Shrinking would work to some extent, by borrowing information from other variables. This is better done in a Bayesian context with multilevel models. Also weak

Shrinking would work to some extent, by borrowing information from other variables. This is better done in a Bayesian context with multilevel models. Also weakly informative priors, which have regularizing effects. Actually these two things are a big focus area of Stan, which is the probabilistic programming language built by the group which writes that blog.