Resumen de Estimating the dose-response function through a generalized linear model approach
Barbara Guardabascio, Marco Ventura
Abstract. In this article, we revise the estimation of the dose–response function described in Hirano and Imbens (2004, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 73–84) by proposing a flexible way to estimate the generalized propensity score when the treatment variable is not necessarily normally distributed. We also provide a set of programs that accomplish this task. To do this, in the existing doseresponse program (Bia and Mattei, 2008, Stata Journal 8: 354–373), we substitute the maximum likelihood estimator in the first step of the computation with the more flexible generalized linear model.
