arXiv Machine Learning June 19, 2026 By Shuozhi Zuo, Yixin Wang

Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction

Read the original on arXiv Machine Learning →

arXiv:2601. 02322v2 Announce Type: replace-cross Abstract: A common approach to out-of-distribution prediction restricts models to causal or invariant covariates to avoid spurious associations that may change across environments.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

Anti-causal domain generalization: Leveraging unlabeled data

Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies

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