博士生讨论班2025[30]
时间：2025-12-25
阅读量：89次
- 演讲人： 陈天玏
- 时间：2025年12月30日15:30
- 地点：浙江大学紫金港校区行政楼1417报告厅
报告文章：Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding
摘要：Data-driven individualized decision making has recently received increasing research interest. However, most existing methods rely on the assumption of no unmeasured confounding, which cannot be ensured in practice especially in observational studies. Motivated by the recently proposed proximal causal inference, we develop several proximal learning methods to estimate optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. Explicitly, in terms of two types of proxy variables, we are able to establish several identification results for different classes of ITRs respectively, exhibiting the trade-off between the risk of making untestable assumptions and the potential improvement of the value function in decision making. Based on these identification results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and establish their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via extensive simulation experiments and a real data application.