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>correlation without any strongly proven causality How would one really prove causality or does it even matter. When I think of science, I try to think of it

>correlation without any strongly proven causality How would one really prove causality or does it even matter. When I think of science, I try to think of it like how I think of physics, where we create a model that best describes the evidence but which doesn't have any guarantee of being how things really work. Take Newtonian gravity. It is a pretty good model that describes a lot of basic interactions. Given a state at a given moment in time, it lets us determine things going forward or backwards (though for more complex physics, backwards stops working because of assumptions and estimates in the model). But at the same time, it is wrong. More complex physics shows there is a model that fits even more experimental data which contradicts what we thought was happening in the Newtonian model. Mass doesn't attract mass. Mass bends space time and impacts objects traveling through it in such a way that it appears mass attracts mass (though even this may end up being wrong and something else entirely is at play). So, is it really important to describe why a ball falls to the ground when I release it, or is it good enough to have a system that describes the interactions enough that I can apply it to problems? If I solve a problem, say what angle and what speed I need to throw a ball to get over fence, does it really matter if I think the ball falls back to earth because mass attracts mass or because mass bends spacetime? Or is it just important for my equations to be accurate enough that any error is less than what is innate in applying the solution to real life (measuring the exact height of the fence, throwing at an exact angle). What does applying a similar mind set in something dealing with vastly more complicated systems, such as in medicine, looks like?

bostonpete 8y ago
There's a big difference between knowing that A causes B and knowing how A causes B. For most purposes relating to health it's sufficient for a layperson to know just that A does cause B, no matter how. But if A is just correlated with B and doesn't cause it, that makes a world of difference.
- PurpleBoxDragon 8y ago
  But you can never really know if A causes B or not. You can only create models, some that have A causing B and others than only have them correlated, and get rid of models as you find contradicting data. What actually causes a ball to drop to the ground when I release it? We don't know. The best model (that I know of) is the bending of space time, but that isn't the real answer and may one day be overturned just as the older idea of mass attracts mass. I guess the question is, are we sure enough of our model to be able to trust the airplane isn't going to drop to the ground like the ball does, and how do we achieve equivalent certainty in the biological sciences.
thrmsforbfast 8y ago
> So, is it really important to describe why a ball falls to the ground when I release it, or is it good enough to have a system that describes the interactions enough that I can apply it to problems?... does it really matter if I think the ball falls back to earth because mass attracts mass or because mass bends spacetime? I think you answer this question in your own post. The answer depends entirely on your goals. "All models are wrong, but some are useful". If all we ever wanted to do was shoot cannon balls over/into walls accurately, then we probably would've never bothered inventing modern physics. But we did want to do other things, so we built better models. It's worth noting that some of those things we wanted to do were more philosophical than others, e.g., engineering ("put satellites into orbit"), explaining empirical observations that seem important ("explain how electricity really works") and philosophical ("understand the nature of reality") are all answers to the question "what are you goals?" that have inspired progress toward better models in physics. > What does applying a similar mind set in something dealing with vastly more complicated systems, such as in medicine, looks like? It takes a certain amount of philosophical sophistication to realize that even the most perfect model is still a model and to then reason through what that entails -- epistemologically -- for less perfect models and for the entire scientific enterprise.
- PurpleBoxDragon 8y ago
  >what are you goals? I'm personally a fan of the 'make a better model to have a better model and let someone else figure out the application' model, but that one is harder to get funding for.
skybrian 8y ago
It matters for decision-making. If A causes B then forcing A to happen artificially (without changing anything else) will make B happen too. If it's not a cause then it won't. The correlation might always happen in the original environment, but it doesn't survive intervention. This does matter in physics too.

bostonpete 8y ago

There's a big difference between knowing that A causes B and knowing how A causes B. For most purposes relating to health it's sufficient for a layperson to know just that A does cause B, no matter how. But if A is just correlated with B and doesn't cause it, that makes a world of difference.

PurpleBoxDragon 8y ago
But you can never really know if A causes B or not. You can only create models, some that have A causing B and others than only have them correlated, and get rid of models as you find contradicting data. What actually causes a ball to drop to the ground when I release it? We don't know. The best model (that I know of) is the bending of space time, but that isn't the real answer and may one day be overturned just as the older idea of mass attracts mass. I guess the question is, are we sure enough of our model to be able to trust the airplane isn't going to drop to the ground like the ball does, and how do we achieve equivalent certainty in the biological sciences.

thrmsforbfast 8y ago

> So, is it really important to describe why a ball falls to the ground when I release it, or is it good enough to have a system that describes the interactions enough that I can apply it to problems?... does it really matter if I think the ball falls back to earth because mass attracts mass or because mass bends spacetime? I think you answer this question in your own post. The answer depends entirely on your goals. "All models are wrong, but some are useful". If all we ever wanted to do was shoot cannon balls over/into walls accurately, then we probably would've never bothered inventing modern physics. But we did want to do other things, so we built better models. It's worth noting that some of those things we wanted to do were more philosophical than others, e.g., engineering ("put satellites into orbit"), explaining empirical observations that seem important ("explain how electricity really works") and philosophical ("understand the nature of reality") are all answers to the question "what are you goals?" that have inspired progress toward better models in physics. > What does applying a similar mind set in something dealing with vastly more complicated systems, such as in medicine, looks like? It takes a certain amount of philosophical sophistication to realize that even the most perfect model is still a model and to then reason through what that entails -- epistemologically -- for less perfect models and for the entire scientific enterprise.

PurpleBoxDragon 8y ago
>what are you goals? I'm personally a fan of the 'make a better model to have a better model and let someone else figure out the application' model, but that one is harder to get funding for.

skybrian 8y ago

It matters for decision-making. If A causes B then forcing A to happen artificially (without changing anything else) will make B happen too. If it's not a cause then it won't. The correlation might always happen in the original environment, but it doesn't survive intervention. This does matter in physics too.