Class imbalance in the predictors


I know we have to take care of class imbalance in the outcome variable.

But what about class imbalance in the predictors? When is it important for our models?

Could you explain what that means? “Class imbalance” by definition refers to an imbalance in classes for a classification problem, i.e. the outcome. Based on that definition it doesn’t make much sense to talk about a “class imbalance” in predictors.

Say one of your predictors is a categorical variable.
If it’s very imbalanced I guess it will influence the results of your analysis.
For example you have 10000 men and 100 women.

I feel like I can only give you the same answer I gave last time: this sounds a lot like a basics-of-causal-inference question that doesn’t have anything to do with Julia specifically. The Imbens/Rubin textbook is a very good introduction to the subject, and I’m sure there are also loads of specific resources for your field (as epidmiology is one of the key contributor field of causal inference methods).