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Missing data imputation, classification, prediction and average treatment effect estimation via Random Recursive Partitioning

Author

Listed:
  • 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 describe some applications of the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in non parametric hot-deck missing data imputation, classification, prediction, average treatment effect estimation and, more generally, in matching problems. RRP is a Monte Carlo procedure that randomly generates non-empty recursive partitions of the data and evaluates the proximity between observations as the empirical frequency they fall in the same cell of these random partitions over all the replications. RRP works also in the presence of missing data and is invariant under monotonic transformations of the data. No other formal properties of the method are known yet, therefore Monte Carlo experiments are provided in order to explore the performance of the method. A companion software is available in the form of a package for the R statistical environment.

Suggested Citation

  • Stefano Iacus & Giuseppe Porro, 2006. "Missing data imputation, classification, prediction and average treatment effect estimation via Random Recursive Partitioning," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1022, Universitá degli Studi di Milano.
    • Handle: RePEc:bep:unimip:unimi-1022
    Note: oai:cdlib1:unimi-1022
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    Cited by:

    1. 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.

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