Inference under covariate-adaptive randomization with imperfect compliance

Federico A. Bugni and Mengsi Gao

Journal of Econometrics, 2023, vol. 237, issue 1

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.

Keywords: Covariate-adaptive randomization; Stratified block randomization; Treatment assignment; Randomized controlled trial; Strata fixed effects; Saturated regression; Imperfect compliance (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:237:y:2023:i:1:s0304407623002130

DOI: 10.1016/j.jeconom.2023.105497

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