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Temporally dense single-person "small data" have become widely availablethanks to mobile apps and wearable sensors. Many caregivers and self-trackerswant to use these data to help a specific person change their behavior toachieve desired health outcomes. Ideally, this involves discerning possiblecauses from correlations using that person's own observational time seriesdata. In this paper, we estimate within-individual average treatment effects ofphysical activity on sleep duration, and vice-versa. We introduce the modeltwin randomization (MoTR; "motor") method for analyzing an individual'sintensive longitudinal data. Formally, MoTR is an application of the g-formula(i.e., standardization, back-door adjustment) under serial interference. Itestimates stable recurring effects, as is done in n-of-1 trials and single caseexperimental designs. We compare our approach to standard methods (withpossible confounding) to show how to use causal inference to make betterpersonalized recommendations for health behavior change, and analyze 222 daysof Fitbit sleep and steps data for one of the authors.

Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors