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Random Recursive Partitioning: a matching method for the estimation of the average treatment effect

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  • Giuseppe Porro

    (Department of Economics and Statistics, University of Trieste, P.le Europa 1, I-34127 Trieste, Italy)

  • Stefano Maria Iacus

    (Department of Economics, Business and Statistics, University of Milan, Via Conservatorio 7, I-20122 Milano, Italy)

Abstract

In this paper we introduce the Random Recursive Partitioning (RRP) matching method. RRP generates a proximity matrix which might be useful in econometric applications like average treatment effect estimation. RRP is a Monte Carlo method that randomly generates non-empty recursive partitions of the data and evaluates the proximity between two observations as the empirical frequency they fall in a same cell of these random partitions over all Monte Carlo replications. From the proximity matrix it is possible to derive both graphical and analytical tools to evaluate the extent of the common support between data sets. The RRP method is “honest” in that it does not match observations “at any cost”: if data sets are separated, the method clearly states it. The match obtained with RRP is invariant under monotonic transformation of the data. Average treatment effect estimators derived from the proximity matrix seem to be competitive compared to more commonly used estimators. RRP method does not require a particular structure of the data and for this reason it can be applied when distances like Mahalanobis or Euclidean are not suitable, in the presence of missing data or when the estimated propensity score is too sensitive to model specifications. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
  • Handle: RePEc:jae:japmet:v:24:y:2009:i:1:p:163-185
    DOI: 10.1002/jae.1026
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    References listed on IDEAS

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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
    4. Smith, Jeffrey & Todd, Petra, 2005. "Rejoinder," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 365-375.
    5. 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.
    6. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
    7. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    8. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    9. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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    Cited by:

    1. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    2. Claudio Cozza & Franco Malerba & Maria Luisa Mancusi & Giulio Perani & Andrea Vezzulli, 2012. "Innovation, profitability and growth in medium and high-tech manufacturing industries: evidence from Italy," Applied Economics, Taylor & Francis Journals, vol. 44(15), pages 1963-1976, May.
    3. Iacus, Stefano M. & Porro, Giuseppe, 2007. "Missing data imputation, matching and other applications of random recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 773-789, October.
    4. 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.
    5. Mathieu Bunel & Yannick L'Horty, 2012. "The Effects of Reduced Social Security Contributions on Employment: An Evaluation of the 2003 French Reform," Fiscal Studies, Institute for Fiscal Studies, vol. 33(3), pages 371-398, September.
    6. Stefano VERZILLO & Paolo BERTA & Matteo BOSSI, 2015. "%CEM: A SAS Macro to Perform Coarsened Exact Matching," Departmental Working Papers 2015-22, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    7. Matthew Blackwell & Stefano Iacus & Gary King & Giuseppe Porro, 2009. "cem: Coarsened exact matching in Stata," Stata Journal, StataCorp LP, vol. 9(4), pages 524-546, December.
    8. 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).
    9. Chang, Hung-Hao, 2016. "Evaluating the Impacts of the 2008-2009 Great Recession on Labor Supply of Family Farm Households," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235161, Agricultural and Applied Economics Association.

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