Estimating the dose–response function through a generalized linear model approach
Barbara Guardabascio
Istat, Italian National Institute of Statistics
Rome, Italy
[email protected]
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Marco Ventura
Istat, Italian National Institute of Statistics
Rome, Italy
[email protected]
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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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