Using Artificial Intelligence to Aid in the Development of Causal Models

Abstract : 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.