{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T23:30:43Z","timestamp":1785713443945,"version":"3.56.0"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2008,9,2]],"date-time":"2008-09-02T00:00:00Z","timestamp":1220313600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2009,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010empty 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 \u201chonest\u201d in that it does not match observations \u201cat any cost\u201d: if data sets are separated, the method clearly states it.<\/jats:p><jats:p>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 \u00a9 2008 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.1026","type":"journal-article","created":{"date-parts":[[2008,9,2]],"date-time":"2008-09-02T13:48:38Z","timestamp":1220363318000},"page":"163-185","source":"Crossref","is-referenced-by-count":24,"title":["Random Recursive Partitioning: a matching method for the estimation of the average treatment effect"],"prefix":"10.1002","volume":"24","author":[{"given":"Giuseppe","family":"Porro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano Maria","family":"Iacus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2008,12,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2006.00655.x"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/1536867X0200200403"},{"key":"e_1_2_8_4_1","volume-title":"Linear network optimization: algorithm and codes","author":"Bertsekas D","year":"1991"},{"key":"e_1_2_8_5_1","volume-title":"Classification and Regression Trees","author":"Breiman L","year":"1984"},{"key":"e_1_2_8_6_1","first-page":"417","article-title":"Controlling Bias in Observational Studies: A Review","volume":"35","author":"Cochran W","year":"1973","journal-title":"Sankhya A"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2004.04.012"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10473858"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/003465302317331982"},{"key":"e_1_2_8_10_1","unstructured":"DiamondA SekhonJS.2005.Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies. 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