Matching Estimators with Few Treated and Many Control Observations

Bruno Ferman

MPRA Paper from University Library of Munich, Germany

Abstract: We analyze the properties of matching estimators when the number of treated observations is fixed while the number of treated observations is large. We show that, under standard assumptions, the nearest neighbor matching estimator for the average treatment effect on the treated is asymptotically unbiased, even though this estimator is not consistent. We also provide a test based on the theory of randomization tests under approximate symmetry developed in Canay et al. (2014) that is asymptotically valid when the number of control observations goes to infinity. This is important because large sample inferential techniques developed in Abadie and Imbens (2006) would not be valid in this setting.

Keywords: matching estimator; treatment effect; hypothesis testing; randomization inference (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 (search for similar items in EconPapers)
Date: 2017-05-04
New Economics Papers: this item is included in nep-ecm
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https://mpra.ub.uni-muenchen.de/78940/3/MPRA_paper_78940.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/79508/1/MPRA_paper_79508.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/85013/1/MPRA_paper_85013.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/89212/1/MPRA_paper_89212.pdf revised version (application/pdf)

Related works:
Journal Article: Matching estimators with few treated and many control observations (2021)
Working Paper: Matching Estimators with Few Treated and Many Control Observations (2021)
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