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Random recursive partitioning: a matching method for the estimation of the average treatment effect

Author

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  • Stefano Iacus

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

  • Giuseppe Porro

    (Department of Economics and Statistics, University of Trieste, Italy)

Abstract

In this paper we introduce the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in applications like average treatment effect estimation in observational studies. 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 the replications. From the proximity matrix it is possible to derive both graphical and analytical tools to evaluate the extent of the common support between two datasets. The RRP method is ``honest'' in that it does not match observations ``at any cost'': if two datasets are separated, the method clearly states it. This method is affine under invariant transformation of the data and hence it is an equal percent bias reduction (EPBR) method when data come from ellipsoidal and symmetric distributions. Average treatment effect estimators derived from the proximity matrix seem to be competitive compared to more commonly used methods (like, e.g., Mahalanobis full match with calipers within propensity scores) even outside the hypotheses leading to EPBR. 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. As a method working on the original data (i.e. on a multidimensional space instead of a one dimensional measure), RRP is affected by the curse of dimensionality when the number of continuous covariates is too high. Asymptotic properties as well as the behaviour of the RRP method under different data distributions are explored using Monte Carlo methods.

Suggested Citation

  • Stefano Iacus & Giuseppe Porro, 2006. "Random recursive partitioning: a matching method for the estimation of the average treatment effect," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1018, Universitá degli Studi di Milano.
  • Handle: RePEc:bep:unimip:unimi-1018
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    References listed on IDEAS

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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. Emilio Aguirre, 2016. "Impacto de ser becado del Programa Compromiso Educativo," Documentos de Trabajo (working papers) 1616, Department of Economics - dECON.
    3. 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.
    4. 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.
    5. Corrocher, Nicoletta & Lamperti, Francesco & Mavilia, Roberto, 2019. "Do science parks sustain or trigger innovation? Empirical evidence from Italy," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 140-151.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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, Boston, Massachusetts 235161, Agricultural and Applied Economics Association.

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