  Interpretable policy learning seeks to estimate intelligible decision
policies from observed actions; however, existing models force a tradeoff
between accuracy and interpretability, limiting data-driven interpretations of
human decision-making processes. Fundamentally, existing approaches are
burdened by this tradeoff because they represent the underlying decision
process as a universal policy, when in fact human decisions are dynamic and can
change drastically under different contexts. Thus, we develop Contextualized
Policy Recovery (CPR), which re-frames the problem of modeling complex decision
processes as a multi-task learning problem, where each context poses a unique
task and complex decision policies can be constructed piece-wise from many
simple context-specific policies. CPR models each context-specific policy as a
linear map, and generates new policy models $\textit{on-demand}$ as contexts
are updated with new observations. We provide two flavors of the CPR framework:
one focusing on exact local interpretability, and one retaining full global
interpretability. We assess CPR through studies on simulated and real data,
achieving state-of-the-art performance on predicting antibiotic prescription in
intensive care units ($+22\%$ AUROC vs. previous SOTA) and predicting MRI
prescription for Alzheimer's patients ($+7.7\%$ AUROC vs. previous SOTA). With
this improvement, CPR closes the accuracy gap between interpretable and
black-box methods, allowing high-resolution exploration and analysis of
context-specific decision models.
