Normally, causation has a couple confounders for correlation, such as - inverse causation - common cause - random correlations The first two should be relativ

Normally, causation has a couple confounders for correlation, such as - inverse causation - common cause - random correlations The first two should be relatively stable and reproducible, and we could then proceed to find out about causation with an intervention study (e.g. if "good education" directly causes "good job prospects", will it help if we give people good education that wouldn't normally get one? Or does it improve people's educational achievement when we give them better access to jobs?) The third isn't reproducible, but should normally be relatively rare. Why we're seeing more and more non-reproducible results is usually - People fishing around in data sets for correlations, or tweaking experiments until they find a correlation Because of this, we end up with a great deal of correlations that have nothing to do with causation. For a related but different perspective, see this article ("Language is never, ever random", Adam Kilgariff) http://www.kilgarriff.co.uk/Publications/2005-K-lineer.pdf http://www.kilgarriff.co.uk/Publications/2005-K-lineer.pdf