You only have to hang around the world of social science research- or policy-related blogging for a few hours before you come across someone willing to snottily inform you, or some other luckless interlocutor, that although the finding of this or that paper may appeal to you, nevertheless don’t you know that Correlation Is Not Causation. Often this seems to be the only thing they know about statistics.

I grudgingly admit that it’s a plausible-sounding rule, and in the textbooks and stuff. But, to be honest, I read it too many times in various posts and comments threads the other day, and in my raging pique I found myself thinking that the next time it happened I would say, “That’s completely backwards: in fact, causation is just correlation” and fling a copy of Hume’s first Enquiry at their head. Or at the screen, I suppose, but that image is less satisfying, because now who’s the crank on the internet, etc.

OK, he’s kidding. Sort of. But not really. Bloggers are fond of yelling “correlation is not causation” at any piece of research that comes to a conclusion they find distasteful, but what they almost never do is actually read the paper in question, which invariably addresses most of their concerns: research methodology; alternate explanations; potential intervening variables; results of similar studies in the past; shortcomings in the data set; etc. That’s not to say that researchers always take every possible problem seriously enough, or that social science papers don’t deserve heightened scrutiny. But it is to say that if, in 30 seconds, some possible problem with the research program occurs to you, it’s almost a dead certainty that the person with a PhD who performed the study also thought of the same thing. And discusses it in the paper.

At least, that’s what’s I’ve found on virtually every occasion when I’ve cracked open one of these things. The discussion isn’t always great, and sometimes it leaves a variety of questions hanging, but it’s almost always there. It’s true that correlations don’t always imply causation, especially if the research is poorly done or the statistical analysis is mangled, but it also turns out, surprisingly enough, that people with doctorates mostly understand this stuff almost as well as bloggers who read the New York Times.