[dhfhduk]

The theory you mention about additive noise, is clever but doesn't strike me as robust at all. It's been awhile since I looked at that literature, but when I did it seemed really unrealistic, that at some level it reduced to assuming that all deviations from normality are interpretable in terms of the desired causal inferences. There might be some scenarios where you could reduce the variables involved to the point where that idea is feasible, but otherwise it seemed really unbelievable to me.

Part of me wants to dive into this causality modeling, because it seems up my alley, but I'm very sceptical of it showing anything definitive. I do observational research, but short of a priori randomization, I'm sceptical of any claims to causality. Even then, with experiments, I'm deeply sceptical unless something has been replicated across various secondary conditions by multiple distinct groups.

Modern causality theory and modeling has definitely raised the bar in terms of what we say about data, and I love it, but sometimes I wonder if causality is a red herring. Even with hard experimental evidence, I'm tempted to not interpret it beyond "when someone does X, this tends to happen."

[gwern]

It does seem too good to be true, but they've compiled a real-world dataset of causal relationships, and the additive-noise and fancier ML algorithms do seem to infer the right direction well above chance, so there's at least something there.