A more useful lesson might be skepticism about personalized medicine. Personalized medicine – the idea that I can read your genome and your blood test results and whatever and tell you what antidepressant (or supplement, or form of therapy) is right for you has been a big idea over the past decade. And so far it’s mostly failed. A massively polycausal model would explain why. The average personalized medicine company gives you recommendations based on at most a few things – zinc levels, gut flora balance, etc. If there are dozens or hundreds of things, then you need the full massively polycausal model – which as mentioned before is computationally intractable at least without a lot more work.
(You can still have some personalized medicine. We don’t have to know the causes of depression to treat it. You might be depressed because your grandfather died, but Prozac can still make you feel better. So it’s possible that there’s a simple personalized monocausal way to check who eg responds better to Prozac vs. Lexapro, though the latest evidence isn’t really bullish about this. But this seems different from a true personalized medicine where we determine the root cause of your depression and fix it in a principled way.)
Even if we can’t get much out of this, I think it can be helpful just to ask which factors and sciences are oligocausal vs. massively polycausal. For example, what percent of variability in firm success are economists able to determine? Does most of the variability come from a few big things, like talented CEOs? Or does most of it come from a million tiny unmeasurable causes, like “how often does Lisa in Marketing get her reports in on time”?
Maybe this is really stupid – I’m neither a geneticist or a statistician – but I imagine an alien society where science is centered around polycausal scores. Instead of publishing a paper claiming that lead causes crime, they publish a paper giving the latest polycausal score for predicting crime, and demonstrating that they can make it much more accurate by including lead as a variable. I don’t think you can do this in real life – you would need bigger Big Data than anybody wants to deal with. But like falsifiability and compressability, I think it’s a useful thought experiment to keep in mind when imagining what science should be like.
Scott Alexander, “The Omnigenic Model As Metaphor For Life”, Slate Star Codex, 2018-09-13.
February 4, 2021
QotD: The (as-yet-unfulfilled) promise of “personalized medicine”
Comments Off on QotD: The (as-yet-unfulfilled) promise of “personalized medicine”
No Comments
No comments yet.
RSS feed for comments on this post.
Sorry, the comment form is closed at this time.
