I mean this is an extremely difficult thing to disentangle in the first place. It is very common for people in one breath to recite that correlation does not equate to causation and then in the next breath propose causation. Cliches are cliches because people keep making the error. People really need to understand that developing causal graphs is really difficult, and that there's almost always more than one causal factor (a big sticking point for politics and the politicization of science, to me, is that people think there are one and only one causal factor).

Developing causal models is fucking hard. But there is work in that area in ML. It just isn't as "sexy" because they aren't as good. The barrier to entry is A LOT higher than other type of learning, so this prevents a lot of people from pursuing this area. But still, it is an necessary condition if we're ever going to develop AGI. It's probably better to judge how close we are to AGI with causal learning than it is for something like Dall-E. But most people aren't aware of this because they aren't in the weeds.

I should also mention that causal learning doesn't necessitate that we can understand the causal relationships within our model, just the data. So our model wouldn't be interpretable although it could interpret the data and form causal DAGs.