  Contextual bandits often provide simple and effective personalization in
decision making problems, making them popular tools to deliver personalized
interventions in mobile health as well as other health applications. However,
when bandits are deployed in the context of a scientific study -- e.g. a
clinical trial to test if a mobile health intervention is effective -- 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. It is essential to assess the effectiveness of the intervention
before broader deployment for better resource allocation. The two objectives
are often deployed under different model assumptions, making it hard to
determine how achieving the personalization and statistical power affect each
other. In this work, we develop general meta-algorithms to modify existing
algorithms such that sufficient power is guaranteed while still improving each
user's well-being. We also demonstrate that our meta-algorithms are robust to
various model mis-specifications possibly appearing in statistical studies,
thus providing a valuable tool to study designers.
