Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data

Carlos A. Flores () and Oscar A. Mitnik ()
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Carlos A. Flores: University of Miami
Oscar A. Mitnik: University of Miami

No 4451, IZA Discussion Papers from Institute for the Study of Labor (IZA)

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 (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-exp
Date: 2009-09

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