How is this different than typical multi-variable regressions with control variables? I think it is different. Do you understand the smoking/tar/cancer example

How is this different than typical multi-variable regressions with control variables? I think it is different. Do you understand the smoking/tar/cancer example? See here for the example: http://bayes.cs.ucla.edu/IJCAI99/ijcai-99.pdf http://bayes.cs.ucla.edu/IJCAI99/ijcai-99.pdf (pages 34-38 and 46), and here for a better introductory text: http://cscs.umich.edu/~crshalizi/notebooks/graphical-models.html http://cscs.umich.edu/~crshalizi/notebooks/graphical-models.... . Basically, if the situation allows you to reason that some causal arrows are impossible (e.g. due to time or space separation), you can often infer which of the remaining causal arrows are real and which aren't, using only non-experimental correlation data and some formal inference rules. Of course it doesn't always work - sometimes there really isn't enough data and you have to do a controlled experiment. But on the other hand, people often need to reason about causality in areas where controlled experiments are impossible or unethical, like smoking/tar/cancer.