Can you suggest a good review of case control matching algorithms? Algorithms that can be used to set up the matched pairs of one case and one control, or matched blocks of a case and multiple controls.(Preferably a paper, book chapter or website discussing recent developments.)
For an overview of some matching algorithms as well as clear examples of applications in everything from education to medical experiments, I would suggest:
- Paul R. Rosenbaum (2010). Design of Observational Studies. Springer.
Rosenbaum's earlier book provides a more technical review, though because matching is such a hot topic at the moment, it may not cover the most current techniques:
- Paul R. Rosenbaum (2002). Observational Studies, Second Edition. Springer.
Even if Rosenbaum doesn't hit on a particular topic of interest, his chapter bibliographies are an excellent resource (particularly those in Design). He has also done some very valuable work on matching sensitivity analyses, which are covered extensively in these books.
Of course, you would probably also be served by going directly to the source (I haven't read this myself):
- Donald B. Rubin (2006). Matched Sampling for Causal Effects. Cambridge University Press.
As mentioned above, matching is something of a hot topic. So, generally, I would look through the citations of more recent books and articles. Besides work by Donald Rubin and Paul Rosenbaum, I would look for work by Alberto Abadie and Guido Imbens (both at Harvard) and James Heckman (Chicago), probably in that order. Of course, depending on your particular research interests, others may be equally as important.
Try the What's New in Econometrics section by Imbens and Wooldridge here:
There is a section on matching and it is aimed at the informed practitioner, not suitable for the complete novice.