Bayesian Estimation of Bipartite Matchings for Record Linkage

ABSTRACT

The bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities. This is nontrivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine information from different sources. Most statistical techniques currently used for record linkage are derived from a seminal article by Fellegi and Sunter in 1969. These techniques usually assume independence in the matching statuses of record pairs to derive estimation procedures and optimal point estimators. We argue that this independence assumption is unreasonable and instead target a bipartite matching between the two datafiles as our parameter of interest. Bayesian implementations allow us to quantify uncertainty on the matching decisions and derive a variety of point estimators using different loss functions. We propose partial Bayes estimates that allow uncertain parts of the bipartite matching to be left unresolved. We evaluate our approach to record linkage using a variety of challenging scenarios and show that it outperforms the traditional methodology. We illustrate the advantages of our methods merging two datafiles on casualties from the civil war of El Salvador. Supplementary materials for this article are available online.

KEYWORDS:

• Assignment problem
• Bayes estimate
• Data matching
• Fellegi–Sunter decision rule
• Mixture model
• Reject option

Acknowledgments

The author thanks Kira Bokalders, Bill Eddy, Steve Fienberg, Rebecca Nugent, Jerry Reiter, Beka Steorts, Andrea Tancredi, Bill Winkler, the editors, associate editor, and referees for helpful comments and suggestions on earlier versions of this article, Patrick Ball and Megan Price from the Human Rights Data Analysis Group – HRDAG for providing access to the data used in this article, and Peter Christen for sharing his synthetic datafile generator.

Funding

This research is derived from the Ph.D. thesis of the author and was supported by NSF grants SES-11-30706 to Carnegie Mellon University and SES-11-31897 to Duke University/NISS.