When making theories about data, we often want to assert that "X causes Y"—for example, "the page getting slower caused users to click less." You can not simply establish causation because of correlation. By considering how you would validate a theory of causation, you can usually develop a good sense of how credible a causal theory is.
Sometimes, people try to hold on to a correlation as meaningful by asserting that even if there is no causal relationship between A and B, there must be something underlying the coincidence so that one signal can be a good indicator or proxy for the other
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