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Theoretical limits of microclustering for record linkage.
J E Johndrow1, K Lum2, D B Dunson3
1Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, Stanford, California 94305, U.S.A.
Biometrika
|June 9, 2018
Summary
Accurate entity resolution in record linkage is theoretically challenging. Even with ideal data, precise matching is often impossible, suggesting a need for cautious interpretation and coarser analysis methods.
Area of Science:
- Computer Science
- Statistics
- Data Science
Background:
- Record linkage aims to identify and group records referring to the same entity across databases lacking unique identifiers.
- This process is analogous to microclustering, characterized by numerous small clusters (entities) within large datasets.
Purpose of the Study:
- To theoretically analyze the inherent difficulties of accurate entity resolution in record linkage.
- To provide guidance on interpreting record linkage results and suggest alternative analytical approaches.
Main Methods:
- Theoretical analysis of the record linkage problem under idealized conditions.
- Simulation studies to evaluate the impact of entity resolution accuracy on population size estimation.
Main Results:
- Accurate entity resolution is fundamentally difficult and often practically impossible.
- Success is contingent on a small number of entities relative to records or very large separation between entities.
- Coarser inference, such as population size estimation, may remain accurate even with imperfect entity resolution.
Conclusions:
- Emphasize conservatism when interpreting record linkage outcomes.
- Advocate for collecting more data to improve entity disambiguation.
- Promote research into coarser inference methods that are robust to fine-scale resolution errors.
Keywords:
Closed population estimationClusteringEntity resolutionMicroclusteringRecord linkageSmall clustersMore Related Videos
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