Susan Murphy, Harvard University
Title: Micro-Randomized Trials & Online Decision-Making Algorithms Micro-Randomized Trials & Online Decision-Making Algorithms
Abstract: A formidable challenge in designing sequential treatments in health is to determine when and in which context it is best to deliver treatments to individuals. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. Micro-randomized experiments, in which each individual is randomized many times can be use to provide data for constructing these decision rules. Further there is much interest in personalization during the experiment, that is, in real time as the individual experiences sequences of treatment. Here we discuss our work in designing online "bandit" learning algorithms for use in personalizing mobile health interventions Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in a Digital Health.