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Inference under Covariate-Adaptive Randomization with Multiple Treatments

Federico A. Bugni, Ivan A. Canay, Azeem M. Shaikh

arXiv 11 Jun 2018 · Econometrics · publishedQuantitative Economics (2019) · 70 citations (OpenAlex)

arXiv:1806.04206 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specifically, we study inference about the average effect of one or more treatments relative to other treatments or a control. As in Bugni et al. (2018), covariate-adaptive randomization 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 Bugni et al. (2018), we not only allow for multiple treatments, but further allow for the proportion of units being assigned to each of the treatments to vary across strata. We first study the properties of estimators derived from a fully saturated linear regression, i.e., a linear regression of the outcome on all interactions between indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity-consistent estimator of the asymptotic variance are invalid; on the other hand, tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact. For the special case in which the target proportion of units being assigned to each of the treatments does not vary across strata, we additionally consider tests based on estimators derived from a linear regression with strata fixed effects, i.e., a linear regression of the outcome on indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity-consistent estimator of the asymptotic variance are conservative, but tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact. A simulation study illustrates the practical relevance of our theoretical results.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bugni, F. A., Canay, I. A. and Shaikh, A. M (2018) Inference under covariate-adaptive randomization self0.94720885%
2Imbens, G. W. and Rubin, D. B (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction0.874122100%
3Chong, A., Cohen, I., Field, E., Nakasone, E. and Torero, M (2016) Iron deficiency and schooling attainment in peru0.69371100%
4Rosenberger, W. F. and Lachin, J. M (2016) Randomization in clinical trials: theory and practice0.64422100%
5Bruhn, M. and McKenzie, D (2009) In pursuit of balance: Randomization in practice in development field experiments0.51121100%
6Callen, M., Gulzar, S., Hasanain, A., Khan, Y. and Rezaee, A (2019) Personalities and public sector performance: Evidence from a health experiment in Pakistan0.40511100%
7Bai, Y (2018) On optimal stratification in randomized controlled trials0.40511100%
8Dizon-Ross, R (2018) Parents' beliefs about their children's academic ability: implications for educational investments0.40511100%
9Duflo, E., Dupas, P. and Kremer, M (2015) Education, HIV, and early fertility: Experimental evidence from Kenya0.40511100%
10Duflo, E., Glennerster, R. and Kremer, M (2007) Using randomization in development economics research: A toolkit0.40511100%

Showing the top 10 of 19 scored citations.

Cited by, within the corpus

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Citing paperIntensityMentionsSections
1Inference under Covariate-Adaptive Randomization with Imperfect Compliance1.000215
2Efficient Semiparametric Estimation of Average Treatment Effects Under Covariate Adaptive Randomization1.000134
3Adjustments with Many Regressors under Covariate-Adaptive Randomizations1.00085
4Assumption-lean covariate adjustment under covariate adaptive randomization when $p = o (n)$1.00063
5Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations0.73732
6Decomposition and Interpretation of Treatment Effects in Settings with Delayed Outcomes0.73732
7Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance0.64422
8Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes0.63083
9Optimal Stratification of Survey Experiments0.51121
10Inference for Matched Tuples and Fully Blocked Factorial Designs0.51122