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IlyaShpitser comments on Causal Diagrams and Causal Models - Less Wrong
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You are correct -- directed edges do not imply causality by means of only conditional independence tests. You need something called the faithfulness assumption, and additional (causal) assumptions, that Eliezer glossed over. Without causal assumptions and with only faithfulness, all you are recovering is the structure of a statistical, rather than a causal model. Without faithfulness, conditional independence tests do not imply anything. This is a subtle issue, actually.
There is no magic -- you do not get causality without causal assumptions.
Is this another variation of the theme that one needs to assume the possibility of inductive reasoning to make an argument for it (or also assume Occam's Razor to argue for it)? Also, the specific example he gave seems to me like an instance of "given very skewed data, the best guesses are still wrong" (there was sometime a variation of that here, regarding bets and opponents who have superior information). Or are you thinking of something for subtle?
Even if you assume that we can do induction (and assume faithfulness!), conditional independence tests simply do not select among causal models. They select among statistical models, because conditional independences are properties of joint distributions (statistical, rather than causal objects). Linking those joint distributions with something causal relies on causal assumptions.
I think the biggest lesson to learn from Pearl's book is to keep statistical and causal notions separate.
Thanks for clarifying!