Safe and Interpretable Estimation of Optimal Treatment Regimes

arXiv:2310.15333v2 Announce Type: replace
Abstract: Recent statistical and reinforcement learning methods have significantly advanced patient care strategies. However, these approaches face substantial challenges in high-stakes contexts, including missing data, inherent stochasticity, and the critical requirements for interpretability and patient safety. Our work operationalizes a safe and interpretable framework to identify optimal treatment regimes. This approach involves matching patients with similar medical and pharmacological characteristics, allowing us to construct an optimal policy via interpolation. We perform a comprehensive simulation study to …

abstract advanced arxiv challenges cs.lg data face framework however identify interpretability patient patient care reinforcement reinforcement learning requirements safe safety stat.ap statistical stat.me strategies treatment type work