IDEAS home Printed from https://ideas.repec.org/p/bep/unimip/unimi-1089.html
   My bibliography  Save this paper

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

    Download full text from publisher

    File URL: http://services.bepress.com/unimi/statistics/art46
    Download Restriction: no
    ---><---

    Other versions of this item:

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tymon Słoczyński, 2015. "The Oaxaca–Blinder Unexplained Component as a Treatment Effects Estimator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 588-604, August.
    2. Solomon Asfaw & Silvio Daidone & Benjamin Davis & Josh Dewbre & Alessandro Romeo & Paul Winters & Katia Covarrubias & Habiba Djebbari, 2012. "Analytical Framework for Evaluating the Productive Impact of Cash Transfer Programmes on Household Behaviour – Methodological Guidelines for the From Protection to Production Project," Working Papers 101, International Policy Centre for Inclusive Growth.
    3. Srinivasa, Aditya Korekallu & Praveen, K.V. & Subash, S.P. & Nithyashree, ML & Jha, Girish Kumar, 2021. "Does a Farmer’s Knowledge of Minimum Support Price (MSP) Affect the Farm-Gate Price? Evidence from India," 2021 Conference, August 17-31, 2021, Virtual 315205, International Association of Agricultural Economists.
    4. Ragni, Alessandra & Ippolito, Daniel & Masci, Chiara, 2024. "Assessing the impact of hybrid teaching on students’ academic performance via multilevel propensity score-based techniques," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    5. Matthew Blackwell & Stefano Iacus & Gary King & Giuseppe Porro, 2009. "cem: Coarsened exact matching in Stata," Stata Journal, StataCorp LLC, vol. 9(4), pages 524-546, December.
    6. Robert J. R. Elliott & Liza Jabbour & Liyun Zhang, 2016. "Firm productivity and importing: Evidence from Chinese manufacturing firms," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(3), pages 1086-1124, August.
    7. Michael Lechner & Anthony Strittmatter, 2019. "Practical procedures to deal with common support problems in matching estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 193-207, February.
    8. Nancy Nicosia & John M. MacDonald & Rosalie Liccardo Pacula, 2012. "Does Mandatory Diversion to Drug Treatment Eliminate Racial Disparities in the Incarceration of Drug Offenders? An Examination of California's Proposition 36," NBER Working Papers 18518, National Bureau of Economic Research, Inc.
    9. Siddique Abu Bakkar, 2020. "Identity-based Earning Discrimination among Chinese People," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 11(1), pages 1-42, January.
    10. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    11. Kevin P. Josey & Elizabeth Juarez‐Colunga & Fan Yang & Debashis Ghosh, 2021. "A framework for covariate balance using Bregman distances," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 790-816, September.
    12. Iacus, Stefano & Porro, Giuseppe, 2008. "Invariant and Metric Free Proximities for Data Matching: An R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i11).
    13. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
    14. Fredrik Savje, 2019. "On the inconsistency of matching without replacement," Papers 1907.07288, arXiv.org, revised Jun 2021.
    15. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    16. Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    17. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    18. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    19. Weihua An & Ying Ding, 2018. "The Landscape of Causal Inference: Perspective From Citation Network Analysis," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 265-277, July.
    20. Yount, Kathryn M. & Cheong, Yuk Fai & Khan, Zara & Miedema, Stephanie S. & Naved, Ruchira T., 2021. "Women's participation in microfinance: Effects on Women's agency, exposure to partner violence, and mental health," Social Science & Medicine, Elsevier, vol. 270(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bep:unimip:unimi-1089. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/damilit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.