A two-stage causal estimator for semi-continuous exposures that disentangles exposure status and dose via a two-part propensity score in a marginal structural model.
The central role of the propensity score in observational studies for causal effects
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Two-stage Estimation for Causal Inference Involving a Semi-continuous Exposure
A two-stage causal estimator for semi-continuous exposures that disentangles exposure status and dose via a two-part propensity score in a marginal structural model.
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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.