I recently read an article about how you can increase longevity by sleeping less. This article, like many others I've read, references a statistical study and implies that causation was found between two events(sleeping less and living longer). Well I'm not a statistician, but I'm aware of the common fallacies about causation and I find it very hard to accept the information given in this article and other similar ones. Even further, I can almost never know from an article if the original study found causation or not, without taking the time and digging into it.
Although I'd really like to, I'm not going to go into any depth bringing examples of how the causation might be wrong in the given article, because I would most likely be preaching to the choir. Luckily though, to my relief, the original article in NY Times only talks about correlation and even has a disclaimer in the end that basically says that the study doesn't imply causation.
Also, I now read that, in accordance with my intuition, it is actually really hard to find and/or test for causation. Reading through the wiki page on the subject I saw two models which seemed to be the most common ones used to find actual links in causation: Granger causality test and convergent cross mapping. But I see, that both of these have some gaps in them.
Now, I have a few questions regarding all this.
Q1: Are there any models that can, with high accuracy, find actual causation in such widespread studies? If so what are the most common ones?
Q2: How often do these widespread studies actually test for causation? E.g. if I read somewhere that a very broad study implies that A causes B, do I have any reason whatsoever to believe that there's something more than correlation behind those claims.
Q3: Is there a good technique that can be used by a non-statistician to filter out good statistics from the bad ones in everyday life.