Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding

• Activity Number: 75 - Invited EPoster Session II
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 9:35 PM to 10:30 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Zhengling Qi and Rui Miao and Xiaoke Zhang*
• Companies: George Washington University and George Washington University and George Washington University
• Keywords: Proximal causal inference; Endogeneity; Treatment regime identification ; Double robustness

Abstract

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 recently 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 a real data application.