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The Balance Permutation Test: A Machine Learning Replacement for Balance Tables

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  • Rametta, Jack T.
  • Fuller, Sam

    (Harvard University)

Abstract

Balance tests are standard for experiments despite their inability to detect randomization issues and covariate imbalance. There is no consensus on how randomization and balance should be checked, and how failures and imbalances should be addressed, if they should be addressed at all. In this article we provide clear guidelines and implement a new method, the "balance permutation test," designed to detect complex randomization failures. Our approach leverages both permutation inference and machine learning for this task. We show how the balance permutation test is able to detect complex imbalance in real, simulated, and even fabricated data. Moreover we advocate researchers employ powerful doubly-robust machine learning treatment effect estimators to improve precision and power both when randomization goes awry and in general. Lastly, we introduce an R package, MLbalance implementing our approach. We aim to resolve the debate over how to detect and adjust for randomization issues in experiments.

Suggested Citation

  • Rametta, Jack T. & Fuller, Sam, 2024. "The Balance Permutation Test: A Machine Learning Replacement for Balance Tables," OSF Preprints xcwt9_v1, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:xcwt9_v1
    DOI: 10.31219/osf.io/xcwt9_v1
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    References listed on IDEAS

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    1. John A. List & Ian Muir & Gregory Sun, 2024. "Using machine learning for efficient flexible regression adjustment in economic experiments," Econometric Reviews, Taylor & Francis Journals, vol. 44(1), pages 2-40, July.
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