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Design-Based Covariate Adjustments in Paired Experiments

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  • Edward Wu
  • Johann A. Gagnon-Bartsch

Abstract

In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. The resulting treatment and control groups should be well-balanced; however, there may still be small chance imbalances. Building on work for completely randomized experiments, we propose a design-based method to adjust for covariate imbalances in paired experiments. We leave out each pair and impute its potential outcomes using any prediction algorithm such as lasso or random forests. This method addresses a unique trade-off that exists for paired experiments. By addressing this trade-off, the method has the potential to improve precision over existing methods.

Suggested Citation

  • Edward Wu & Johann A. Gagnon-Bartsch, 2021. "Design-Based Covariate Adjustments in Paired Experiments," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 109-132, February.
    • Handle: RePEc:sae:jedbes:v:46:y:2021:i:1:p:109-132
    DOI: 10.3102/1076998620941469
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Small, Dylan S. & Ten Have, Thomas R. & Rosenbaum, Paul R., 2008. "Randomization Inference in a GroupRandomized Trial of Treatments for Depression: Covariate Adjustment, Noncompliance, and Quantile Effects," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 271-279, March.
    3. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    4. Zhenke Wu & Constantine E. Frangakis & Thomas A. Louis & Daniel O. Scharfstein, 2014. "Estimation of treatment effects in matched-pair cluster randomized trials by calibrating covariate imbalance between clusters," Biometrics, The International Biometric Society, vol. 70(4), pages 1014-1022, December.
    5. Xinran Li & Peng Ding, 2020. "Rerandomization and regression adjustment," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 241-268, February.
    6. Colin B Fogarty, 2018. "Regression-assisted inference for the average treatment effect in paired experiments," Biometrika, Biometrika Trust, vol. 105(4), pages 994-1000.
    7. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
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    Cited by:

    1. Bai, Yuehao & Jiang, Liang & Romano, Joseph P. & Shaikh, Azeem M. & Zhang, Yichong, 2024. "Covariate adjustment in experiments with matched pairs," Journal of Econometrics, Elsevier, vol. 241(1).

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