Multivariate Matching Methods That Are Monotonic Imbalance Bounding
AbstractWe 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.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of the American Statistical Association.
Volume (Year): 106 (2011)
Issue (Month): 493 ()
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- Stefano Iacus & Gary King & Giuseppe Porro, 2009. "Multivariate Matching Methods That are Monotonic Imbalance Bounding," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1089, Universitá degli Studi di Milano.
- Stefano Maria IACUS & Gary KING & Giuseppe PORRO, 2009. "Multivariate matching methods that are monotonic imbalance bounding," Departmental Working Papers 2009-51, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
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