Abstract. A number of algorithms purport to discover causal structure from empirical data with no need for specific subject-matter knowledge. Advocates claim that the algorithms are superior to methods already used in the social sciences (regression analysis, path models, factor analysis, hierarchical linear models, and so on). But they have no real success stories to report. The algorithms are computationally impressive and the associated mathematical theory may be of some interest. However, the problem solved is quite removed from the challenge of causal inference from imperfect data. Nor do the methods resolve long-standing philosophical questions about the meaning of causation.

Causation, Prediction, and Search by Peter Spirtes, Clark Glymour, and Richard Scheines (SGS) is an ambitious book. SGS claim to have methods for discovering causal relations based only on empirical data, with no need for subject-matter knowledge. These methods–which combine graph theory, statistics, and computer science–are said to allow quick, virtually automated, conversion of association to causation. The algorithms are held out as superior to methods already in use in the social sciences (regression analysis, path models, factor analysis, hierarchical linear models, and so on).