Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish several identification results for different classes of ITRs, exhibiting the trade-off between the risk of making untestable assumptions and the value function improvement in decision making. Based on these results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and develop their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via an extensive simulation study and one real data application.
翻译:以数据为驱动的个别决策最近引起了越来越多的研究兴趣,大多数现有方法都依赖于假定没有无法衡量的混乱,遗憾的是,这在实际中特别是在观察研究中是无法确保的。根据最近提出的近似因果推论,我们制定了几种最初步的学习方法,以估计最佳个别处理制度(ITRs),而同时又有未经测定的个别处理制度(ITRs),特别是,我们为不同类别的ITRs确定了若干识别结果,显示了作出无法检验的假设的风险与决策的增值功能的改进之间的权衡。根据这些结果,我们提出了几种基于分类的方法,以寻找各种受限制的类内最佳ITRs,并发展其理论特性。我们拟议方法的具有吸引力的数字性表现通过广泛的模拟研究和一个实际数据应用来证明。