Take this passage from the vaccination page for example:
> Some studies have claimed to show that current vaccine schedules increase infant mortality and hospitalization rates;[103][104] those studies, however, are correlational in nature and therefore cannot demonstrate causal effects, and the studies have also been criticized for cherry picking the comparisons they report, for ignoring historical trends that support an opposing conclusion, and for counting vaccines in a manner that is "completely arbitrary and riddled with mistakes".[105][106]
If you go to the vaccination page and look at the cited studies, you'll see they are peer-reviewed studies in real scientific journals. But a Wikipedia editor went in and modified the language to cast doubt on it (see here: https://en.wikipedia.org/w/index.php?title=Vaccination&diff=...).
All statistical analyses are "correlational in nature", so there's no reason why that should weaken the findings of this study, and the source used to accuse the study of "cherry-picking" is a blog post from 2011. In my estimation this kind of spin happens all the time on Wikipedia.
Goodness, are you not familiar with the phrase "correlation does not imply causation"?
There are ways to establish causal relationships, the gold standard of which is to conduct a double-blind controlled trial.
To claim as you have that a correlational study establishes a causal relationship is deeply misguided, irrespective of whether or not it is peer-reviewed.
Yes I am - you don't seem to know that all a double-blind controlled trial produces is (at best) a very strong correlation. I'm not claiming that a correlational study implies causation. I'm claiming that ALL statistical analyses can only prove correlation, and never causation. You're wrong about double-blind controlled trials proving causation.
You're confusing strong evidence for causality with establishment and absolute proof of causality. This is even discussed in the sources you've cited (second link). For example here is a quote from one of your sources about the necessity understanding the underlying mechanism before causality can be established:
> A causal mechanism is the process that creates the connection between the variation in an independent variable and the variation in the dependent variable that it is hypothesized to cause (Cook & Campbell, 1979:35; Marini & Singer, 1988). Many social scientists (and scientists in other fields) argue that no causal explanation is adequate until a mechanism is identified.