What?! Causal Drivers from Cross-Sectional Survey Data. How? (LiNGAM)
How often do we as researchers want to attribute causality to what we know is just correlational? It can be so easy to mix up correlation with causation! And in our industry we do driver analyses so often. The mix-up is easy: for example, ice cream sales and drownings both increase during summer, but it doesn’t mean that one causes the other. Correlation shows that variables change together. Causation explains which variable leads to the change in the other, and this is very important when deciding where to invest or which factors really have the effect. This distinction becomes especially important in advanced analytics approaches, where decisions depend on identifying true drivers rather than coincidental patterns. The Analytics Team has been finding causal drivers for over 7 years now applying cutting-edge validated methods. And we have built interactive maps and simulators around these leading insights. These take typical drivers to the next level of practical applicatio...