On Prediction Feature Assignment in the Heckman Selection Model

arXiv:2309.08043v2 Announce Type: replace
Abstract: Under missing-not-at-random (MNAR) sample selection bias, the performance of a prediction model is often degraded. This paper focuses on one classic instance of MNAR sample selection bias where a subset of samples have non-randomly missing outcomes. The Heckman selection model and its variants have commonly been used to handle this type of sample selection bias. The Heckman model uses two separate equations to model the prediction and selection of samples, where the selection features include …

abstract arxiv bias cs.lg feature instance paper performance prediction random sample samples stat.me type variants