[borroka]

While I agree and it is surprising that multi-level/hierarchical modeling is rarely applied in industry (I used them extensively in academia and industry), dealing with hundreds or thousands of random effects in large data sets, especially in non-linear models, is a computational nightmare. And the benefits may not warrant those nightmares.

[RA_Fisher]

Finally multi-level/hierarchical modeling is starting to permeate industry thanks to Stan and company.

I use hierarchical modeling regularly to help build Zapier. So do other companies like Generable: https://www.generable.com/

I suspect hierarchical models will become the next “new” hot data structure in software engineering due to their ability to compact logic. https://twitter.com/statwonk/status/1363104221747421184?s=21

[borroka]

I don't know about permeating the industry. I know for example that the model that Airbnb used 3 years ago (things may have changed in the meantime) to forecast occupancy was a random-effects model maintained by a single person in Europe. I don't know about the penetrance of Generable and companies providing similar probabilistic modeling solutions, although I hope they succeed.

When I was working for one of the FAANGs, I was the only one using random effects models (that I know of), in particular non-linear random effects models with ~ hundreds of random effects. I was using a language/tool faster than Stan (fitting the same model with Stan would have taken hours, or more likely days), but making the models converge was always challenging. In addition, since most of my colleagues had a CS background and were in love with the latest not interpretable, brute force algorithm, and were scared of a more statistical approach they made no effort to learn, I faced pushback and skepticism despite the model working very well.

I love random effects model, and I build my technical career on them.