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.
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Machine learning framework predicts fetal birth weight using parental factors in low-resource settings.
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Parental Imprints On Birth Weight: A Data-Driven Model For Neonatal Prediction In Low Resource Prenatal Care
Machine learning framework predicts fetal birth weight using parental factors in low-resource settings.