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The Hard Problem of Theory Choice: A Case Study on Causal Inference and Its Faithfulness Assumption

Published online by Cambridge University Press:  01 January 2022

Hanti Lin
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Abstract

The problem of theory choice and model selection is hard but still important when useful truths are underdetermined, perhaps not by all kinds of data but by the kinds of data we can have access to ethically or practicably—even if we have an infinity of such data. This article addresses a crucial instance of that problem: the problem of inferring causal structures from nonexperimental, nontemporal 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 justify a standard practice of causal inference in data science.

Type
Logic, Formal Epistemology, and Decision Theory
Information
Philosophy of Science , Volume 86 , Issue 5 , December 2019 , pp. 967 - 980
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

I am indebted to Kevin Kelly for the 10 years of discussions with him, without which this article would have been impossible. I am indebted to Jiji Zhang for the many iscussions with him, which helped me see the generality of the approach developed in this article. I am also indebted to Reuben Stern for his very detailed, helpful comments on an earlier draft of this article.

References

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