Past talk · AI/ML Seminar Series
Causal inference and machine learning in mobile health: Modeling time-varying effects using longitudinal functional data
Assistant Professor of Statistics, University of California, Irvine
- Date & time
- Monday, October 21, 2024 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
To optimize mobile health interventions and advance domain knowledge on intervention design, it is critical to understand how the intervention effect varies over time and with contextual information. This study aims to assess how a push notification suggesting physical activity influences individuals' step counts using data from the HeartSteps micro-randomized trial (MRT). We propose the first semiparametric causal excursion effect model with varying coefficients to model the time-varying effects within a decision point and across decision points in an MRT. We propose a two-stage causal effect estimator that uses machine learning and is robust against a misspecified high-dimensional outcome regression nuisance model. Our analysis provides new insights into individuals' change in response profiles due to the activity suggestions.
About the speaker
Tianchen Qian is an Assistant Professor in Statistics at UC Irvine. His research focuses on leveraging data science, mobile technology, and wearable devices to design robust, personalized, and cost-effective interventions that can impact health and well-being at a significant scale. He also works on causal inference, experimental design, machine learning, semiparametric efficiency theory, and longitudinal data methods. He has a PhD in Biostatistics from Johns Hopkins University. Before joining UCI, he was a postdoc fellow in Statistics at Harvard University.