This question is bound to be a can of worms. There has been a great deal written about the matter, particularly in regard to observational studies.
Drawing causal inference is overwhelmingly likely to be wrong when there is a good chance that unknown variables are influencing the correlates observed. In health-related sciences that more often than not is the case.
A few years ago there was a study correlating hours of TV watched and ADHD symptoms in children. The news media picked up on these "findings" and of course the causal influence of TV on ADHD was reported.
It was obvious that saying watching TV caused ADHD was absurd, that other variables weren't taken into account, e.g., some other characteristic of ADHD kids prompted watching TV more than other kids.
There was a great article published in PLOS several years ago (ATM I don't have the link) showing mathematically that the odds were about 1 in a million that an observational study like the above would turn out to be a "true" causal relationship, and the author concluded most published studies were junk.
In experimental studies, variables are limited and controlled as well as possible. With fewer and known variables, correlations would have a greater chance of revealing a reproducible causal relationship among events.
The discussion gets tripped up when it comes to defining "cause" or "causal relationship". The theory is controlled as an experiment may be, there's a possibility that unknown variables were present and affected the phenomena occurring in the experiment. Conclusions can't be absolute, but only true to some probability.
I think the history of science over the last 100 years or so has something to say about the nature of "causal relationships".
> There was a great article published in PLOS several years ago (ATM I don't have the link) showing mathematically that the odds were about 1 in a million that an observational study like the above would turn out to be a "true" causal relationship, and the author concluded most published studies were junk.
I very much doubt the second part of that, that most published studies are junk. The idea that not showing a causal link and only a correlational one makes a study junk is not held by anyone in the field who garners a whole lot of respect or notoriety. I'm not saying the quality is equal whatsoever, simply that correlation studies are not inherently junk - some are fantastic and some are quite the opposite.
There is no question that journals in general are pumping out a significant amount of junk (in studies of all types), but I speculate the root cause of that has more to do with the rise of "publish or perish" and significant increases in grad school enrollment. And even worse the notion that for a grad student, the number of publications they have their name attributed to is more important than the content they publish in terms of employment after they graduate. So there is a situation where people have more pressure to publish than ever before [0], there is more competition for scarce funding so studies are vastly underfunded and studies are rushed so another can begin to add to the resume.
That doesn't mean there is less good science being done either! There probably is more good science being published now than ever before, the problem is the signal:noise ratio has gone down making it harder for good studies to get media attention, and easier for the media to latch on to whatever story they think will get viewers.
[0]: Sidenote, this also makes it increasingly challenging not only to have high quality research, but research that only is making a correlation as opposed to going through and providing evidence to claim you might have a causal link.
It's quite a famous paper. Many rebuttals, comments, blog-posts etc, have been published, have a look at the comments on the PLOS site, I especially like this blog post summarizing more research:
>Under assumptions about the way people perform these tests and report them it is possible to construct a universe where most published findings are false positive results. Important drawback: The paper contains no real data, it is purely based on conjecture and simulation.
Drawing causal inference is overwhelmingly likely to be wrong when there is a good chance that unknown variables are influencing the correlates observed. In health-related sciences that more often than not is the case.
A few years ago there was a study correlating hours of TV watched and ADHD symptoms in children. The news media picked up on these "findings" and of course the causal influence of TV on ADHD was reported.
It was obvious that saying watching TV caused ADHD was absurd, that other variables weren't taken into account, e.g., some other characteristic of ADHD kids prompted watching TV more than other kids.
There was a great article published in PLOS several years ago (ATM I don't have the link) showing mathematically that the odds were about 1 in a million that an observational study like the above would turn out to be a "true" causal relationship, and the author concluded most published studies were junk.
In experimental studies, variables are limited and controlled as well as possible. With fewer and known variables, correlations would have a greater chance of revealing a reproducible causal relationship among events.
The discussion gets tripped up when it comes to defining "cause" or "causal relationship". The theory is controlled as an experiment may be, there's a possibility that unknown variables were present and affected the phenomena occurring in the experiment. Conclusions can't be absolute, but only true to some probability.
I think the history of science over the last 100 years or so has something to say about the nature of "causal relationships".