Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/35961 
Year of Publication: 
2009
Series/Report no.: 
IZA Discussion Papers No. 4451
Publisher: 
Institute for the Study of Labor (IZA), Bonn
Abstract: 
This paper assesses the effectiveness of unconfoundedness-based estimators of mean effects for multiple or multivalued treatments in eliminating biases arising from nonrandom treatment assignment. We evaluate these multiple treatment estimators by simultaneously equalizing average outcomes among several control groups from a randomized experiment. We study linear regression estimators as well as partial mean and weighting estimators based on the generalized propensity score (GPS). We 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. Our results show that in the multiple treatment setting there may be treatment groups for which it is extremely difficult to find valid comparison groups, and that the GPS plays a significant role in identifying those groups. In such situations, the estimators we consider perform poorly. However, their performance improves considerably once attention is restricted to those treatment groups with adequate overlap quality, with difference-in-difference estimators performing the best. Our results suggest that unconfoundedness-based estimators are a valuable econometric tool for evaluating multiple treatments, as long as the overlap quality is satisfactory.
Subjects: 
Multiple treatments
nonexperimental estimators
generalized propensity score
JEL: 
C13
C14
C21
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
577.23 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.