Curie's principle and causal graphs. (June 2021)

• Record Type: Journal Article
• Authors: Kinney, David
• Is Part Of: Studies in history and philosophy of science. Volume 87(2021)
• Page Start: 22
• Page End: 27
• Publication Date: 2021-06
• Subjects: Markov condition -- Causal graphs -- Curie's Principle -- Symmetry -- Causation; Science -- History -- Periodicals; Science -- Philosophy -- Periodicals; 509
• DOI: 10.1016/j.shpsa.2021.02.007
• Languages: English
• ISSNs: 0039-3681

Abstract

Abstract: Curie's Principle says that any symmetry property of a cause must be found in its effect. In this article, I consider Curie's Principle from the point of view of graphical causal models, and demonstrate that, under one definition of a symmetry transformation, the causal modeling framework does not require anything like Curie's Principle to be true. On another definition of a symmetry transformation, the graphical causal modeling formalism does imply a version of Curie's Principle. These results yield a better understanding of the logical landscape with respect to the relationship between Curie's Principle and graphical causal modeling. Highlights: Gives a brief historical overview of Pierre Curie's conjecture that causation is symmetry-preserving. Considers whether Curie's Principle is valid within the framework of graphical causal modeling. Shows that the validity of Curie's Principle depends on how one defines a symmetry transformation of a random variable.