Estimation of multivalued treatment effects under conditional independence

This article discusses the poparms command that implements two semiparametric estimators for multivalued treatment effects discussed in Cattaneo (2010). The first is a properly reweighted inverse-probability weighted estimator and the second is an efficient-influence-function estimator which can be interpreted as having the double-robust property. Our implementation jointly estimates means and quantiles of the potential-outcome distributions allowing for multiple, discrete treatment levels. These estimators are then used to estimate a variety of multivalued treatment effects. We discuss preand postestimation approaches that can be used in conjunction with our main implementation. We illustrate the program and provide a simulation study assessing the finite-sample performance of the inference procedures.