Deep propensity network using a sparse autoencoder for estimation of treatment effects
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DOI: 10.1093/jamia/ocaa346
Abstract: OBJECTIVE Drawing causal estimates from observational data is problematic, because datasets often contain underlying bias (eg, discrimination in treatment assignment). To examine causal effects, it is important to evaluate what-if scenarios-the so-called "counterfactuals." We propose… read more here.
Keywords: treatment; propensity network; network using; treatment effects ... See more keywords