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.
Prominent roles of conditionally invariant components in domain adaptation: Theory and algorithms.arXiv preprint arXiv:2309.10301, 2023
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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.