Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding

• 成果类型: Article
• 署名作者: Qi, Zhengling; Miao, Rui; Zhang, Xiaoke
• 署名单位: George Washington University; University of California System; University of California Irvine; George Washington University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2147841
• 发表日期: 2024
• 页码: 915-928
• 关键词: Causal Inference; treatment rules; efficient; DESIGN; models; care

摘要

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.