论文标题
近端因果学习的最佳治疗方案
Optimal Treatment Regimes for Proximal Causal Learning
论文作者
论文摘要
当决策者从观察数据中提取因果推断并做出决定时，一个普遍的问题是，所测量的协变量不足以说明所有混淆的来源，即，标准的无混淆假设无法持有。最近提出的近端因果推理框架表明，可以利用现实生活中的替代变量，以识别因果关系，从而促进决策。在这方面的工作基础上，我们提出了一种基于所谓的结果和治疗混淆的桥梁的新型最佳个性化治疗方案。然后，我们证明了这种新的最佳治疗方案的价值函数优于文献中现有的价值功能。建立了理论保证，包括识别，优越性，超额价值结合和估计制度的一致性。此外，我们通过数值实验和实际数据应用程序演示了所提出的最佳制度。
A common concern when a policymaker draws causal inferences from and makes decisions based on observational data is that the measured covariates are insufficiently rich to account for all sources of confounding, i.e., the standard no confoundedness assumption fails to hold. The recently proposed proximal causal inference framework shows that proxy variables that abound in real-life scenarios can be leveraged to identify causal effects and therefore facilitate decision-making. Building upon this line of work, we propose a novel optimal individualized treatment regime based on so-called outcome and treatment confounding bridges. We then show that the value function of this new optimal treatment regime is superior to that of existing ones in the literature. Theoretical guarantees, including identification, superiority, excess value bound, and consistency of the estimated regime, are established. Furthermore, we demonstrate the proposed optimal regime via numerical experiments and a real data application.
