Causality is dead" is a popular notion in the Big Data community. They argue that given enough statistical evidence, it's no longer necessary to understand why things happen - we need only know what things happen together. David Ritter published some excellent thoughts on the subject.https://hbr.org/2014/03/when-to-act-on-a-correlation-and-when-not-to

Discuss the circumstances when it makes sense to act on a correlation and when one should not act. Many predictive models are based on correlated variables. How should such models be developed and used so the predictions can be acted upon? Some of these types of models are very complex. Do we need to understand why they work?