Comment by tomrod

This is sort of the basis of econometrics, as well as a driving thought behind causal inference.

Econometrics cares not only about statistical significance but also usefulness/economic usefulness.

Causal inference builds on base statistics and ML, but its strength lies in how it uses design and assumptions to isolate causality. Tools like sensitivity analysis, robustness checks, and falsification tests help assess whether the causal story holds up. My one beef is that these tools still lean heavily on the assumption that the underlying theoretical model is correctly specified. In other words, causal inference helps stress-test assumptions, but it doesn’t always provide a clear way to judge whether one theoretical framework is more valid than another!