TY - JOUR
T1 - A stata package for the application of semiparametric estimators of dose–response functions
AU - Bia, Michela
AU - Flores, Carlos A.
AU - Flores-Lagunes, Alfonso
AU - Mattei, Alessandra
N1 - Publisher Copyright:
© 2014 StataCorp LP.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2014/9
Y1 - 2014/9
N2 - 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).
AB - 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).
KW - Dose–response function
KW - Drf
KW - Generalized propensity score
KW - Kernel estimator
KW - Penalized spline estimator
KW - St0352
KW - Weak unconfoundedness
UR - http://www.scopus.com/inward/record.url?scp=85001013309&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85001013309&partnerID=8YFLogxK
U2 - 10.1177/1536867x1401400307
DO - 10.1177/1536867x1401400307
M3 - Article
AN - SCOPUS:85001013309
VL - 14
SP - 580
EP - 604
JO - Stata Journal
JF - Stata Journal
SN - 1536-867X
IS - 3
M1 - st0352
ER -