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 in nature, so focus is on estimating a continuous dose-response function. In this article we propose a set of Stata programs to semiparametrically estimate the dose-response function of a continuous treatment, under the key assumption that adjusting for pre-treatment variables removes all biases (uncounfoundedness). We focus on kernel methods and penalized spline models, and use generalized propensity score methods under continuous treatment regimes for covariate adjustment. Our Stata programs use generalized linear models for estimating the generalized propensity score, allowing users to choose among alternative parametric assumptions. They also allow users to impose a common support condition and evaluate the balancing 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 a data set collected by Imbens et al. (2001, American Economic Review, 778-794).