Augmenting the Bayes Estimator to Find More Matches in Bipartite Record Linkage

Abstract

In probabilistic record linkage, analysts seek to link records in two data files using error-prone identifiers like names or demographic variables. One approach is to treat the linkage structure as a random variable via a Bayesian model specification. The estimation process typically results in multiple plausible linked files that can be used for downstream analyses or to estimate uncertainty in the linkages. Often, however,analysts are interested in a point estimate of the linkage structure. Typically, they use a Bayes estimator that, in the usual implementation, declares record pairs links only if the model assigns them a posterior probability of being true links that exceeds 0.5. Thus, this estimator can miss links that have high posterior probability but do not cross the majority threshold. We introduce a strategy to augment the Bayes estimator with the goal of catching some of these potential missed links. The basic idea is to specify a loss function for adding links to the Bayes estimator. The loss parameters can be tuned to be more or less aggressive in declaring more pairs as links. We illustrate the strategy using a variety of simulation studies and discuss when it can be expected, or not, to add true matches to the Bayes estimator.