Evaluating Nonexperimental Estimators For Multiple Treatments: Evidence From Experimental Data

This paper assesses the effectiveness of unconfoundedness-based estimators of mean effects for multiple or multivalued treatments in eliminating bias arising from nonrandom treatment assignment. The authors evaluate these multiple treatment estimators by simultaneously equalizing average outcomes among several control groups from a randomized experiment. They study linear regression estimators as well as partial mean and weighting estimators based on the Generalized Propensity Score (GPS). They also study the use of the GPS in assessing the comparability of individuals among the different treatment groups, and propose a strategy to determine the overlap or common support region that is less stringent than those previously used in the literature.