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Flagpoles anyone? Causal and explanatory asymmetries

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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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
Creators:
CreatorsEmailORCID
Woodward, Jamesjfw@pitt.edu
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
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
Date: 2022
Page Range: pp. 7-52
Volume: 37
Number: 1
ISSN: 2171-679X
URI: http://philsci-archive.pitt.edu/id/eprint/20605

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