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Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift
We study counterfactual regression, which maps features to outcomes under hypothetical scenarios that differ from those observed in the data. This problem is central to decision-making under distribution shift, where treatment patterns may change at deployment. We develop a semiparametric framework
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