I am working in a project using linear regression with a data set that has a lot of multicollinearity. From what I could understand from my research about the topic, I can separate the issues caused by multicollinearity in:
- Problems related to the modelling itself, like the instability of the regression coefficients.
- Interpretability problems
I found mainly 3 methods that can help, but I don't think they can solve the intepretability issue:
- Using shrinkage methods such as Ridge and Elastic Net
- Eliminating features with high VIF
- Combining features suffering from multicollinearity
For example, let's say I have a dataset where my dependent variable is "health" and my independent variables are different types of medicines.
My questions are: 1. What if I want to compare the impact of each medicine to the health? 2. What if I get a dataset in which the intakes of medicines are differently correlated?
Thanks a lot!