A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
Statistical methods in medical research , volume=
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Framework for federated learning with missing data that identifies conditions favoring complete-case estimators over inverse-probability weighting and proposes a calibrated weighting approach consistent if at least one candidate model is correct.
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In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
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Federated Learning with Incomplete Data: When to Use Complete Cases and When to Weight
Framework for federated learning with missing data that identifies conditions favoring complete-case estimators over inverse-probability weighting and proposes a calibrated weighting approach consistent if at least one candidate model is correct.
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