This is a pretty straightforward question and I guess I will get a negative score here - I was so happy improving my points in here lol -, but I couldn't find anywhere and even though I believe I know the answer, I would like to assess with more senior Data Scientists.
When dealing with Linear Regression, it's often recommended to remove features that presents multicollinearity so we could get the correct interpretability (even though it's not a bias problem). The way we do this is using Variance Inflation Factor. Should we do this recursively? I mean, should I check VIF, then remove some of then, run again, remove others and so on? Is there anything we need to check besides VIF in this process?