Granger Causality - A possible Feature Selection Method in Marketing Mix Modeling (MMM)

Feature Selection (aka covariate/independent variable selection) in any regression problem is a crucial task.Incorporating features in the model that best predicts or explains the dependent variable is both an art and science.There are many feature selection methods.In MMM, incorporating the right features in the model is a very tricky proposition.Traditionally, in MMM, feature selection is done using a combination of correlation approaches and domain informed judgments about the variables.However, this is not a robust approach since this can lead to Bias in the model.As a workaround, we have been constantly experimenting with other feature selection methods.In this paper, we propose that Granger Causality could be a potential feature selection method.We illustrate how the Granger Causality (if carefully used) can aide in feature selection in an MMM setup.