In both cases the goal is to measure the effect of one choice, including effects through intermediate causes, without including in that measurement any other factors.
Assuming a complex system and a fairly large sample, you can correct by gathering lots of data about as many things which might be factors as possible: If, however, there is an unmeasured trait "Does not enjoy mind-clouding events" which correlates both to increase in IQ and not smoking dope, it cannot be discovered by correction.
To control, you take your population and divide it into groups as evenly as possible along every axis that you can measure except the independent variable, and then force the independent variable of each group to be the same.
Maybe the definitions I'm using are different from the jargon, and if so I am 'wrong' in a real sense; what is the jargon for distinguishing between those two types of differentiation?
Ok, when you say "correct" you mean you try to discover as many hidden variables in your DAG as possible and try to collect data on them such that they become observed. When you say "control" you mean a particular implementation of the adjustment formula: p(y | do(a)) = sum{c} p(y | a, c) p(c), where a is the treatment, y is the outcome, and c is measured covariates. (Note: using "independent/dependent" variable is not correct because those variables are not guaranteed to have a causal relationship you want -- an effect can ...
A week ago the meetup group in Berkeley discussed a new article in PNAS titled "Persistent cannabis users show neuropsychological decline from childhood to midlife". Several people who didn't attend said they were interested in how that conversation went.
Before discussing the specifics of the article, we went around the room and stated how many IQ points we'd be willing to spend for some level of cannabis use. The median answer given was 4 points for moderate usage. Then someone pointed out that since we responded out loud, there may have been an anchoring effect here.
My understanding of the group's understanding of the scientific result is that smoking so much marijuana that you're diagnosable as cannabis-dependent (whatever that means) before the age of 18 will give you an IQ hit of 9-11 points, maybe more, over 20 years, compared to nonusers.
People who were diagnosable as dependent on cannabis but not before 18 got an IQ hit of 4 or 5 points, on average. We don't know if this is because cannabis is bad for adults, or if it's bad for people just over 18.
People who have used cannabis but were not diagnosed as dependent got an IQ hit of 1 or 2 points on average. The article gives us little information on what the risks of various moderate levels of cannabis use are.
We didn't discuss any methodological errors in the study, but the general attitude of the group was that the scientific result is worth taking seriously.
After the discussion, people who use cannabis opportunistically or not at all — especially the younger attendees — said that after learning about the study they now have another reason not to use cannabis. One person who uses cannabis less than once per week said they wouldn't change their usage habits.