Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
Susan Murphy is Mallinckrodt Professor of Statistics and of Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University. Her research focuses on improving sequential, individualized, decision-making in health, in particular, clinical trial design and data analysis to inform the development of just-in-time adaptive interventions in digital health. She developed the micro-randomized trial for use in constructing digital health interventions; this trial design is in use across a broad range of health-related areas. Her lab works on online learning algorithms for developing personalized digital health interventions. She is a 2013 MacArthur Fellow, and a member of the National Academy of Sciences and the National Academy of Medicine, both of the US National Academies.