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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
    as

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    References listed on IDEAS

    as
    1. Giuseppe Porro & Stefano Maria Iacus, 2009. "Random Recursive Partitioning: a matching method for the estimation of the average treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 163-185.
    2. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    3. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
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