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Covariance Adjustments for the Analysis of Randomized Field Experiments.
MedLine Citation:
PMID:  24647925     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Richard Berk; Emil Pitkin; Lawrence Brown; Andreas Buja; Edward George; Linda Zhao
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-3-18
Journal Detail:
Title:  Evaluation review     Volume:  -     ISSN:  1552-3926     ISO Abbreviation:  Eval Rev     Publication Date:  2014 Mar 
Date Detail:
Created Date:  2014-3-20     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8004942     Medline TA:  Eval Rev     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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