Curie's principle and causal graphs. (June 2021) - Kinney, David
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.