[nvy]

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.

[silent_cal]

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.

[nvy]

You are factually incorrect.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081045/

>In clinical medical research, causality is demonstrated by randomized controlled trials (RCTs).

https://www.sagepub.com/sites/default/files/upm-binaries/236...

https://www.statisticssolutions.com/dissertation-resources/r...

https://www.abs.gov.au/statistics/understanding-statistics/s...

https://www2.stat.duke.edu/~jerry/Papers/causal.pdf

If you argue this further I'm just going to conclude you're trolling and won't respond again.

You are wrong. Face it.

[silent_cal]

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.

[nvy]

>If you argue this further I'm just going to conclude you're trolling and won't respond again.

>You are wrong. Face it.

[silent_cal]

Thank you for demonstrating your willingness to engage with opposing viewpoints even when supporting evidence is presented (;