Covariance Adjustments for the Analysis of Randomized Field Experiments

Richard Berk, Emil Pitkin, Lawrence Brown, Andreas Buja, Edward George and Linda Zhao

Evaluation Review, 2013, vol. 37, issue 3-4, 170-196

Abstract: Background: It has become common practice to analyze randomized experiments using linear regression with covariates. Improved precision of treatment effect estimates is the usual motivation. In a series of important articles, David Freedman showed that this approach can be badly flawed. Recent work by Winston Lin offers partial remedies, but important problems remain. Results: In this article, we address those problems through a reformulation of the Neyman causal model. We provide a practical estimator and valid standard errors for the average treatment effect. Proper generalizations to well-defined populations can follow. Conclusion: In most applications, the use of covariates to improve precision is not worth the trouble.

Keywords: randomized field experiments; covariate adjustments; Neyman causal model. (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:37:y:2013:i:3-4:p:170-196

DOI: 10.1177/0193841X13513025

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