Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting
arXiv:2607.05806v1 Announce Type: new Abstract: Training data for machine learning is routinely collected by a selection process the model never sees: loans are observed only when granted, outcomes only when a test was ordered. The standard fixes -- importance weighting, covariate-shift correction, MAR imputation -- assume selection is ignorable given observables. Econometrics solved the harder case in 1979: Heckman's two-equation model jointly fits a probit selection equation and an outcome equation linked through correlated errors, and the inverse-Mills-ratio term corrects for selection on u
This paper addresses a fundamental and recognized challenge in machine learning, offering a novel econometric solution (Heckman correction) to a long-standing issue of selection bias.
Improving how AI models handle selection bias significantly enhances their reliability and fairness, especially in sensitive applications relying on incomplete or biased training data.
The proposed Heckman-corrected epistemic uncertainty method offers a more robust way to mitigate selection on unobservables, potentially leading to fairer and more accurate AI systems in domains where data collection is inherently biased.
- · AI developers
- · Econometricians
- · Sectors with biased observational data (e.g., finance, healthcare)
- · AI models relying solely on importance weighting or covariate-shift correction
AI models will become more reliable and less susceptible to biases introduced during data collection.
Increased adoption of econometric methods in machine learning research and practical application.
More equitable and trustworthy AI systems across critical societal functions currently affected by data selection biases.
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Read at arXiv cs.LG