Deep propensity network using a sparse autoencoder for estimation of treatment effects

ConclusionDeep sparse autoencoders are particularly suited for treatment effect estimation studies using electronic health records because they can handle high-dimensional covariate sets, large sample sizes, and complex heterogeneity in treatment assignments.
Source: Journal of the American Medical Informatics Association - Category: Information Technology Source Type: research