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.

Estimating the dose-response function through a GLM approach / Guardabascio, Barbara; Ventura, Marco. - In: THE STATA JOURNAL. - ISSN 1536-867X. - 14:(2014), pp. 141-158.

Estimating the dose-response function through a GLM approach

Ventura, Marco
2014

Abstract

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.
2014
glmgpscore; glmdose; generalized propensity score; generalized linear model; dose–response; continuous treatment; bias removal
01 Pubblicazione su rivista::01a Articolo in rivista
Estimating the dose-response function through a GLM approach / Guardabascio, Barbara; Ventura, Marco. - In: THE STATA JOURNAL. - ISSN 1536-867X. - 14:(2014), pp. 141-158.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1246591
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