Econometric models typically do not say anything about causality. For example, the coefficients in OLS are just correlation, i.e. one unit increase in X is associated with $\beta$ increase in Y. Let's say that we regress "happiness" on "being married." It's impossible to tell which way does the causal relation flow, or if "happiness" and "being married" are both caused by a third factor altogether.
In contrast, causal models explicitly think about when we can claim that an effect is causal.
From the econometrics I have seen there is a heavy focus on finding correlations and at best argumentations whether such correlations are plausible causal relationships.
Good scientific method suggests working in the opposite direction: first make a hypothesis, then test it. Working backwards from the statistical test to the hypothesis is... troublesome.
Many academic fields are overlapping, yet have a surprising ignorance of each other. Tenure is largely independent of one's knowledge outside one's own field.
In contrast, causal models explicitly think about when we can claim that an effect is causal.