No ground truth? No problem: Improving administrative data linking using active learning and a little bit of guile.

ABSTRACT: While linking records across large administrative datasets ["big data"] has the potential to revolutionize empirical social science research, many administrative data files do not have common identifiers and are thus not designed to be linked to others. To address this problem, researchers have developed probabilistic record linkage algorithms which use statistical patterns in identifying characteristics to perform linking tasks. Naturally, the accuracy of a candidate linking algorithm can be substantially improved when an algorithm has access to "ground-truth" examples-matches which can be validated using institutional knowledge or auxiliary data. Unfortunately, the cost of obtaining these examples is typically high, often requiring a researcher to manually review pairs of records in orde

SUBMITTER: Tahamont S 

PROVIDER: S-EPMC10072450 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature