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Matching Estimators with Few Treated and Many Control Observations

Ferman, Bruno (2017): Matching Estimators with Few Treated and Many Control Observations.

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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.

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