Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
Almodóvar et al.[2] propose a deconfounding normalizing flow that, applied to proximal settings, implicitly solves the bridge equation
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Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.