In "Superforecasting", it's a bit clearer. What they end up doing is asking you to predict an EVENT by a SPECIFIC TIME. In other words, what is the probability

In "Superforecasting", it's a bit clearer. What they end up doing is asking you to predict an EVENT by a SPECIFIC TIME. In other words, what is the probability that "x" will happen by "DATE Y". In this case, by the time DATE Y takes place, you have a clearly verifiable binary outcome, which you can use to judge whether the forecasters were correct or not. There is some art to this -- the "event" in question needs to be very well-defined... I think the example they're using is a bit odd and they have much better examples in their book. The example of a counterfactual event (i.e., 'if X happens then estimate the likelihood of Y happening by DATE Z') adds a great deal of confusion here because you're predicting conditional probabilities. Still possible to verify but to your point, much more difficult. At this point, you're asking forecasters to build a probabilistic graph and estimate causality, which is becoming more of a science (e.g., Judea Pearle's work) but still lots to do in that space. Anyway, I'm digressing.