Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for XXXX .

@entity1 @entity131 @entity33870
UNASSIGNED: For a continuous treatment, the @entity33870 ( @entity33870 ) is defined as the conditional density of the treatment, given covariates. @entity33870 adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose-response function assumes correct specification of both the @entity33870 and the outcome-treatment relationship. This paper introduces a machine learning method, the 'Super Learner', to address model selection in this context. In the two-stage estimation approach proposed, the Super Learner selects a @entity33870 and then a dose-response function conditional on the @entity33870 , as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the @entity33870 and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for @entity1 with acute @entity131 . With parametric models for the outcome, we find that dose-response curves differ according to choice of specification. With the Super Learner approach to both regression and the @entity33870 , we find that transfer time does not have a statistically significant marginal effect on the outcomes.    2015 The Authors. Health Economics Published by John Wiley _ Sons Ltd.
