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

Liang Jian, Oliver Linton, Haihan Tang and Yichong Zhang

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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.

Keywords: Covariate-adaptive randomization; High-dimensional data; Local average treatment effects; Randomized experiment; Regression adjustment (search for similar items in EconPapers)
JEL-codes: C14 C21 I21 (search for similar items in EconPapers)
Date: 2023-10-25
New Economics Papers: this item is included in nep-exp
Note: obl20
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Related works:
Working Paper: Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance (2023)
Working Paper: Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2366

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