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 InfoPaper provided by Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano in its series Departmental Working Papers with number 2009-51.
Date of creation: 16 Oct 2009
Date of revision:
Matching methods; causal inference; Equal Percent Bias Reducing;
Other versions of this item:
- Iacus, Stefano M. & King, Gary & Porro, Giuseppe, 2011. "Multivariate Matching Methods That Are Monotonic Imbalance Bounding," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 345-361.
- 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.
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