Preprint
Jul 2026
Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting
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. E...
Gunner Levi Howe
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