Robust Bayesian Learning for Individualized Treatment Rules under Unmeasured Confounding

Music City Center 

Data-driven personalized decision-making has become increasingly important in recent statistical research. Existing methods often rely on the assumption of no unmeasured confounding to establish valid causal inferences before proceeding with decision-making for identifying the optimal individualized treatment rule (ITR). However, this assumption cannot be guaranteed in practice, especially in observational studies. While additional data sources such as instrumental variables or proxies have been commonly utilized to address unmeasured confounding, such information is not always available. In this work, we develop a novel Bayesian approach for robustly learning the optimal ITR under unmeasured confounding. We propose a Bayesian joint model for continuous outcome and treatment, accounting for observed covariates and unmeasured confounders. We prove that the proposed joint model achieves unique causal identification under certain mild distributional assumptions, without requiring additional data sources. Through simulation studies and an application to a large-scale kidney transplantation database, we demonstrate the identifiability, utility, and robustness of the proposed method.

Causal identification

Conservative policy optimization

Individualized treatment rules

Precision medicine

Unmeasured confounding 

Biometrics Section