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How exactly does one “control for other variables”?

JackOfAll · 2011
Stack Exchange · Datasets · License: CC BY-SA · 2011
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causalityconfoundingregression
Here is the article that motivated this question: Does impatience make us fat? I liked this article, and it nicely demonstrates the concept of “controlling for other variables” (IQ, career, income, age, etc) in order to best isolate the true relationship between just the 2 variables in question. Can you explain to me how you actually control for variables on a typical data set? E.g., if you have 2 people with the same impatience level and BMI, but different incomes, how do you treat these data? Do you categorize them into different subgroups that do have similar income, patience, and BMI? But, eventually there are dozens of variables to control for (IQ, career, income, age, etc) How do you then aggregate these (potentially) 100’s of subgroups? In fact, I have a feeling this approach is barking up the wrong tree, now that I’ve verbalized it. Thanks for shedding any light on something I've meant to get to the bottom of for a few years now...!
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