在数据不完全可识别下，学习能控制治疗风险的精准分配方案。
Learning Treatment Allocations with Risk Control Under Partial Identifiability
- 基于有限样本设计可验证的风险控制方法
- 在部分可识别条件下实现治疗风险的有效管控
- 适合关注医疗决策安全性的研究人员
在精准医学中，为患者群体学习有益的治疗分配是一项重要任务。许多治疗伴随难以量化其益处的副作用，未获益患者将承受不必要的伤害，形成‘治疗风险’。本文旨在学习有益分配的同时控制该风险。由于治疗风险在随机试验或观察数据下通常不可识别，该约束学习问题面临挑战。我们提出一种可验证的学习方法，在有限样本和部分可识别设定下控制治疗风险。方法通过模拟数据和真实数据进行了验证。
原文摘要 · Abstract (English)
Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. Many treatments come with adverse side effects that are not commensurable with their potential benefits. Patients who do not receive benefits after such treatments are thereby subjected to unnecessary harm. This is a `treatment risk' that we aim to control when learning beneficial allocations. The constrained learning problem is challenged by the fact that the treatment risk is not in general identifiable using either randomized trial or observational data. We propose a certifiable learning method that controls the treatment risk with finite samples in the partially identified setting. The method is illustrated using both simulated and real data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。