Under the subject line “Legit?”, Kevin Lewis pointed me to this press release, “New statistical approach will help researchers better determine cause-effect.” I responded, “No link to any of the research papers, so cannot evaluate.”
In writing this post I thought I’d go further. The press release mentions 6 published articles so I googled the first one, from the British Journal of Mathematical and Statistical Psychology (hey, I’ve published there!) and found this paper, “Significance tests to determine the direction of effects in linear regression models.”
Uh oh, significance tests. It’s almost like they’re trying to piss me off!
I’m traveling so I can’t get access to the full article. From the abstract:
Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. . . .
The third moment of regression residuals??? This is nuts!
OK, I can see the basic idea. You have a model in which x causes y; the model looks like y = x + error. The central limit theorem tells you, roughly, that y should be more normal-looking than x, hence all those statistical tests.
Really, though, this is going to depend so much on how things are measured. I can’t imagine it will be much help in understanding causation. Actually, I think it will hurt in that if anyone takes it seriously, they’ll just muddy the waters with various poorly-supported claims. Nothing wrong with doing some research in this area, but all that hype . . . jeez!