  Personalized treatment decisions have become an integral part of modern
medicine. Thereby, the aim is to make treatment decisions based on individual
patient characteristics. Numerous methods have been developed for learning such
policies from observational data that achieve the best outcome across a certain
policy class. Yet these methods are rarely interpretable. However,
interpretability is often a prerequisite for policy learning in clinical
practice. In this paper, we propose an algorithm for interpretable off-policy
learning via hyperbox search. In particular, our policies can be represented in
disjunctive normal form (i.e., OR-of-ANDs) and are thus intelligible. We prove
a universal approximation theorem that shows that our policy class is flexible
enough to approximate any measurable function arbitrarily well. For
optimization, we develop a tailored column generation procedure within a
branch-and-bound framework. Using a simulation study, we demonstrate that our
algorithm outperforms state-of-the-art methods from interpretable off-policy
learning in terms of regret. Using real-word clinical data, we perform a user
study with actual clinical experts, who rate our policies as highly
interpretable.
