I wonder if there are intermediary steps to getting to better at causality in ML. Causality is an abstraction of a whole set of a lot of different levels of pro

I wonder if there are intermediary steps to getting to better at causality in ML. Causality is an abstraction of a whole set of a lot of different levels of problems. In terms of concrete problems earlier than causality. E.g. toddlers I think get object permanence before causality, and I think ML might struggle with that too. Edit: then the next interesting thing after permanence is maybe obj path prediction, then you have an interesting basis for some level of causality inference because you have a prediction and some set of conditions that might disrupt the prediction.