> What distinguishes an intervention vs just normal observable randomness? The difference is your knowledge that value of some variable is controlled and indep

> What distinguishes an intervention vs just normal observable randomness? The difference is your knowledge that value of some variable is controlled and independent from anything else in this situation. If causal model is some kind of a puzzle, then you could see how it would work if some parts of model are made dysfunctional by your intervention. X causes Y means that changes in X lead to changes in Y. If you changed X than Y would change also. By observation you can witness correlation, which says nothing about causes. You could add temporal element, like "X happened earlier than Y" it means that Y couldn't be the cause of X. But it doesn't answer the question about cause, it just rejects some hypothesis about cause, and is all uncertain: we could easily devise an example when such a rejection would be a mistake. For example people could act because they are able predict something will happen. So something is not happened yet, but already is the cause of some events. But when changes to X were made by you, then you know exactly when X was changed, you know that it was no changed to changes of Y or some Z which is a confounder. Here we also could make a mistakes, for example, because we do not know exactly how we make decisions and our behavior could be a confounder or mediator or something like. So we devised randomized controlled trials, double-blind experiments to make experiment really independent. > I guess it's kind of the debate about where determinism ends and free will begins. No need to get to that length. If your behavior is independent from the process which you research, than you'll get a "practical free will": free from influences of studied variables. This practical and situational free will definition is enough for learning about causes.