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The Stata Journal
Volume 14 Number 1: pp. 141-158



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Estimating the dose-response function through a generalized linear model approach

Barbara Guardabascio
Istat, Italian National Institute of Statistics
Rome, Italy
guardabascio@istat.it
Marco Ventura
Istat, Italian National Institute of Statistics
Rome, Italy
mventura@istat.it
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.

View all articles by these authors: Barbara Guardabascio, Marco Ventura

View all articles with these keywords: glmgpscore, glmdose, generalized propensity score, generalized linear model, dose–response, continuous treatment, bias removal

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