Abstract
Statistical inference turns on trade-offs among conflicting assumptions that provide stronger or weaker guarantees of convergence to the truth, and stronger or weaker measures of uncertainty of inference. In applied statistics—social statistics, epidemiology, economics—these assumptions are often hidden within computerized data analysis procedures, although they reflect broader epistemological issues. I claim that the content of the trade-offs, and of the epistemological issues they manifest, is clarified by placing them within the framework of formal learning theory.
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References
Spirtes, P., Glymour, C. and Scheines, R. (2000). Causation, Prediction and Search, 2nd ed., Cambridge (Mass.): MIT Press.
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Glymour, C. (2007). Trade-Offs. In: Friend, M., Goethe, N.B., Harizanov, V.S. (eds) Induction, Algorithmic Learning Theory, and Philosophy. Logic, Epistemology, and the Unity of Science, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6127-1_9
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DOI: https://doi.org/10.1007/978-1-4020-6127-1_9
Publisher Name: Springer, Dordrecht
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