DSL uses doubly robust pseudo-outcomes and a multi-output neural network to jointly estimate time-varying conditional average treatment effects for right-censored survival data.
Evaluating meta-learners to analyze treatment heterogeneity in survival data: Application to electronic health records of pediatric asthma care in covid-19 pandemic
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Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
DSL uses doubly robust pseudo-outcomes and a multi-output neural network to jointly estimate time-varying conditional average treatment effects for right-censored survival data.