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Balancing tests in stratified randomized controlled trials: A cautionary note

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  • Firpo, Sergio
  • Foguel, Miguel N.
  • Jales, Hugo

Abstract

We study the properties of imbalances tests of predetermined characteristics (covariates) in stratified randomized controlled trials. When the parameter of interest is a weighted average of the causal effects at all strata, then a test statistic based on a fixed effects regression model may suffice to detect the relevant imbalances. This will be true under a condition on the partial correlation between covariates and the outcome variable (uniformity condition). If that condition is violated, then the test based on the fixed effects regression will lack power. In that case, a test based on the fully saturated regression model can overcome this problem. Finally, we provide an omnibus test for the relevance of controlling for the covariates when estimating the average treatment effect (ATE).

Suggested Citation

  • Firpo, Sergio & Foguel, Miguel N. & Jales, Hugo, 2020. "Balancing tests in stratified randomized controlled trials: A cautionary note," Economics Letters, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:ecolet:v:186:y:2020:i:c:s0165176519303878
    DOI: 10.1016/j.econlet.2019.108771
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    References listed on IDEAS

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    1. Bruno Crépon & Esther Duflo & Marc Gurgand & Roland Rathelot & Philippe Zamora, 2013. "Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(2), pages 531-580.
    2. 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.
    3. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    4. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    6. Krueger, Alan B & Whitmore, Diane M, 2001. "The Effect of Attending a Small Class in the Early Grades on College-Test Taking and Middle School Test Results: Evidence from Project STAR," Economic Journal, Royal Economic Society, vol. 111(468), pages 1-28, January.
    7. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
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    Cited by:

    1. Brandon Genetin & Joyce Chen & Vladimir Kogan & Alan Kalish, 2022. "Mitigating implicit bias in student evaluations: A randomized intervention," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(1), pages 110-128, March.

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