Cosma Shalizi (2009): Peter Spirtes, Clark Glymour and Richard Scheines, Causation, Prediction and Search: "Re-read as part of preparing for my lecture on casual discovery. I spent much of the winter of 2000 working my way through the first edition, and wound up completely imprinted on its way of thinking about what causal relationships are, how we should reason about them, and how we can find them from empirical evidence... http://www.stat.cmu.edu/~cshalizi/350/lectures/31/lecture-31.pdf...
.... On causation and prediction it now has an equal in Pearl's book (and I admit the latter looks prettier), but on search, that is, on discovering causal structure, there is still no rival. Their key observation is that even though correlation does not imply causation, correlations must have causal explanations. (This idea goes back to Herbert Simon, and Hans Reichenbach [see above] at least.)
So patterns of correlations, among more than just two variables, constrain what causal structures are possible. Sometimes they constrain the causal structure uniquely, in other cases it's only partially identified by the dependencies. And of course there is always the possibility of making a mistake with limited data. But none of this is any different for causal discovery than it is for any other form of statistical inference. The great contribution of this book is showing that causal discovery can be just another learning problem. They have transformed metaphysical misery into ordinary statistical unhappiness...
#shouldread
