Variable Selection: What your model can't tell you

James Donahue

Variable selection is often left up to an algorithm. However, controlling for some variables can improve measurement accuracy, and thus overall performance. On the other hand, certain "bad" controls can block pathways of relationships between variables that we want to preserve or create spurious correlations. Using real and simulated data, I explain when to reconsider your controls, and why that may significantly improve model accuracy.