Plenty of software has reinvented something that was done prior. Even if the outcome hasn’t improved significantly, how the outcome was achieved still has plenty of value. Just as we’ve seen in software, repeatedly finding better and efficient processes to achieve largely the same technical outcome.
Throwing the machine learning label into the mix always pushes the expected results to a higher standard.
In contrast, demand estimation is a statistical or rather econometric problem that targets exactly the areas that ML has yet to explore: Causal analysis, censoring of dgp and related to this but distinct in the literature, identification and endogeneity.
The authors in this article do not show any ML, its all mainline stats.
ML is used, even in these areas, for the things it does best. So it is a misnomer to separate nowadays.
But i disagree that one wozld expect ML to do better in this area.
Look at websites doing ab testing, which is certainly not ML but experimental stats.
I should note that I meant people tend to expect more from ML outputs when they hear the words ML/AI while that may not be the case and while it could simply be a process optimizations making the whole original output easier to achieve for personal new to it or gives experts a shorter path.
Throwing the machine learning label into the mix always pushes the expected results to a higher standard.