Correlation is not causation …

… smart-asses often point out when someone mis-uses some statistical or perceived correlation. That’s true, and easy enough when potential common causal connections are obviously available, but that’s quite *unusual* in the “real world”.

Causation is fundamentally mysterious even in simple Newtonian billiard-ball cases, or our daily expectation of sunrise, but at the common sense level causation is really about empirical certainty of expectation and prediction – *if this then that*, whether precise “physical” mechanisms are or are not clearly available.

In the real world however mechanisms are rarely simple or precise, so causation is really a testable theory of *why if this then that*, where the why is controllable. But real life is never a repeatable experiment, except maybe with drosophilla, and rarely with humans. And in real life caustion may involve many variables over many space-time-scales. It’s complicated.

Correlation is useful even with negligible knowledge of causation. More knowledge of causation always helps. (Now, let’s read this:

“Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS” by Stefano Canali

(Hat tip Timandra Harkness.)

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