In linear regression for supervised domain adaptation, causal invariance yields finite-sample gains only when target-risk margins exceed estimation error, with matching upper and lower bounds derived and connected to structural shifts.
Domain generalization and adaptation in intensive care with anchor regression.arXiv preprint arXiv:2507.21783, 2025
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A Neyman-orthogonal estimator for risk heterogeneity between groups is consistent and asymptotically normal, reduces finite-sample bias relative to likelihood methods in simulations, and identifies ethnicity-specific effects in eICU mortality data that standard approaches miss.
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How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
In linear regression for supervised domain adaptation, causal invariance yields finite-sample gains only when target-risk margins exceed estimation error, with matching upper and lower bounds derived and connected to structural shifts.
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Robust inference for risk heterogeneity under group imbalance
A Neyman-orthogonal estimator for risk heterogeneity between groups is consistent and asymptotically normal, reduces finite-sample bias relative to likelihood methods in simulations, and identifies ethnicity-specific effects in eICU mortality data that standard approaches miss.