Intervention, underdetermination, and theory generation
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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We consider the use of intervention data for eliminating the underdetermination in statistical modelling, and for guiding extensions of the statistical models. The leading example is factor analysis, a major statistical tool in the social sciences. We first relate indeterminacy in factor analysis to the problem of underdetermination. Then we draw a parallel between factor analysis models and Bayesian networks with hidden nodes, which allows us to clarify the use of intervention data for dealing with indeterminacy. It will be shown that in some cases, the indeterminacy can be resolved by an intervention. In the other cases, the intervention data suggest specific extensions of the model. The upshot is that intervention data can replace theoretical criteria that are typically employed in resolving underdetermination and theory change.
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