It’s apparently been put to use with some success. Clark Glymour—a philosophy professor who helped develop TETRAD—wrote a long review of The Bell Curve that lists applications of an earlier version of TETRAD (see section 6 of the review):
Several other applications have been made of the techniques, for example:
Spirtes et al. (1993) used published data on a small observational sample of Spartina grass from the Cape Fear estuary to correctly predict—contrary both to regression results and expert opinion—the outcome of an unpublished greenhouse experiment on the influence of salinity, pH and aeration on growth.
Druzdzel and Glymour (1994) used data from the US News and World Report survey of American colleges and universities to predict the effect on dropout rates of manipulating average SAT scores of freshman classes. The prediction was confirmed at Carnegie Mellon University.
Waldemark used the techniques to recalibrate a mass spectrometer aboard a Swedish satellite, reducing errors by half.
Shipley (1995, 1997, in review) used the techniques to model a variety of biological problems, and developed adaptations of them for small sample problems.
Akleman et al. (1997) have found that the graphical model search techniques do as well or better than standard time series regression techniques based on statistical loss functions at out of sample predictions for data on exchange rates and corn prices.
Personally I find it a little odd that such a useful tool is still so obscure, but I guess a lot of scientists are loath to change tools and techniques.
It’s apparently been put to use with some success. Clark Glymour—a philosophy professor who helped develop TETRAD—wrote a long review of The Bell Curve that lists applications of an earlier version of TETRAD (see section 6 of the review):
Personally I find it a little odd that such a useful tool is still so obscure, but I guess a lot of scientists are loath to change tools and techniques.