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Revisiting Randomization with the Cube Method

Laurent Davezies, Guillaume Hollard, Pedro Vergara Merino

arXiv 18 Jul 2024 · Econometrics

arXiv:2407.13613 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We introduce a new randomization procedure for experiments based on the cube method, which achieves near-exact covariate balance. This ensures compliance with standard balance tests and allows for balancing on many covariates, enabling more precise estimation of treatment effects using pre-experimental information. We derive theoretical bounds on imbalance as functions of sample size and covariate dimension, and establish consistency and asymptotic normality of the resulting estimators. Simulations show substantial improvements in precision and covariate balance over existing methods, particularly when the number of covariates is large.

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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
1Deville, J.-C. and Tillé, Y (2004) Efficient balanced sampling: The cube method0.6638188%
2Bai, Y., Romano, J. P., and Shaikh, A. M (2022) Inference in Experiments With Matched Pairs0.6526183%
3Harshaw, C., Sävje, F., Spielman, D. A., and Zhang, P (2024) Balancing Covariates in Randomized Experiments with the Gram–Schmidt Walk Design0.64441100%
4Snyder, C. M. and Zhuo, R (2024) Examining Selection Pressures in the Publication Process through the Lens of Sniff Tests0.64441100%
5Athey, S. and Imbens, G. W (2017) Chapter 3 - The Econometrics of Randomized Experimentsa0.58531100%
6Bai, Y (2022) Optimality of Matched-Pair Designs in Randomized Controlled Trials0.5757157%
7Bruhn, M. and McKenzie, D (2009) In Pursuit of Balance: Randomization in Practice in Development Field Experiments0.51121100%
8Hahn, J (1998) On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects0.51121100%
9Imbens, G. W (2011) Experimental design for unit and cluster randomid trials. Technical report, Harvard University0.51121100%
10Bugni, F. A., Canay, I. A., and Shaikh, A. M (2018) Inference Under Covariate-Adaptive Randomization0.4816133%

Showing the top 10 of 28 scored citations.