It’s evidence for the claim that language is primarily a tool, and as such follows metacognitive optimization, efficiency in this case. For me, my native polish don’t depends on word ordering, therefore one can change the meaning of the sentence on the fly, which is helpful in oral discussions.

CI is great to defend and criticize methodological ideas. Similarly to metascientific findings, it often shows that actual science is bad and good science is hard. Purely statistical work, and offloading causal conclusion to reader is easier. Actual science condition is part of adoption problem.

You mean knowing part of mechanisms constituting object of inquire is not sufficient. Agree. But it is necessary, although I have wider concept of mechanistic knowledge, probably closer to yours concept of some „theory” (not the theory!). Like knowing the symptoms, not failing selection bias, etc.

Any example of causality without mechanistic knowledge?

But how can you have better evidence without mechanisms? I mean, how do you know that in fact some empirical evidence is better w/o even minimal mechanistic knowledge?

This is practically but not theoretically correct. If one would have fully specified mechanisms in form of structural causal model, even in the case of fat handed intervention one would predict correctly. The problem is that we have such knowledge only for constructed things (see causal chambers)

@hayoungsong.bsky.social gz! What about negative „aha” like spotting the contradictions? Moreover, if we would combine both negative and positive aha in study, I expect the stronger the contradiction, the stronger the positive insights that would resolve it. Would be lovely to se it!

True, the link between causal models and their target systems remains fuzzy in causality. Not sure we can grasp this framework from stochastic thermodynamics without mastering the framework foundations first. Getting into causality is though.

Do you mean that if Y is caused by $X then difference between changing $ or X is meaningful only in dynamic systems ? Changing parameter is changing causal relationships on all phenomenon level, changing X is changing its state on units level. They are not same, even with same numerical effect.

It is acceptable, but functional opacity has to be countered by external evaluation of the outcomes. For example competent judges should grade random 10% of the outcomes.

Agree. I was not saying that it’s wrongly selling itself as something more.