Decision-makers frequently must choose a single action from a finite set of alternatives -- for example, physicians selecting a treatment, investors choosing a portfolio risk level, or judges determining sentences. To improve outcomes, policymakers often issue policy rules or guidelines to inform such choices. In this paper, I show how to generally derive policy rules from observational data in a multi-action framework under relatively weak assumptions about the underlying structure of the heterogeneous sampled population. Conditional average treatment effects (CATEs) are consistently estimated via a weighted K-means algorithm, assuming the outcome model is correctly specified within each homogeneous subgroup. Feasible policy rules are then implemented via a standard decision tree, allowing for both perfect and imperfect adherence to treatment. The methodology is applied to treatment options for Hepatitis C (HCV) among patients co-infected with human immunodeficiency virus (HIV), a setting in which no uniform guideline exists for modern pharmaceutical therapies. The results identify a subgroup of patients with approximately an 80% probability of spontaneous HCV clearance without treatment. Estimation results also show that reallocating treatments among treated individuals could have reduced total treatment costs by CAN$3.6-4.9 million while still increasing aggregate health benefits relative to the status quo. These findings demonstrate that the proposed approach can generate improved, data-driven treatment guidelines for the management of HIV/HCV co-infected patients.

翻译：决策者经常需要从有限备选方案中选择单一行动——例如，医生选择治疗方案、投资者选择投资组合风险水平、或法官确定判决。为改善结果，政策制定者常发布策略规则或指南来指导此类选择。本文展示了在异质性抽样人群的基础结构假设相对较弱的条件下，如何从多行动框架下的观测数据中通用地推导出策略规则。通过加权K-means算法一致估计条件平均处理效应（CATE），前提是结果模型在每个同质子群内被正确设定。随后利用标准决策树实施可行的策略规则，允许对治疗有完全和部分依从性。该方法被应用于人类免疫缺陷病毒（HIV）合并感染患者中丙型肝炎（HCV）的治疗方案选择——这一场景下尚无针对现代药物疗法的统一指南。结果识别出一个约有80%概率无需治疗即可自发清除HCV的患者子群。估计结果还表明，在已接受治疗的个体间重新分配治疗，可在不降低总健康收益的情况下，使总治疗成本较现状减少360-490万加元。这些发现证明，所提出方法可为HIV/HCV合并感染患者的管理生成基于数据的改进治疗指南。