Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?
Journal:
BMC medical research methodology
PMID:
39095707
Abstract
PURPOSE: Propensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsupervised autoencoders for propensity score estimation, comparing them with traditional methods for bias and variance accuracy in treatment effect estimations.