First, I am unsure what it means for a story to be true.
Ah, by that I generally mean something like "the causal network N with a particular factorization F is the underlying causal representation of reality," and so a particular experiment measures data and then we calculate "the aforementioned causal network would generate this data with probability P" for various hypothesized causal networks.
For situations where you can control at least one of the nodes, it's easy to see how you can generate data useful for this. For situations where you only have observational data (like the history of human evolution, mostly), then it's trickier to determine which causal network(s) is(are) best, but often still possible to learn quite a bit more about the underlying structure than is obvious at first glance.
So suppose we have lots of historical lives which are compressed down to two nodes, A which measures "anger" (which is integer-valued and non-negative, say) and C which measures "children" (which is also integer valued and non-negative). The story "anger is spurious" is the network where A and C don't have a link between them, and the story "anger is reproductively useful" is the network where A->C and there is some nonzero value a^* of A which maximizes the expected value of C. If we see a relationship between A and C in the data, it's possible that the relationship was generated by the "anger is spurious" network which said those variables were independent, but we can calculate the likelihoods and determine that it's very very low, especially as we accumulate more and more data.
Third, likelihood ratios are good when you know you have a complete set of potential explanations. And you generally don't.
Sure. But even if you're only aware of two hypotheses, it's still useful to use the LR to determine which to prefer; the supremacy of a third hidden hypothesis can't swap the ordering of the two known hypotheses!
Nassim Taleb's black swans are precisely the beasties that appear out of "something else" to bite you in the ass.
Yes, reversal effects are always possible, but I think that putting too much weight on this argument leads to Anton-Wilsonism (certainty is necessary but impossible). I think we do often have a good idea of what our meta uncertainty looks like in a lot of cases, and that's generally enough to get the job done.
I have only glanced at Pearl's work, not read it carefully, so my understanding of causal networks is very limited. But I don't understand on the basis of which data will you construct the causal network for anger and children (and it's actually more complicated because there are important society-level effects). In what will you "see a relationship between A and C"? On the basis of what will you be calculating the likelihoods?
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