21 February 2007
As some folks know, I'm on the legal academic job market this year. My job talk paper is on the application of the potential outcomes framework for causation to legal matters, particularly anti-discrimination issues that arise in litigation. As I've presented the framework, I've highlighted one of its advantages as being the fact that much of the hard work of separating covariates from intermediate outcomes and balancing covariates can (and should) be done without access to the outcome variable. The idea is that without access to the outcome variable, it is harder for a researcher (or, God forbid, an expert witness) to model-snoop, i.e., to fit model after model until finding one that "proves" a pet theory.
In a few schools, reaction to the claim of increased objectivity has been chilly. Skeptics have said, in essense, "I don't know enough about statistics to argue with you, but I'm REALLY SURE that your method is just as manipulable as, say, regression, even if you don't have access to the outcome variable when you do the hard work." The skeptics have then asked me to tell them how to manipulate the potential outcomes framework (i.e., to tell them why they are right and I am wrong), assuming no access to the outcome variable.
Any ideas on this? I'm able to think of one way it can be done (although the results of "my" way would not be nearly as bad as those from model-snooping), but I'd prefer not to stifle any comments folks might have by putting forth my own thoughts.