Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
citing papers explorer
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.