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

Michela Bia (), Carlos A. Flores (), Alfonso Flores-Lagunes () and Alessandra Mattei ()
Additional contact information
Carlos A. Flores: California Polytechnic State University
Alessandra Mattei: University of Florence

Stata Journal, 2014, vol. 14, issue 3, 580-604

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). Copyright 2014 by StataCorp LP.

Keywords: drf; dose–response function; generalized propensity score; kernel estimator; penalized spline estimator; weak unconfoundedness (search for similar items in EconPapers)
Date: 2014
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj14-3/st0352/
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Citations: View citations in EconPapers (22)

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Working Paper: A Stata package for the application of semiparametric estimators of dose-response functions (2013)
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