Inferential Evaluation of Surrogate-Derived Models under Covariate Shift
In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are unobserved. Evaluating its target performance is essential for determining whether decisions based on the model remain reliable, yet it is difficult when gold labels are scarce, and covariate distributions differ across data sources. We study a three-sample setting with a small gold-labeled source, a larger surrogate-labeled source, and an unlabeled target. Under conditional transportability, we evaluate the surrogate-derived model against the latent gold-standard outcome in the target population. We propose cross-fitted estimators that tran...
| Authors | Longtian Shi, Molei Liu, Doudou Zhou |
|---|---|
| Primary category | stat.ML |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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