3 ms·

Unfortunately nowadays people seem to have the same understanding of the terms "statistically significant" and "proven causality". There's no way a single study

Unfortunately nowadays people seem to have the same understanding of the terms "statistically significant" and "proven causality". There's no way a single study can prove causality and the closest we can get to reaching for the truth is meta-analysis. Not saying studies results are invaluable. They can certainly point to the right direction if done right. But just a single RCT won't be enough to prove causality.

svara 3y ago
> There's no way a single study can prove causality Why? If you're talking about epidemiological studies looking for associations between variables in big datasets, okay. But that's just one type of study, and it doesn't get better by throwing many of them together in a meta study. You absolutely can show causality in a single study by doing experiments. I'm feeling rather the opposite really, "correlation is not causation" is a beaten horse and will come up in every discussion where it may or may not fit on this website. In many cases, correlation is a hint at causality, and if there's a strong model explaining it, should be considered evidence for causality.
- bsder 3y ago
  > if there's a strong model explaining it This is the key that everybody ignores. If you simply throw variables together, you wind up with too many confounders. You can also get the direction wrong. "Growing tall causes you to be male" is my go to example. The correlation is super powerful--because it's completely backwards. It is also competely valid unless you have a model to differentiate--at which point it becomes obviously non-sensical.
  - mquander 3y ago
    If you ran an RCT where you could directly make people to be male and then see what happened to them, you wouldn't need any further modelling to distinguish between which is causing which. The reason you need the model is if you can't randomize.

svara 3y ago

> There's no way a single study can prove causality Why? If you're talking about epidemiological studies looking for associations between variables in big datasets, okay. But that's just one type of study, and it doesn't get better by throwing many of them together in a meta study. You absolutely can show causality in a single study by doing experiments. I'm feeling rather the opposite really, "correlation is not causation" is a beaten horse and will come up in every discussion where it may or may not fit on this website. In many cases, correlation is a hint at causality, and if there's a strong model explaining it, should be considered evidence for causality.

bsder 3y ago
> if there's a strong model explaining it This is the key that everybody ignores. If you simply throw variables together, you wind up with too many confounders. You can also get the direction wrong. "Growing tall causes you to be male" is my go to example. The correlation is super powerful--because it's completely backwards. It is also competely valid unless you have a model to differentiate--at which point it becomes obviously non-sensical.
- mquander 3y ago
  If you ran an RCT where you could directly make people to be male and then see what happened to them, you wouldn't need any further modelling to distinguish between which is causing which. The reason you need the model is if you can't randomize.