It is widely understood that statistical correlation between two variables ≠ causation. But despite this admonition, people are routinely overconfident in claiming correlations to support particular causal interpretations and are surprised by the results of randomized experiments, suggesting that they are biased & systematically underestimating the prevalence of confounds/common-causation. I speculate that in realistic causal networks or DAGs, the number of possible correlations grows faster than the number of possible causal relationships. So confounds really are that common, and since people do not think in DAGs, the imbalance also explains overconfidence.
Full article: http://www.gwern.net/Causality
Yes. Even more generally... might be an over-application of Occam's razor: insisting everything be maximally simple? It's maximally simple when A and B correlate to infer that one of them causes the other (instead of postulating a C common cause); it's maximally simple to explain inexplicable events as due to a supernatural agent (instead of postulating a universe of complex underlying processes whose full explication fills up libraries without end and are still poorly understood).
That sounds more like a poor understanding of Occam's razor. Complex ontologically basic processes is not simpler than a handful of strict mathematical rules.