I strongly agree with this. Not to mention parameter interpretability and, in the case of Bayesian models, uncertainty estimates and convergence diagnostics. Such things are very important when making decision under uncertainty. Kaggle competitions and empirical benchmarks are very biased samples of model performance in real life.

I feel these two things often influence too much the course of Machine Learning research and communities, and this is not good. Most ML researchers and pratictioners are barely aware of the latest advances in parametric modelling, which is a shame. Multilevel models allow you to model response variables with explicit dependent structures. This is done through random (sometimes hierarchical) effects constrained by variance parameters. These parameters regularize the effects themselves and converge really well when fitting factors with high cardinality.

Also, multilevel models are very interesting when it comes to the bias-variance tradeoff. Having more levels in a distribution of random effects actually DECREASES [1] overfitting, which is fascinating.

[1] https://m-clark.github.io/posts/2019-05-14-shrinkage-in-mixe...