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The Stata Journal
Volume 14 Number 3: pp. 580-604

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

Michela Bia
Esch-Sur-Alzette, Luxembourg
Carlos A. Flores
Department of Economics
California Polytechnic State University
San Luis Obispo, CA
Alfonso Flores-Lagunes
Department of Economics
State University of New York, Binghamton
Binghamton, NY
Alessandra Mattei
Department of Statistics, Informatics, Applications ``Giuseppe Parenti''
University of Florence
Florence, Italy
Abstract.  In many observational studies, the treatment may not be binary or categorical but rather continuous, so the focus is on estimating a continuous dose–response function. In this article, we propose a set of programs that semiparametrically estimate the dose–response function of a continuous treatment under the unconfoundedness assumption. We focus on kernel methods and penalized spline models and use generalized propensity-score methods under continuous treatment regimes for covariate adjustment. Our programs use generalized linear models to estimate the generalized propensity score, allowing users to choose between alternative parametric assumptions. They also allow users to impose a common support condition and evaluate the balance 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 data collected by Imbens, Rubin, and Sacerdote (2001, American Economic Review, 778–794).
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View all articles by these authors: Michela Bia, Carlos A. Flores, Alfonso Flores-Lagunes, Alessandra Mattei

View all articles with these keywords: drf, dose–response function, generalized propensity score, kernel estimator, penalized spline estimator, weak unconfoundedness

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