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Covariance Adjustments for the Analysis of Randomized Field Experiments

Author

Listed:
  • Richard Berk
  • Emil Pitkin
  • Lawrence Brown
  • Andreas Buja
  • Edward George
  • Linda Zhao

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.

Suggested Citation

  • Richard Berk & Emil Pitkin & Lawrence Brown & Andreas Buja & Edward George & Linda Zhao, 2013. "Covariance Adjustments for the Analysis of Randomized Field Experiments," Evaluation Review, , vol. 37(3-4), pages 170-196, June.
    • Handle: RePEc:sae:evarev:v:37:y:2013:i:3-4:p:170-196
    DOI: 10.1177/0193841X13513025
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    References listed on IDEAS

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    1. Heller, Ruth & Rosenbaum, Paul R. & Small, Dylan S., 2009. "Split Samples and Design Sensitivity in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1090-1101.
    2. Luke W. Miratrix & Jasjeet S. Sekhon & Bin Yu, 2013. "Adjusting treatment effect estimates by post-stratification in randomized experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 369-396, March.
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    Cited by:

    1. Matt Taddy & Matt Gardner & Liyun Chen & David Draper, 2016. "A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 661-672, October.

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