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