Multivariate matching methods that are monotonic imbalance bounding

Stefano Iacus (), Gary King and Giuseppe Porro

Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università 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: Matching methods; causal inference; Equal Percent Bias Reducing (search for similar items in EconPapers)
Date: 2009-10-16
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Citations: View citations in EconPapers (5)

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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:mil:wpdepa:2009-51

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