Seth Green writes:
I thought you might enjoy this update from the STATA team:
. . . suppose we wish to know the effect on employment status of a job training program. Further suppose that motivation affects employment status and motivation affects participation. We do not observe motivation. We have an endogeneity problem.
Stata 14’s new eteffects eliminates the confounding effects of unobserved variables and allows us to test for endogeneity. In return, you must model both the treatment and the outcome.
Well ok then! Glad we can all retire!
I was shocked. I already emailed the support staff with a quote from Judea Pearl about how the correctness of the model is, even in principle, unverifiable. Whom do you think they hire to write these updates?
To be fair, if you have 2 natural experiments you should be able to estimate 2 separate causal effects and then get what you want. The trouble is with any implication that this can be automatically done from observational data. “You must model,” sure, but a statistical model without some real-world identification won’t get you far!
To which Green responded:
I wish that that was what they were claiming. In the example on the page, however, “eteffects” models “wages as being determined by job tenure and age” and “college attainment by age and the number of parents who attended college.” So the actual implementation is “independent conditional on observables.” The post then gives a test of “the correlation between the unobservables that affect treatment and outcome. If these correlations are zero, we have no endogeneity.” The test detects endogeneity, the model was correct because it was simulated data, and therefore endogeneity has been addressed (!).
The deeper I peer in the less meaning there is.
All I can say is, what an amazing accomplishment. Whoever came up with it is the most extraordinary collection of talent, of human knowledge, that has ever been gathered in the field of statistics, with the possible exception of when Stephen Wolfram dined alone.