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Inference in Cluster Randomized Trials with Matched Pairs

Yuehao Bai, Jizhou Liu, Azeem M. Shaikh, Max Tabord-Meehan

arXiv 27 Nov 2022 · Econometrics · publishedJournal of Econometrics (2024) · 1 citations (OpenAlex)

arXiv:2211.14903 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies inference in cluster randomized trials where treatment status is determined according to a "matched pairs" design. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the level of the cluster; by a "matched pairs" design, we mean that a sample of clusters is paired according to baseline, cluster-level covariates and, within each pair, one cluster is selected at random for treatment. We study the large-sample behavior of a weighted difference-in-means estimator and derive two distinct sets of results depending on if the matching procedure does or does not match on cluster size. We then propose a single variance estimator which is consistent in either regime. Combining these results establishes the asymptotic exactness of tests based on these estimators. Next, we consider the properties of two common testing procedures based on t-tests constructed from linear regressions, and argue that both are generally conservative in our framework. We additionally study the behavior of a randomization test which permutes the treatment status for clusters within pairs, and establish its finite-sample and asymptotic validity for testing specific null hypotheses. Finally, we propose a covariate-adjusted estimator which adjusts for additional baseline covariates not used for treatment assignment, and establish conditions under which such an estimator leads to strict improvements in precision. A simulation study confirms the practical relevance of our theoretical results.

Citation extraction

38
references
107
in-text mentions
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distinct cited
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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
1de Chaisemartin, C. and Ramirez-Cuellar, J (2024) At what level should one cluster standard errors in paired and small-strata experiments?1.00064100%
2Bugni, F., Canay, I., Shaikh, A. and Tabord-Meehan, M (2024) Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes self0.94112383%
3Bai, Y., Jiang, L., Romano, J. P., Shaikh, A. M. and Zhang, Y (2024) Covariate adjustment in experiments with matched pairs self0.9209378%
4Bai, Y., Liu, J. and Tabord-Meehan, M (2024) Inference for Matched Tuples and Fully Blocked Factorial Designs self0.8434475%
5Bai, Y., Romano, J. P. and Shaikh, A. M (2022) Inference in Experiments With Matched Pairs self0.82227656%
6Bai, Y (2022) Optimality of Matched-Pair Designs in Randomized Controlled Trials self0.7373367%
7Bai, Y., Shaikh, A. M. and Tabord-Meehan, M (2024) A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances self0.6443267%
8Cytrynbaum, M (2023) Covariate adjustment in stratified experiments0.64422100%
9Negi, A. and Wooldridge, J. M (2021) Revisiting regression adjustment in experiments with heterogeneous treatment effects0.64422100%
10Chung, E. and Romano, J. P (2013) Exact and asymptotically robust permutation tests0.5853333%

Showing the top 10 of 38 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1On the Efficiency of Highly Stratified Experiments1.00054
2Inference for Two-stage Experiments under Covariate-Adaptive Randomization0.95685
3A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances0.81142
4Genuinely Robust Inference for Clustered Data0.64422
5Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method0.40511
6A New Design-Based Variance Estimator for Finely Stratified Experiments0.40511
7Limitations of Randomization Tests in Finite Samples0.40511