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

Michela Bia, Carlos A. Flores, Alfonso Flores-Lagunes, Alessandra Mattei

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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).

Original languageEnglish (US)
Article numberst0352
Pages (from-to)580-604
Number of pages25
JournalStata Journal
Issue number3
StatePublished - Sep 2014
Externally publishedYes


  • Dose–response function
  • Drf
  • Generalized propensity score
  • Kernel estimator
  • Penalized spline estimator
  • St0352
  • Weak unconfoundedness

ASJC Scopus subject areas

  • Mathematics (miscellaneous)


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