Forecasting vs econometrics, how is regression used differently? I have this homework question:


  Q: Suppose that a marketing company is seeking to identify customers interested in their product. 
  They regress the number of sales in an area on the average education level in that area. Does the coefficient likely represent a causal effect? Why? Is this a problem? Explain.
  
  A: The coefficient is not likely to represent a causal effect, it
  merely shows how the number of sales and average education move
  together. That relationship could be causality, reserve causality, an
  omitted variable could be determining both, or the two could be
  simultaneously determined. This isn’t a problem because the company
  isn’t trying to cause people to have more interest, they just want to
  predict if they’re interested or not.


So now I want to know, how is a regression used differently in a forecast vs a causal analysis?

If I'm looking for predictive power vs causality, how might the regression models be different, if at all?

Or how might the results be interpreted 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.