Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction
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. Despite its theoretical appeal, this strategy can underperform empirical risk minimization when only a subset of the causal parents of the outcome is observed. In such settings, non-causal covariates can serve as proxies for unobserved causal parents and improve prediction when the proxy relationship is stable, but they can hurt when shifts disrupt that relationship. T
The paper addresses a fundamental limitation in current out-of-distribution prediction methods, which is a significant focus in advanced AI research as models are deployed in increasingly varied real-world scenarios.
This research could lead to more robust and reliable AI systems, especially those operating in dynamic environments where novel data distributions are common, thus impacting areas from autonomous driving to medical diagnostics.
The understanding of how to leverage non-causal covariates for improved prediction under specific, stable conditions, moving beyond a sole reliance on purely causal features.
- · AI researchers
- · ML model developers
- · Industries deploying AI in complex environments (e.g., healthcare, autonomous sy
- · Systems solely relying on naive causal inference approaches
- · Models that are brittle to environmental shifts
Improved performance of AI models in real-world, dynamic settings.
Increased trust and adoption of AI in critical applications where robustness to distribution shifts is paramount.
Accelerated development of AI agents capable of more sophisticated adaptation to new environments, potentially impacting white-collar automation.
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Read at arXiv cs.LG