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Ruoqi Yu Department of Statistics, University of California , Davis, California , USA Ruoqi Yu, Department of Statistics, University of California, Davis, CA 95616, USA. Email: ruoqi.yu.ry@gmail.com Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0002-1770-1458 Biometrics, Volume 79, Issue 3, September 2023, Pages 2346–2356, https://doi.org/10.1111/biom.13771
Published:
12 October 2022
Article history
Received:
09 March 2022
Accepted:
03 October 2022
Published:
12 October 2022
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Ruoqi Yu, How Well Can Fine Balance Work for Covariate Balancing, Biometrics, Volume 79, Issue 3, September 2023, Pages 2346–2356, https://doi.org/10.1111/biom.13771
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Abstract
Fine balance is a matching technique to improve covariate balance in observational studies. It constrains a match to have identical distributions for some covariates without restricting who is matched to whom. However, despite its wide application and excellent performance in practice, there is very little theory indicating when the method is likely to succeed or fail and to what extent it can remove covariate imbalance. In order to answer these questions, this paper studies the limits of what is possible for covariate balancing using fine balance and near-fine balance. The investigations suggest that given the distributions of the treated and control groups, in large samples, the maximum achievable balance by using fine balance only depends on the matching ratio (ie, the ratio of the sample size of the control group to that of the treated group). In addition, the results indicate how to estimate this matching ratio threshold without knowledge of the true distributions in finite samples. The findings are also illustrated by numerical studies in this paper.
covariate balance, fine balance, matching, matching ratio, observational studies
© 2022 The International Biometric Society.
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
Issue Section:
Biometric Methodology > Biometric Methodology
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