TL;DR通过将问题重新定义为一个学习任务,而不是 m 个个别任务,我们提出了一种新的方法,基于将数据递归地分成不同治疗方案最佳的区域。我们开发了新的工具来验证和评估观察数据中的个性化模型,并在个性化医学和职业培训应用中展示了我们的新方法的优势。
Abstract
We study the problem of learning to choose from m discrete treatment options
(e.g., news item or medical drug) the one with best causal effect for a
particular instance (e.g., user or patient) where the training data consists of
passive observations of covariates, treatment, and the ou