A Stata package for the application of semiparametric estimators of dose-response functions
Michela Bia
CEPS/INSTEAD
Esch-Sur-Alzette, Luxembourg
[email protected]
|
Carlos A. Flores
Department of Economics
California Polytechnic State University
San Luis Obispo, CA
[email protected]
|
Alfonso Flores-Lagunes
Department of Economics
State University of New York, Binghamton
Binghamton, NY
[email protected]
|
Alessandra Mattei
Department of Statistics, Informatics, Applications ``Giuseppe Parenti''
University of Florence
Florence, Italy
[email protected]
|
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).
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
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
