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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
Marco Ventura
Istat, Italian National Institute of Statistics
Rome, Italy
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.
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View all articles with these keywords: glmgpscore, glmdose, generalized propensity score, generalized linear model, dose–response, continuous treatment, bias removal

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