Abstract:  Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health.   However, after an 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.

Bio:  Susan Murphy’s research focuses on improving sequential, individualized, decision making 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.  Dr. Murphy is a member of the National Academy of Sciences and of the National Academy of Medicine, both of the US National Academies.  In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making.  She is a Fellow of the College on Problems in Drug Dependence, Past-President of Institute of Mathematical Statistics, Past-President of the Bernoulli Society and a former editor of the Annals of Statistics.