Forecasting vs econometrics, how is regression used differently?
In forecasting, we just want to predict what will happen. So assuming that there is a certain degree of stability in the relation, so that the relation is valid outside the sample, we estimate how past sales co-vary with education level (or whatever), and we say "so if we move into New Area "X", and apply the same sales strategy, since in "X" the educational level is "Y" we predict that our sales will be "Z", given the estimated co-movement relation".

In an econometric analysis, we certainly are interested in estimating the same relation, but if we want to perform also causality analysis, identifying assumptions become necessary and specific models arise, (like the "Differences-in-Differences" approach for example), designed to detect and assess causality.

By the way the first sentence in the "A" part of the question is "wrong in the other direction": the phrase "is not likely to represent a causal effect" is inaccurate: it is "as likely" to represent one, as it is not, since basic regression is "agnostic" about causal effects, it is just about detecting and estimating correlation.