[psandersen]

I suspect we'll see causal techniques start merging with more traditional AI/ML tools over the coming years.

Causal forests are an example that extends random forests, but I imagine a lot of the value in current pipelines would be to use causality as regulariser. This could be a parameter that controls the weight of established causal links, or it could be as a scaffold; e.g. a first 'causal pass' is used to establish constraints (monotonicity, conditional variable selection, reject changes that result in predictions inconsistent with the initial causal model when there is a strong causal model etc).

RL is likely more promising. If agents could be made to search for causality in an environment these relationships could be made much harder to unlearn which would then enable more efficient exploration & incremental learning. Framed this way causality guides attention, limits the search space and locks in learning.

I've got some quarantine reading/experiments to try! :)

[6gvONxR4sf7o]

I totally agree as causation as a sort of generalization enhancer akin to regularization. In stats, there's the notion of some "true" parameter that's trying to be estimated, but you get all sorts of systematic errors creeping in if you estimate it wrong. If you get a good estimate of it, though, you've learned something "true" and that generalizes much better than systematically wrong versions. Like, if you figure out F and m and that F=ma, you're going to make really good predictions, regardless of how far from your original training you are. Other truths are still pretty limited (like the example of a social study finding the true treatment effect of something on affluent white 20 year olds in LA), but the scientific ideas of internal and external validity still apply quite nicely.