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Single-subject health data are becoming increasingly available thanks toadvances in self-tracking technology (e.g., wearable devices, mobile apps,sensors, implants). Many users and health caregivers seek to use suchobservational time series data to recommend changing health practices in orderto achieve desired health outcomes. However, there are few available causalinference approaches that are flexible enough to analyze such idiographic data.We develop a recently introduced causal-analysis framework based on n-of-1randomized trials, and implement a flexible random-forests g-formula approachto estimating a recurring individualized effect called the "average periodtreatment effect". In the process, we argue that our approach essentiallyresembles that of a longitudinal study by partitioning a single time seriesinto periods taking on binary treatment levels. We analyze six years of theauthor's own self-tracked physical activity and weight data to demonstrate ourapproach, and compare the results of our analysis to one that does not properlyaccount for confounding.

Person as Population: A Longitudinal View of Single-Subject Causal Inference for Analyzing Self-Tracked Health Data