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Causality and Unification: How Causality Unifies Statistical Regularities

Schurz, Gerhard (2015) Causality and Unification: How Causality Unifies Statistical Regularities. THEORIA. An International Journal for Theory, History and Foundations of Science, 30 (1). pp. 73-95. ISSN 2171-679X

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Abstract

Two key ideas of scientific explanation - explanations as causal information and explanation as unification - have frequently been set into mutual opposition. This paper proposes a "dialectical solution" to this conflict, by arguing that causal explanations are preferable to non-causal explanations because they lead to a higher degree of unification at the level of the explanation of statistical regularities. The core axioms of the theory of causal nets (TC) are justified because they give the best if not the only unifying explanation of two statistical phenomena: screening off and linking up. Alternative explanation attempts are discussed and it is shown why they don't work. It is demonstrated that not the core of TC but extended versions of TC have empirical content, by means of which they can generate independently testable predictions.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Schurz, Gerhardschurz@phil.uni-duesseldorf.de
Additional Information: ISSN: 0495-4548 (print)
Keywords: unification; explanation; causality; theory of causal nets; screening off; linking up
Subjects: General Issues > Causation
Specific Sciences > Probability/Statistics
Depositing User: Unnamed user with email theoria@ehu.es
Date Deposited: 05 Jun 2016 01:57
Last Modified: 05 Jun 2016 01:57
Item ID: 12134
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: http://www.ehu.eus/ojs/index.php/THEORIA/article/v...
DOI or Unique Handle: 10.1387/theoria.11913
Subjects: General Issues > Causation
Specific Sciences > Probability/Statistics
Date: January 2015
Page Range: pp. 73-95
Volume: 30
Number: 1
ISSN: 2171-679X
URI: http://philsci-archive.pitt.edu/id/eprint/12134

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