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A Stata package for the application of semiparametric estimators of dose-response functions

Michela Bia (), FLORES Carlos A., Alfonso Flores-Lagunes () and Alessandra Mattei

No 2013-07, LISER Working Paper Series from LISER

Abstract: In many observational studies the treatment may not be binary or categorical, but rather continuous in nature, so focus is on estimating a continuous dose-response function. In this paper we propose a set of Stata programs to semiparametrically estimate the dose-response function of a continuous treatment, under the key assumption that adjusting for pre-treatment variables removes all biases (uncounfoundedness). We focus on kernel methods and penalized spline models, and use generalized propensity score methods under continuous treatment regimes for covariate adjustment. Several alternative parametric assumptions on the functional form of the generalized propensity score are implemented in our Stata programs, which also allow users to impose a common support condition and evaluate the balancing of the covariates using various approaches. We illustrate our routines by estimating the effect of the prize amount on subsequent labor earnings for Massachusetts lottery winners, using a data set collected by Imbens et al. (2001).

Keywords: dose-response function; generalized propensity score; kernel estimator; penalized spline estimator; weak unconfoundedness (search for similar items in EconPapers)
JEL-codes: C13 J31 J70 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2013-04
New Economics Papers: this item is included in nep-dcm
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Related works:
Journal Article: A Stata package for the application of semiparametric estimators of dose-response functions (2014) Downloads
Journal Article: A Stata package for the application of semiparametric estimators of dose–response functions (2014) Downloads
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