The Philosophy Of Models (Regression): The Right Way
If you cite, enjoy, or create “research” or “studies”, this post is a must. I’ve eschewed all math (given next time in The Wrong Way) and focused entirely on the idea.
I hope you will both read and pass it on.
Lecture
We have, at long last, arrived at statistical models. We started from nothing–in a quite literal sense–and built that into our entire system of epistemology, of certainty and uncertainty, which is to say, logic and its completion probability.
I don’t want to continuously re-cover those topics, yet some of that will be inevitable. Because great swaths of it will be forgotten in the mad giddy happiness of modeling. My prophecies that you will have forgotten all warnings about models will be realized. Sadly.
Our goal, which you must never forget, but will, is to discover the full cause–formal, material, efficient, and final causes–of those things modeled, and the conditions under which those causes come about. Falling short of cause, which we almost always will, we come to correlation, which is not cause, but which can be used to make predictions.
Almost all models will be correlational, even if they have causal components. The biggest mistake made is to suppose My Correlation Is Causation, just because it is your correlation. The second, and a whopper, as we have discussed ad nauseam, is to say your wee P confirmed your correlation is a cause, a double-plus-bad fallacy.
All of you will make it.
We start with the most ubiquitous model: regression. It is simple. It is everywhere, and, with incredibly vanishingly rare exceptions, used wrongly. In error. Mistakenly. Badly. Horribly.
Yet today will will do it right. Next time we do it wrong. Which is to say, the way most do it.
source wmbriggs.substack.com
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