Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data

53 Pages Posted: 15 Oct 2009

See all articles by Carlos A. Flores

Carlos A. Flores

University of Miami

Oscar A. Mitnik

Inter-American Development Bank; IZA Institute of Labor Economics

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.

Keywords: multiple treatments, nonexperimental estimators, generalized propensity score

JEL Classification: C13, C14, C21

Suggested Citation

Flores, Carlos A. and Mitnik, Oscar A., Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data. IZA Discussion Paper No. 4451, Available at SSRN: https://ssrn.com/abstract=1489274 or http://dx.doi.org/10.2139/ssrn.1489274

Carlos A. Flores (Contact Author)

University of Miami ( email )

Coral Gables, FL 33124
United States

Oscar A. Mitnik

Inter-American Development Bank ( email )

1300 New York Ave, NW
Washington, DC 20577
United States

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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