  Optimal treatment rules can improve health outcomes on average by assigning a
treatment associated with the most desirable outcome to each individual. Due to
an unknown data generation mechanism, it is appealing to use flexible models to
estimate these rules. However, such models often lead to complex and
uninterpretable rules. In this article, we introduce an approach aimed at
estimating optimal treatment rules that have higher accuracy, higher value, and
lower loss from the same simple model family. We use a flexible model to
estimate the optimal treatment rules and a simple model to derive interpretable
treatment rules. We provide an extensible definition of interpretability and
present a method that - given a class of simple models - can be used to select
a preferred model. We conduct a simulation study to evaluate the performance of
our approach compared to treatment rules obtained by fitting the same simple
model directly to observed data. The results show that our approach has lower
average loss, higher average outcome, and greater power in identifying
individuals who can benefit from the treatment. We apply our approach to derive
treatment rules of adjuvant chemotherapy in colon cancer patients using cancer
registry data. The results show that our approach has the potential to improve
treatment decisions.
