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Multivariate Matching Methods That are Monotonic Imbalance Bounding

Author

Listed:
  • Stefano Iacus

    (Department of Economics, Business and Statistics, University of Milan, IT)

  • Gary King

    (Institute for Quantitative Social Science, Harvard University)

  • Giuseppe Porro

    (Department of Economics and Statistics, University of Trieste)

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 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.
  • Handle: RePEc:bep:unimip:unimi-1089
    Note: oai:cdlib1:unimi-1089
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    Keywords

    causal inference; treatment effect; matching;

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