Bipartite Matching Estimation for Record Linkage

• Activity Number: 478
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Mauricio Sadinle*
• Companies: Carnegie Mellon University
• Keywords: Bipartite matching ; Fellegi-Sunter decision rule ; Data matching ; Data linkage ; Data integration

Abstract

In this talk we are concerned with the most traditional scenario of record linkage, which consists of linking two disparate datafiles containing overlapping information on a set of individuals or entities, and it is assumed that each entity is recorded maximum once in each datafile. Most statistical techniques currently in use are derived from a seminal paper by Fellegi and Sunter (1969) who formalized procedures that had been used earlier by other researchers. These techniques usually assume independence in the matching status of record pairs to derive estimation procedures and optimal point estimators (e.g. Fellegi-Sunter decision rule). We argue that this independence assumption is unreasonable and target instead a bipartite matching between the two sets of records coming from the two files as our parameter of interest, and derive estimation procedures and point estimators accordingly. We demonstrate the improvements of our approach over traditional methodologies in a number of realistic simulation studies.