Hanti Lin (University of California, Davis)
The problem of theory choice and model selection is particularly hard when the standard of statistical consistency is too high to be achievable, that is, when no inference procedure is guaranteed in a statistical sense to eventually identify the true theory given what is known or assumed. This paper studies a crucial instance: the problem of inferring causal structures from non-experimental data without assuming the so-called causal Faithfulness condition or the like. A new account of epistemic evaluation is developed to solve that problem and to justify proceeding as if the Faithfulness condition or the like were accepted as true.
