Journal
EVALUATION REVIEW
Volume 37, Issue 3-4, Pages 170-196Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/0193841X13513025
Keywords
randomized field experiments; covariate adjustments; Neyman causal model
Categories
Funding
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1406563, 1310795] Funding Source: National Science Foundation
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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.
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