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An empirical process framework for covariate balance in causal inference

We propose a new perspective for the evaluation of matching procedures by considering the complexity of the function class they belong to. Under this perspective we provide theoretical guarantees on post-matching covariate balance through a finite sample concentration inequality. We apply this framework to coarsened exact matching as well as matching using the propensity score and suggest how to apply it to other algorithms. Simulation studies are used to evaluate the procedures.

preprint2023arXivOpen access
Efrén Cruz CortésKevin JoseyFan YangDebashis Ghosh
math.STStatistics Theory
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Open access4 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Efrén Cruz CortésKevin JoseyFan YangDebashis Ghosh

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