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Multivariate matching methods that are monotonic imbalance bounding

Author

Listed:
  • Stefano Maria IACUS
  • Gary KING
  • Giuseppe PORRO

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.

Suggested Citation

  • Stefano Maria IACUS & Gary KING & Giuseppe PORRO, 2009. "Multivariate matching methods that are monotonic imbalance bounding," Departmental Working Papers 2009-051, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    • Handle: RePEc:mil:wpdepa:2009-051
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