What is the conceptual difference between causal inference and 'prediction'
Correct me if I'm wrong here, but after some more reading and thinking this is what I took away: both the term 'prediction' and 'cause' here refer to the maximum likelihood of a distribution ($Y$), meaning the probability of an event ($X$), given sensory data ($Y_{1}$) and the likelihood given prior data ($Y_{2}$) .
Even though maybe seemingly orthogonal to some (like me), the terms here would actually represent the same thing (i.e. no difference).
Please post a better answer if this is partially or completely incorrect =)