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

• Autores: Barbara Guardabascio, Marco Ventura
• Localización: The Stata journal, ISSN 1536-867X, Vol. 14, Nº. 1, 2014, págs. 141-158
• Idioma: inglés
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• Resumen

  • 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.