Three in One: a Unified Estimator of Average Treatment Effects

Professor at Department of Economics, University of California, San Diego. He earned his PhD of Economics from Yale Univerisity. His research fields include Econometric Theory and Applied Econometrics. He has published several articles in Econometrica, Journal of Econometrics,  Review of Economics and Statistics , and other top academic journals.

Please see Prof. Sun's CV for more information.

We consider estimating average treatment effects under the unconfoundedness and overlap assumptions. We introduce a method to integrate the three conventional estimators --- the regression adjustment estimator, the inverse probability weighting (IPW) estimator, and the doubly robust (DR) estimator --- into a single three-in-one (TIO) estimator. The TIO estimator is all three of them: it is a regression estimator, an IPW estimator, and a DR estimator. From an empirical point of view, this removes the burden from having to choose one of the three estimators. In finite samples, the TIO estimator outperforms the three conventional estimators.