A prediction asks what is likely in the observed setting. A causal question asks what would change if the same eligible people received a different action. Write the estimand: the quantity you want to estimate, with its population, action, comparison and outcome horizon.
For example: “Among currently eligible subscribers, how does assigning this retention offer rather than no contact change 30-day retained contribution per assigned account?” This policy comparison is more useful than asking whether some model “reduces churn.”
For each account, imagine two potential outcomes: retention if assigned the offer, Y(1), and
retention if assigned no contact, Y(0). You see only one, so ordinary data never labels the
difference for one person. An average treatment effect compares the averages, and a conditional
effect compares them within a stated context. Neither tells you for certain which person would have
changed their decision.