Woodward, James (2022) Flagpoles anyone? Causal and explanatory asymmetries. THEORIA. An International Journal for Theory, History and Foundations of Science, 37 (1). pp. 7-52. ISSN 2171-679X
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Official URL: https://ojs.ehu.eus/index.php/THEORIA/article/view...
Abstract
This paper discusses some procedures developed in recent work in machine learning for inferring causal direction from observational data. The role of independence and invariance assumptions is emphasized. Several familiar examples including Hempel’s flagpole problem are explored in the light of these ideas. The framework is then applied to problems having to do with explanatory direction in non-causal explanation.
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| Item Type: | Published Article or Volume | ||||||
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| Additional Information: | ISSN: 0495-4548 (print) | ||||||
| Keywords: | causal asymmetry, direction of causation, direction of explanation, invariance, independence, machine learning | ||||||
| Subjects: | General Issues > Causation General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Depositing User: | Unnamed user with email theoria@ehu.es | ||||||
| Date Deposited: | 15 May 2022 03:52 | ||||||
| Last Modified: | 15 May 2022 03:52 | ||||||
| Item ID: | 20605 | ||||||
| Journal or Publication Title: | THEORIA. An International Journal for Theory, History and Foundations of Science | ||||||
| Publisher: | Euskal Herriko Unibertsitatea / Universidad del País Vasco | ||||||
| Official URL: | https://ojs.ehu.eus/index.php/THEORIA/article/view... | ||||||
| DOI or Unique Handle: | 10.1387/theoria.21921 | ||||||
| Subjects: | General Issues > Causation General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Date: | 2022 | ||||||
| Page Range: | pp. 7-52 | ||||||
| Volume: | 37 | ||||||
| Number: | 1 | ||||||
| ISSN: | 2171-679X | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/20605 |
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