Multicollinearity is one of the enemies of Marketing Mix Modeling (MMM).
Multicollinearity generally occurs when there is a high correlation between independent variables.
Multicollinearity does not affect predictive power of the model but it causes a lot of issues when one needs to infer or attribute the changes in dependent variable to the independent variables.
In the context of Market Mix Modeling or other attribution models, Multicollinearity can be a big problem.
One solution that many adopt to solve multicollinearity is – ‘Drop the variable’.
But solving multicollinearity through this method leads to another problem – The Omitted Variable Bias (OVB).
OVB in turn leads to Endogeneity. Endogeneity happens when your independent variables are correlated with the error term.
Since in MMM we are also looking for causal structure, endogeneity can be as big a headache as multicollinearity.
The link to an excellent example of how OVB leads to Endogeneity is provided under resources.
There are many techniques to overcome endogeneity. But that is a topic for future posts.
Resources:
How OVB leads to endogeneity – https://stats.stackexchange.com/a/171628
