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Stuart_Armstrong comments on Counterfactual resiliency test for non-causal models - Less Wrong
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How do you judge the plausibility of a counterfactual?
You say "we can imagine" some of these scenarios more easily than others. But our imaginations aren't magic. There are plenty of things I can imagine that on closer examination are virtually impossible. And plenty of real things that I couldn't imagine until I knew about them.
If we had a good causal model, we could apply it. But we're usually interested in non-causal models precisely when causal models are intractable.
If the counterfactuals' plausibilities boil down to "I said so", then so does the entire argument.
Schematically:
1) model M claims that X happened necessarily the way it did, for reasons we don't understand.
2) A critic presents a counterfactual C where X doesn't happen that way, while C is still consistent with the model. To argue that C changes X, he uses causal reasoning.
3) The defenders of the model must now either abandon the model, show that C is not actually consistent with M, or refute the claim that C changes X.
4) The conversation has now progressed beyond direct claims of likelyhood or not of M.