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A Stata package for the estimation of the dose-response function when the treatment is multidimensional

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  • Enrico Cristofoletti

Abstract

Propensity score methods are wildly used techniques for the evaluation of causal effects in observational studies. Although Rosenbaum and Rubin's (1983) original article focused solely on binary treatments, further studies generalize the methods to multi-valued treatments, continuous treatments, and multidimensional continuous treatments. Despite its potential, Stata offers plenty of packages for all the cases but the last one. This paper aims to introduce a new Stata package – GPSMD – that implements the propensity score generalization to multidimensional continuous treatment developed by Egger and von Ehrlich (2013). The article illustrates the econometric framework and presents the commands implemented. We finally go through a simple working example to show the commands and the capability of the method to overcome bias.

Suggested Citation

  • Enrico Cristofoletti, 2021. "A Stata package for the estimation of the dose-response function when the treatment is multidimensional," DEM Working Papers 2021/07, Department of Economics and Management.
    • Handle: RePEc:trn:utwprg:2021/07
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    References listed on IDEAS

    as
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    2. Egger, Peter H. & von Ehrlich, Maximilian, 2013. "Generalized propensity scores for multiple continuous treatment variables," Economics Letters, Elsevier, vol. 119(1), pages 32-34.
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    5. Michela Bia & Carlos A. Flores & Alfonso Flores-Lagunes & Alessandra Mattei, 2014. "A Stata package for the application of semiparametric estimators of dose–response functions," Stata Journal, StataCorp LP, vol. 14(3), pages 580-604, September.
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