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Data-driven individualized decision making has recently received increasingresearch interests. Most existing methods rely on the assumption of nounmeasured confounding, which unfortunately cannot be ensured in practiceespecially in observational studies. Motivated by the recent proposed proximalcausal inference, we develop several proximal learning approaches to estimatingoptimal individualized treatment regimes (ITRs) in the presence of unmeasuredconfounding. In particular, we establish several identification results fordifferent classes of ITRs, exhibiting the trade-off between the risk of makinguntestable assumptions and the value function improvement in decision making.Based on these results, we propose several classification-based approaches tofinding a variety of restricted in-class optimal ITRs and develop theirtheoretical properties. The appealing numerical performance of our proposedmethods is demonstrated via an extensive simulation study and one real dataapplication.

Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding