Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding
This work addresses a critical limitation in data-driven decision-making for personalized treatments, particularly in observational studies where unmeasured confounding is common.
The authors tackled the problem of estimating optimal individualized treatment regimes in the presence of unmeasured confounding, developing proximal learning approaches that demonstrated appealing numerical performance in simulations and a real data application.
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.