测缺失和高维历史，如何估计随时间及个体情境变化的干预效应？
- 方法与思路
- 作者从元学习视角提出 DR-WCLS 双重稳健推断程序，以更灵活地估计因果偏离效应，并进行理论与模拟比较。
- 材料陈述的贡献
- 摘要称给出估计量的渐近性质，并在缺失观测或随机化概率不确定时展示一致性及效率表现，另提供住院医师数据应用。
- 阅读关注点
- 核对因果偏离效应的干预参照、时变调节变量定义、缺失处理与双重稳健条件，以及摘要所称双向渐近性质的含义。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实。摘要未说明缺失机制、具体渐近条件及效率比较范围，不能泛化优势。
原文摘要
Advances 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. Existing methods for analyzing causal excursion effects assume known randomization probabilities, complete observations, and a linear nuisance function with prespecified features of the high dimensional observed history. However, in complex mobile systems, these assumptions often fall short: randomization probabilities can be uncertain, observations may be incomplete, and the granularity of mHealth data makes linear modeling difficult. To address this issue, we propose a flexible and doubly robust inferential procedure, called "DR-WCLS," for estimating causal excursion effects from a meta-learner perspective. We present the bidirectional asymptotic properties of the proposed estimators and compare them with existing methods both theoretically and through extensive simulations. The results show a consistent and more efficient estimate, even with missing observations or uncertain treatment randomization probabilities. Finally, the practical utility of the proposed methods is demonstrated by analyzing data from a multiinstitution cohort of first-year medical residents in the United States (NeCamp et al., 2020).