The Hard Problem of Theory Choice: A Case Study on Causal Inference and Its Faithfulness Assumption.
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 ins...
| Published in: | Philosophy of Science Vol. 86; no. 5; pp. 967 - 981 |
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| Format: | Article |
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Cambridge University Press
Dec2019
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| Online Access: | View this record in EBSCOhost |
| Summary: | 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. |
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