Power-Constrained Bandits

Contextual bandits often provide simple and effective personalization in decision making problems, making them popular in many domains including digital health. However, when bandits are deployed in the context of a scientific study, the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective. In this work, we develop general meta-algorithms to modify existing algorithms such that sufficient power is guaranteed, without significant decrease in average return.