Abstract: Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models fall short by forcing 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 _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 which retains full global interpretability.We assess CPR through studies on simulated and real data, achieving state-of-the-art performance on two standard benchmarks in medical imitation learning: 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 in predictive performance, CPR closes the accuracy gap between interpretable and black-box methods for policy learning, allowing high-resolution exploration and analysis of context-specific decision models.