Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting
A new arXiv paper demonstrates that standard importance weighting fails to correct for selection bias in machine learning when selection depends on unobservables correlated with outcomes. The authors adapt Heckman's 1979…