Building models of human decision-making from observed behaviour is critical to better
understand, diagnose and support real-world policies such as clinical care. As established
policy learning approaches remain focused on imitation performance, they fall short of
explaining the demonstrated decision-making process. Policy Extraction through decision
Trees (POETREE) is a novel framework for interpretable policy learning, compatible with
fully-offline and partially-observable clinical decision environments--and builds probabilistic …