Nobel in econ just got awarded to folks doing causality research (and labor market stuff). If you're not already in the loop on the whole causality business, worth reading up. Why it should matter to you, firm, and clients: causal models are good for attribution ($$$), are more robust when repurposed (aka always), and natively explain model scores.
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Has anyone been terminated from Novartis?
THERE'S A WHOLE NOTHER HALF HOUR OF THIS
Additional Posts in Data & Analytics Consultants
What is a data lake in basic terms?
Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
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Pearl's rebuttal to Imbens's paper
http://causality.cs.ucla.edu/blog/index.php/2020/01/29/on-imbens-comparison-of-two-approaches-to-empirical-economics/