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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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    1. 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.
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    Cited by:

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    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. Michael A Ruderman & Deirdra F Wilson & Savanna Reid, 2015. "Does Prison Crowding Predict Higher Rates of Substance Use Related Parole Violations? A Recurrent Events Multi-Level Survival Analysis," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    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. Emilio Aguirre, 2016. "Impacto de ser becado del Programa Compromiso Educativo," Documentos de Trabajo (working papers) 1616, Department of Economics - dECON.
    8. Iacus, Stefano M. & King, Gary & Porro, Giuseppe, 2011. "Multivariate Matching Methods That Are Monotonic Imbalance Bounding," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 345-361.
    9. 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.
    10. Anjani Kumar & Vinay K. Sonkar & K. S. Aditya, 2023. "Assessing the Impact of Lending Through Kisan Credit Cards in Rural India: Evidence from Eastern India," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 35(3), pages 602-622, June.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. 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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