Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person's own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on sleep duration, and vice-versa. We introduce the model twin randomization (MoTR; "motor") method for analyzing an individual's intensive longitudinal data. Formally, MoTR is an application of the g-formula (i.e., standardization, back-door adjustment) under serial interference. It estimates stable recurring effects, as is done in n-of-1 trials and single case experimental designs. We compare our approach to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change, and analyze 222 days of Fitbit sleep and steps data for one of the authors.

翻译：摘要：得益于移动应用和可穿戴传感器，时间密集的个体“小数据”现已广泛可用。许多护理者和自我追踪者希望利用这些数据帮助特定个体改变行为，以实现理想的健康结果。理想情况下，这涉及利用该个体自己的观测时间序列数据，从相关性中辨别可能的原因。本文中，我们估计了身体活动对睡眠持续时间的个体内平均处理效应，反之亦然。我们引入了模型双随机化（MoTR；“motor”）方法，用于分析个体的密集型纵向数据。形式上，MoTR是在序列干扰下对g-公式（即标准化、后门调整）的一种应用。它估计稳定的重复性效应，正如在单病例试验和单案例实验设计中所做的那样。我们将我们的方法与标准方法（可能存在混杂因素）进行比较，以展示如何利用因果推断为健康行为改变做出更好的个性化推荐，并分析了其中一位作者222天的Fitbit睡眠和步数数据。