What would you say are the main differences with other approaches?
For instance, a match on a date of birth column lends a greater weight of evidence in favour of a match than a match on first name (since dob has higher cardinality).
The method is also able to estimate weights for fuzzy matches (how much evidence in favour of a match is close match on dob with one character difference), and also how much evidence against a match a mismatch is.
For instance, if you have very high data quality on gender, then a match on gender doesn't tell you much, but a mismatch on gender is quite strong evidence against the idea two records match.
I have a blog post here that delves into this a bit more: https://www.robinlinacre.com/fellegi_sunter_accuracy/
(1) The blocking approach you choose (how wide you cast the net in searching for matches. This is actually somewhere were minhash can be used in conjunction
(2) whether you choose to use complex fuzzy matching functions and how many - this is chosen by the user.
There's some benchmarking results here: https://www.robinlinacre.com/fast_deduplication/
Overall it's an approach which makes a pretty good trade off between speed and accuracy. That's why it's used by many national stats institutes (UK, US, Aus, Germany etc.) - because it's capable of working on country-population sized datasets.
