Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging

ArXi:2604.17694v1 Announce Type: cross Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stability via a concentration condition and prove that subbagging guarantees stability for any bounded-outcome regression algorithm. We