My last post focused on one key distinction between machine learning (ML) and econometrics (E): non-causal ML prediction vs. causal E prediction. I promised later to highlight another, even more important, distinction. I'll get there in the next post.
But first let me note a key similarity. ML vs. E in terms of non-causal vs. causal prediction is really only comparing ML to "half" of E (the causal part). The other part of E (and of course statistics, so let's call it E/S), going back a century or so, focuses on non-causal prediction, just like ML. The leading example is time-series E/S. Just take a look at an E/S text like Elliott and Timmermann (contents and first chapter here; index here). A lot of it looks like parts of ML. But it's not "E/S people chasing ML ideas"; rather, E/S has been in the game for decades, often well ahead of ML.
For this reason the E/S crowd sometimes wonders whether "ML" and "data science" are just the same old wine in a new bottle. (The joke goes, Q: What is a "data scientist"? A: A statistician who lives in San Francisco.) ML/DataScience is not the same old wine, but it's a blend, and a significant part of the blend is indeed E/S.
To be continued...