Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
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ConfoundingSHAP defines a custom Shapley game to attribute confounding strength to individual covariates and uses TabPFN to estimate it scalably without exhaustive refitting.
citing papers explorer
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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.
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ConfoundingSHAP: Quantifying confounding strength in causal inference
ConfoundingSHAP defines a custom Shapley game to attribute confounding strength to individual covariates and uses TabPFN to estimate it scalably without exhaustive refitting.