Policy learning under constraint: Maximizing a primary outcome while controlling an adverse event
约束下的政策学习:最大化主要结果并控制不良事件
A medical policy aims to support decision-making by mapping patient characteristics to individualized treatment recommendations. Standard approaches typically optimize a single outcome criterion. For example, recommending treatment according to the sign of the Conditional Average Treatment Effect (CATE) maximizes the policy "value" by exploiting treatment effect heterogeneity. This point of view shifts policy learning towards the challenge of learning a reliable CATE…