Confound it! Correlation is (usually) not causation! But why not?

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://​​­​​Causality