Applied researchers are often interested in linking individuals between two datasets that lack unique identifiers. Accuracy and computational feasibility are a challenge, particularly when linking large datasets. We develop a Bayesian method for automated probabilistic record linkage and show it recovers 40 \% more true matches, holding accuracy constant, than comparable methods in a matching of Union Army recruitment data to the 1900 US Census for which expert-labelled true matches are known . Our approach, which builds on a recent state-of-the-art Bayesian method, refines the modelling of comparison data, allowing disagreement probability parameters conditional on non-match status to be record-specific . To make this refinement computationally feasible , we implement a Gibbs sampler that achieves significant improvement in speed over comparable recent implementations . We also generalize the notion of comparison data to allow for treatment of very common first names that spuriously produce exact matches in record pairs and show how to estimate true positive rate and positive predictive value when ground truth is unavailable.
Researchers are often interested in linking individuals between two datasets that lack a common unique identifier. Matching procedures often struggle to match records with common names, birthplaces or other field values. Computational feasibility is also a challenge, particularly when linking large datasets. We develop a Bayesian method for automated probabilistic record linkage and show it recovers more than 50 \% more true matches, holding accuracy constant, than comparable methods in a matching of military recruitment data to the 1900 US Census for which expert-labelled matches are available . Our approach, which builds on a recent state-of-the-art Bayesian method, refines the modelling of comparison data, allowing disagreement probability parameters conditional on non-match status to be record-specific in the smaller of the two datasets. This flexibility significantly improves matching when many records share common field values. We show that our method is computationally feasible in practice, despite the added complexity, with an R/C++ implementation that achieves significant improvement in speed over comparable recent methods . We also suggest a lightweight method for treatment of very common names and show how to estimate true positive rate and positive predictive value when true match status is unavailable.
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Applied researchers
Researchers
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unique identifiers. Accuracy and computational feasibility are
a common unique identifier. Matching procedures often struggle to match records with common names, birthplaces or other field values. Computational feasibility is also
R
40
more than 50
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Union Army
military
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true matches are known
matches are available
R
. To make this refinement computationally feasible , we implement a Gibbs sampler
in the smaller of the two datasets. This flexibility significantly improves matching when many records share common field values. We show that our method is computationally feasible in practice, despite the added complexity, with an R/C++ implementation
R
implementations
methods
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generalize the notion of comparison data to allow
suggest a lightweight method
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first names that spuriously produce exact matches in record pairs
names
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ground truth
true match status