Using Artificial Intelligence to Aid in the Development of Causal Models
Final rept. 1 Apr 1989-30 Mar 1990,
CARNEGIE-MELLON UNIV PITTSBURGH PA
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Data analysis that merely fits an empirical covariance matrix or that finds the best least squares linear estimator of a variable is not a reliable guide to judgements about policy, which inevitably involve causal conclusions. We have developed and tested a computer program TETRAD II, that accepts as input background knowledge about a causal structure, a covariance matrix, and a sample size, and outputs a set of suggested models compatible with the background knowledge and that explain the data. In tests on simulated data, TETRAD II was able to suggest a set of models that included the correct one 94 of the time. We have also applied TETRAD II to several data sets supplied by the Naval Personnel Research and Development Center.
- Computer Programming and Software