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Causal Inference without Balance Checking: Coarsened Exact Matching

Causal Inference without Balance Checking: Coarsened Exact Matching We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance Bounding” (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R , Stata, and SPSS that implement all our suggestions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Political Analysis Oxford University Press

Causal Inference without Balance Checking: Coarsened Exact Matching

Political Analysis , Volume 20 (1) – Jan 1, 2012

Causal Inference without Balance Checking: Coarsened Exact Matching

Political Analysis , Volume 20 (1) – Jan 1, 2012

Abstract

We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance Bounding” (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R , Stata, and SPSS that implement all our suggestions.

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References (53)

Publisher
Oxford University Press
Copyright
© The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. For Permissions, please email: journals.permissions@oup.com
ISSN
1047-1987
eISSN
1476-4989
DOI
10.1093/pan/mpr013
Publisher site
See Article on Publisher Site

Abstract

We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance Bounding” (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R , Stata, and SPSS that implement all our suggestions.

Journal

Political AnalysisOxford University Press

Published: Jan 1, 2012

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