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View Submission - SDS2022
A0159
Title: Proximal learning for individualized treatment regimes under unmeasured confounding Authors:  Zhengling Qi - The George Washington University (United States)
Rui Miao - George Washington University (United States)
Xiaoke Zhang - George Washington University (United States) [presenting]
Abstract: Data-driven individualized decision-making has recently received increasing research interest. 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 developing their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via an extensive simulation study and a real data application.