The classification permutation test: a flexible approach to testing for covariate imbalance in observational studies

DOI10.1214/19-AOAS1241zbMath1434.62061MaRDI QIDQ2281195

Yotam Shem-Tov, Johann A. Gagnon-Bartsch

Publication date: 19 December 2019

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1571277760

zbMATH Keywords

classificationmatchingrandom forestsnatural experimentobservational studybalanceFisherian permutation inference

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (8)

On the use of random forest for two-sample testing ⋮ Classification accuracy as a proxy for two-sample testing ⋮ Evaluating and improving a matched comparison of antidepressants and bone density ⋮ New multivariate tests for assessing covariate balance in matched observational studies ⋮ Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies ⋮ Using predictability to improve matching of urban locations in Philadelphia ⋮ The classification permutation test: a flexible approach to testing for covariate imbalance in observational studies ⋮ Global and local two-sample tests via regression

Uses Software

• ElemStatLearn
• miProfile
• DOS

Cites Work

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• Regularization and Variable Selection Via the Elastic Net
• A consistent multivariate test of association based on ranks of distances
• An Exact Distribution-Free Test Comparing Two Multivariate Distributions based on Adjacency
• Multivariate Nonparametric Tests of Independence
• Design of observational studies

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