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.

中文翻译：

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模型双随机化 (MoTR)：使用可穿戴传感器估计个体平均治疗效果的蒙特卡罗方法

由于移动应用程序和可穿戴传感器，时间密集的单人“小数据”变得广泛可用。许多护理人员和自我追踪者希望使用这些数据来帮助特定的人改变他们的行为，以实现预期的健康结果。理想情况下，这涉及使用该人自己的观察时间序列数据从相关性中辨别可能的原因。在本文中，我们估计了身体活动对睡眠持续时间的个体平均治疗效果，反之亦然。我们介绍了用于分析个体密集纵向数据的模型孪生随机化（MoTR；“运动”）方法。形式上，MoTR 是 g 公式（即标准化、后门调整）在串​​行干扰下的应用。它估计稳定的重复效应，正如在 n-of-1 试验和单一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（可能存在混淆）进行比较，以展示如何使用因果推理为健康行为改变提出更好的个性化建议，并为其中一位作者分析 Fitbit 的 222 天睡眠和步数数据。