Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding
Abstract
Data-driven individualized decision making has recently received increasing research interest. However, most existing methods rely on the assumption of no unmeasured confounding, which cannot be ensured in practice especially in observational studies. Motivated by the recently proposed proximal causal inference, we develop several proximal learning methods to estimate optimal individualized treatment regimes (ITRs) in the presence of unmeasured...
Paper Details
Title
Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding
Published Date
Jan 11, 2023
Pages
1 - 14
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