Estimating Precision of Deterministic Linkage

Deterministic record linkage is widely used when unique identifiers are unavailable. Multi-pass deterministic linkage increases the link rate, but evaluating the precision of each pass — that is, the proportion of correct links—is crucial for guiding linkage design. This paper proposes two methods for estimating pass-level precision. The first is an analytic estimator assuming uniformly distributed linking variables; the second is a replication-based estimator that avoids strong distributional assumptions. We demonstrate both methods through simulations using D-MAC, a deterministic linkage macro developed at the Australian Bureau of Statistics. Results show that while the analytic estimator performs well under uniformity, the replication approach provides robust estimates across a range of diverse scenarios, including those where linking variables have skewed distributions.