Improving Probabilistic Record Linkage Using Statistical Prediction Models

Angelo Moretti, Natalie Shlomo

Research output: Contribution to journalArticlepeer-review


Record linkage brings together information from records in two or more data sources that are believed to belong to the same statistical unit based on a common set of matching variables. Matching variables, however, can appear with errors and variations and the challenge is to link statistical units that are subject to error. We provide an overview of record linkage techniques and specifically investigate the classic Fellegi and Sunter (1969) probabilistic record linkage framework to assess whether the decision rule for classifying pairs into sets of matches and non-matches can be improved by incorporating a statistical prediction model. We also study whether the enhanced linkage rule can provide better results in terms of preserving associations between variables in the linked data file that are not used in the matching procedure. A simulation study and an application based on real data are used to evaluate the methods.

Keywords: Linkage errors; Matching variables; Propensity scores; Predictions
Original languageEnglish
Pages (from-to)368-394
JournalInternational Statistical Review
Issue number3
Publication statusAccepted/In press - 7 Nov 2022


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