A Large-Scale Observational Study of the Causal Effects of a Behavioral Health Nudge
AuthorsAchille Nazaret, Guillermo Sapiro
A Large-Scale Observational Study of the Causal Effects of a Behavioral Health Nudge
AuthorsAchille Nazaret, Guillermo Sapiro
This paper was accepted at the workshop “Causality for Real-world Impact” at NeurIPS 2022.
The Apple Watch encourages users to stand throughout the day by delivering a notification onto the users’ wrist if they have been sitting for the first 50 minutes of an hour. This simple behavioral intervention exemplifies the classical definition of nudge as a choice architecture that alters behavior without forbidding options or significantly changing economic incentives. In order to estimate from observational data the causal effect of the notification on the user’s standing probability through-out the day, we introduce a novel regression discontinuity design for time series data with time-varying treatment. Using over 76 billions minutes of private and anonymous observational standing data from more than 160,000 subjects enrolled in the public Apple Heart and Movement Study from 2019 to 2022, we show that the nudge increases the probability of standing by up to 49.5% across all the studied population. The nudge is similarly effective for participants self-identified as male or female, and it is more effective in older people, increasing the standing probability in people over 75 years old by more than 60%. We also demonstrate that closing Apple Watch Activity Rings, another simple choice architecture that visualizes the participant’s daily progress in Move, Exercise, and Stand, correlates with user’s response to the intervention; for users who close their activity rings regularly, the standing nudge almost triples their probability of standing. This observational study, which is one of the largest of its kind exploring the causal effects of nudges in the general population, demonstrates the effectiveness of simple behavioral health interventions and introduces a novel application of regression discontinuity design extended here to time-varying treatments.
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
December 10, 2025research area Methods and Algorithms
Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practice, many modern applications provide multiple related views of the same system, which has rarely been considered for causal discovery. Here, we leverage this multi-view structure to achieve causal discovery with weak assumptions. We propose a multi-view linear Structural Equation Model (SEM) that…
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
July 11, 2025research area Health, research area Methods and Algorithmsconference ICML
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2.5B hours of wearable…