Abstract

The blopmatching estimator for average treatment effects in observational studies is a nonparametric matching estimator proposed by Diaz, Rau, and Rivera (2015, Review of Economics and Statistics 97: 803-812). This approach uses the solutions of linear programming problems to build the weighting schemes that are used to impute the missing potential outcomes. In this article, we describe blopmatch, a new command that implements these estimators.