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Inference under covariate-adaptive randomization with imperfect compliance

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  • Bugni, Federico A.
  • Gao, Mengsi

Abstract

This paper studies inference in a randomized controlled trial (RCT) with covariate-adaptive randomization (CAR) and imperfect compliance of a binary treatment. In this context, we study inference on the local average treatment effect (LATE), i.e., the average treatment effect conditional on individuals that always comply with the assigned treatment. As in Bugni et al. (2018, 2019), CAR refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. In contrast to these papers, however, we allow participants of the RCT to endogenously decide to comply or not with the assigned treatment status.

Suggested Citation

  • Bugni, Federico A. & Gao, Mengsi, 2023. "Inference under covariate-adaptive randomization with imperfect compliance," Journal of Econometrics, Elsevier, vol. 237(1).
    • Handle: RePEc:eee:econom:v:237:y:2023:i:1:s0304407623002130
    DOI: 10.1016/j.jeconom.2023.105497
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    References listed on IDEAS

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    More about this item

    Keywords

    Covariate-adaptive randomization; Stratified block randomization; Treatment assignment; Randomized controlled trial; Strata fixed effects; Saturated regression; Imperfect compliance;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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