(This article was originally published at No Hesitations, and syndicated at StatsBlogs.)
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 much of E/S crowd views "ML" and "data science" as the same old wine in a new bottle. (An old joke is perceptive. 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...
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