Commentary: some remarks on the seminal 1904 paper of Charles Spearman 'The proof and measurement of association between two things'.

摘要

With the recent surge of interest in epidemiology and statistics in causal inference, the adage 'correlation is not causation' has been repeated so often that another salient feature of the relationship of correlation to causation seems virtually to have been forgotten: that correlation is a necessary (but not sufficient) condition for causation. [In fact, there are theoretical exceptions to this as well; for example, there could be a causal relationship that has a U-shaped dose-response curve-in this case, the Spearman (and Pearson) correlation coefficients will be 0.] It is rather easy to come up with theoretical examples-straw man arguments, in fact-where correlation is present and causation not. For example, having 'yellow fingers' and getting lung cancer are correlated, but it is intuitively obvious-although this may not always have been the case-that yellow fingers do not cause lung cancer and lung cancer does not cause yellow fingers.