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Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance

Author

Listed:
  • Liang Jiang

    (International School of Finance, Fudan University)

  • Oliver B. Linton

    (University of Cambridge)

  • Haihan Tang

    (International School of Finance, Fudan University)

  • Yichong Zhang

    (Singapore Management University)

Abstract

We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probabilities of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.

Suggested Citation

  • Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2026. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," The Review of Economics and Statistics, MIT Press, vol. 108(3), pages 774-791, May.
    • Handle: RePEc:tpr:restat:v:108:y:2026:i:3:p:774-791
    DOI: 10.1162/rest_a_01417
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