treatrew: A user-written Stata routine for estimating average treatment effects by reweighting on propensity score

Reweighting is a popular statistical technique to deal with inference in presence of a nonrandom sample. In the literature, various reweighting estimators have been proposed. This paper presents the author-written Stata routine treatrew, which implements the reweighting on the propensity-score estimator as proposed by Rosenbaum and Rubin (1983) in their seminal article, where they show that parameters’ standard errors can be obtained analytically (Wooldridge 2010, 920–930) or via bootstrapping. Because an implementation in Stata of this estimator with analytic standard errors was still missing, this paper, and the ado-file and help-file accompanying it, aims at filling this gap by providing an easy-to-use implementation of the reweighting on the propensity-score method, as a valuable tool for estimating treatment-effects under “selection-on-observables†(or “overt biasâ€). Finally, a Monte Carlo experiment to check the reliability of treatrew and to compare its results with other treatment effect estimators will also be provided.