 The discussion of the previous section conveys some of the complexity of the problem of inferring causal relationships from probabilistic correlations. Fairly recently, a number of techniques have been developed for representing systems of causal relationships, and for inferring causal relationships from probabilities. The name ‘causal modeling’ is often used to describe the new interdisciplinary field devoted to the study of methods of causal inference. This field includes contributions from statistics, artificial intelligence, philosophy, econometrics, epidemiology, and other disciplines. Within this field, the research programs that have attracted the greatest philosophical interest are those of the computer scientist Judea Pearl and his collaborators, and of the philosophers Peter Spirtes, Clark Glymour, and Richard Scheines (SGS) and their collaborators. The most significant works of these authors are Pearl (2009) (first published in 2000), and Spirtes et al. (2000) (first published in 1993).