Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
Understanding how a prediction model will perform in a new environment before deployment is essential to preventing harm when algorithms inform decision-making. Two common sources of model performance degradation are (i) covariate shift, where the target covariate distribution differs from the source, and (ii) selective labels, where the observability of outcomes depends on historical decisions. We study pre-deployment model evaluation under the joint presence of covariate shift and labeling of outcomes selectively based on observed features. In particular, we present a double machine learning procedure for estimating the target risk of an arbitrary black-box prediction model under a gene...
| Authors | Annie Ulichney, Amanda Coston |
|---|---|
| Primary category | stat.ML |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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