A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

Abstract We derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

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Year of publication:	2013
Authors:	Aronow, Peter M. ; Middleton, Joel A.
Published in:	Journal of Causal Inference. - De Gruyter, ISSN 2193-3685, ZDB-ID 2742570-8. - Vol. 1.2013, 1, p. 135-154
Publisher:	De Gruyter
Subject:	randomized experiments \| unbiased estimation \| average treatment effect \| sampling theory

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Type of publication:	Article
Type of publication (narrower categories):	research-article
Language:	English
Other identifiers:	10.1515/jci-2012-0009 [DOI]
Source:	Other ZBW resources

Persistent link: https://www.econbiz.de/10014610789