I don’t in any way intend this as a criticism, but Mark Twain’s complaint about bringing up the weather in conversation seems to apply here: everyone talks about it, but nobody ever does anything about it.
The problem with just saying “correlation is not causation” is that it doesn’t help you figure out what information you can get from an observational study. This is becoming an important issue in my work. We do advanced marketing research for (mostly) large companies. For many years the company’s emphasis was on choice-based conjoint studies, which give you experimental data. (You ask people to repeatedly choose among various sets of hypothetical products, and then analyze the results to figure out what they value.) Now we’re moving more into marketing mix models, which involve purely observational data. Knowing exactly what one can and cannot legitimately infer from observational data, under what assumptions, is a very important practical question for us and our clients.
That’s why I am currently spending a lot of my spare time studying Judea Pearl’s book Causality. I would highly recommend this book to anyone who, as Yvain suggests, is more interested in solving problems than looking smart.
I don’t in any way intend this as a criticism, but Mark Twain’s complaint about bringing up the weather in conversation seems to apply here: everyone talks about it, but nobody ever does anything about it.
The problem with just saying “correlation is not causation” is that it doesn’t help you figure out what information you can get from an observational study. This is becoming an important issue in my work. We do advanced marketing research for (mostly) large companies. For many years the company’s emphasis was on choice-based conjoint studies, which give you experimental data. (You ask people to repeatedly choose among various sets of hypothetical products, and then analyze the results to figure out what they value.) Now we’re moving more into marketing mix models, which involve purely observational data. Knowing exactly what one can and cannot legitimately infer from observational data, under what assumptions, is a very important practical question for us and our clients.
That’s why I am currently spending a lot of my spare time studying Judea Pearl’s book Causality. I would highly recommend this book to anyone who, as Yvain suggests, is more interested in solving problems than looking smart.