Machine learning has traditionally been focused on prediction. Given observations that have been generated by an unknown stochastic dependency, the goal is to infer a law that will be able to correctly predict future observations generated by the same dependency. Statistics, in contrast, has traditionally focused on data modeling, i.e., on the estimation of a probability law that has generated the data. During recent years, the boundaries between the two disciplines have become blurred and both communities have adopted methods from the other, however, it is probably fair to say that neither of them has yet fully embraced the field of causal modeling, i.e., the detection of causal structure underlying the data. Since the Eighties there has been a community of researchers, mostly from statistics and philosophy, who have developed methods aiming at inferring causal relationships from observational data. While this community has remained relatively small, it has recently been complemented by a number of researchers from machine learning. The goal of this workshop is to discuss new approaches to causal discovery from empirical data, their applications and methods to evaluate their success. Emphasis will be put on the definition of objectives to be reached and assessment methods to evaluate proposed solutions. The participants are encouraged to participate in a ""competition pot-luck"" in which datasets and problems will be exchanged and solutions proposed.