10.1177/0193841X04266432 ARTICLEEVALUATION REVIEW / AUGUST 2004Freedman / GRAPHICAL MODELS FOR CAUSATION GRAPHICAL MODELS FOR CAUSATION, AND THE IDENTIFICATION PROBLEM

This article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invari-ant. Graphs can be interpreted using conditional distributions, so that we can better address con-nections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical mod-els, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed