Simpson’s paradox must have an unbounded longevity, partly because traditional statisticians, so it seems, are still refusing to accept the fact that the paradox is causal, not statistical (link to R-414).
This was demonstrated recently in an April discussion on Gelman’s blog where the paradox was portrayed again as one of those typical cases where conditional associations are different from marginal associations. Strangely, only one or two discussants dared call: “Wait a minute! This is not what the paradox is about!” — to little avail.
To watch the discussion more closely, click http://andrewgelman.com/2014/04/08/understanding-simpsons-paradox-using-graph/ .
A recent discussion that might be of interest to readers took place on SEMNET, a Structural Equation Modeling Discussion Group, which appeals primarily to traditional SEM researchers who, generally speaking, are somewhat bewildered by the recent fuss about modern causal analysis. This particular discussion focused on “causal mediation”.
An SEMNET user, Emil Coman, asked (my paraphrasing):
“Who needs causal mediation (CM)?”
All it gives us is: (a) the ability to cope with confounders of the M—>Y relation and (b) the ability to handle interactions. Both (a) and (b) are SEM-fixable; (a) by adjusting for those confounders and (b) by using Bengt Muthen’s software (Mplus), whenever we suspect interactions.
To continue, click here.
A lively discussion flared up early this month on Andrew Gelman’s blog (garnering 114 comments!) which should be of some interest to readers of this blog.
The discussion started by a quote from George Box (1979) on the advantages of model-based approaches, and drifted into related topics such as
(1) What is a model-based approach,
(2) Whether mainstream statistics encourages this approach,
(3) Whether statistics textbooks and education have given face to reality,
(4) Whether a practicing statistician should invest time learning causal modeling,
or wait till it “proves itself” in the real messy world?
I share highlights of this discussion here, because I believe many readers have faced similar disputations and misunderstandings in conversations with pre-causal statisticians.
To read more, click here.