There are lots of ways to gain knowledge other than by looking at correlations. For example you can run experiments. There was a guy named Edward Jenner who was interested in avoiding smallpox. He ran an experiment and it worked. The world learned how to avoid smallpox and there were no correlations in sight...
At the age of 13, Jenner was apprenticed to Dr. Ludlow in Sodbury. He observed that people who caught cowpox while working with cattle were known not to catch smallpox. He assumed a causal connection. The idea was not taken up by Dr. Ludlow at that time. After Jenner returned from medical school in London, a smallpox epidemic struck his home town of Berkeley, England. When he advised the local cattle workers to be inoculated, the farmers told him that cowpox prevented smallpox. This confirmed his childhood suspicion, and he studied cowpox further, presenting a paper on it to his local medical society.
Saying “He ran an experiment and it worked” hides the initial correlational observation that let him to try that experiment.
I think so. If you want to separate them how would you say “people who get pustules from working with cattle are less likely to catch smallpox” differs from “people who give blood are less likely to have heart disease”?
There are lots of ways to gain knowledge other than by looking at correlations. For example you can run experiments. There was a guy named Edward Jenner who was interested in avoiding smallpox. He ran an experiment and it worked. The world learned how to avoid smallpox and there were no correlations in sight...
Wikipedia:
Saying “He ran an experiment and it worked” hides the initial correlational observation that let him to try that experiment.
It seems to me that you want to call all observational data “correlations”.
I think so. If you want to separate them how would you say “people who get pustules from working with cattle are less likely to catch smallpox” differs from “people who give blood are less likely to have heart disease”?