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
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