A common mistake in media mix modeling (MMM) is the belief that "more control variables are always better." This mindset comes from treating statistical models as causality machines. The thinking goes: If we just input everything, we’ll get perfect causal inferences on the other side. That’s just not true. In reality, overloading your model with control variables can create more problems than it solves. It can lead to overfitting, introduce instability, and make your model so complex that it’s impossible to interpret.  The goal of an MMM is not to estimate every possible factor that affects your business—that’s impossible. Instead, the focus should be on getting correct causal estimates for what you actually control – your marketing levers.  In this…