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

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).


Issue Date:
2014-2014-2014
Publication Type:
Journal Article
ISSN:
1536-8634
Language:
English
Published in:
Stata Journal, Volume 14, Number 3
Page range:
580-604

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 Record created 2017-10-27, last modified 2017-10-27

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