Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning

arXiv:2310.07918v4 Announce Type: replace
Abstract: 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 …

abstract accuracy arxiv cs.ai cs.lg data data-driven decision decisions however human imitation learning interpretability making medical modeling policies policy processes recovery stat.ml type