Multivariate Matching Methods That are Monotonic Imbalance Bounding

Stefano Iacus (), Gary King and Giuseppe Porro

No unimi-1089, UNIMI - Research Papers in Economics, Business, and Statistics from Universitá degli Studi di Milano

Abstract: We introduce a new ``Monotonic Imbalance Bounding'' (MIB) class of matching methods for causal inference that satisfies several important in-sample properties. MIB generalizes and extends in several new directions the only existing class, ``Equal Percent Bias Reducing'' (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and present a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective.

Keywords: causal inference; treatment effect; matching (search for similar items in EconPapers)
Date: 2009-10-16
Note: oai:cdlib1:unimi-1089
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Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Multivariate Matching Methods That Are Monotonic Imbalance Bounding (2011)
Working Paper: Multivariate matching methods that are monotonic imbalance bounding (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:bep:unimip:unimi-1089

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