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  1. Curie’s principle and causal graphs.David Kinney - 2021 - Studies in History and Philosophy of Science Part A 87 (C):22-27.
    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 (...)
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  • Bayesian Networks and Causal Ecumenism.David Kinney - 2020 - Erkenntnis 88 (1):147-172.
    Proponents of various causal exclusion arguments claim that for any given event, there is often a unique level of granularity at which that event is caused. Against these causal exclusion arguments, causal ecumenists argue that the same event or phenomenon can be caused at multiple levels of granularity. This paper argues that the Bayesian network approach to representing the causal structure of target systems is consistent with causal ecumenism. Given the ubiquity of Bayesian networks as a tool for representing causal (...)
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  • Clark Glymour’s responses to the contributions to the Synthese special issue “Causation, probability, and truth: the philosophy of Clark Glymour”.Clark Glymour - 2016 - Synthese 193 (4):1251-1285.
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  • Introduction to the special issue “Causation, probability, and truth—the philosophy of Clark Glymour”.Alexander Gebharter & Gerhard Schurz - 2016 - Synthese 193 (4):1007-1010.
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  • Variable Definition and Independent Components.Lorenzo Casini, Alessio Moneta & Marco Capasso - 2021 - Philosophy of Science 88 (5):784-795.
    In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.
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