Missing data imputation, matching and other applications of random recursive partitioning

Authors:
Stefano M. Iacus;Giuseppe Porro
Affiliations:
Department of Economics, Business and Statistics, University of Milan, Via Conservatorio 7, I-20122 Milan, Italy;Department of Economics and Statistics, University of Trieste, P.le Europa 1, I-34127 Trieste, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

Applications of the random recursive partitioning (RRP) method are described. This method generates a proximity matrix which can be used in non-parametric matching problems such as hot-deck missing data imputation and average treatment effect estimation. RRP is a Monte Carlo procedure that randomly generates non-empty recursive partitions of the data and calculates the proximity between observations as the empirical frequency in the same cell of these random partitions over all the replications. Also, the method in the presence of missing data is invariant under monotonic transformations of the data but no other formal properties of the method are known yet. Therefore, Monte Carlo experiments were conducted in order to explore the performance of the method. A companion software is available as a package for the R statistical environment.