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On the Limits of Causal Modeling: Spatially-Structurally Complex Biological Phenomena

Published online by Cambridge University Press:  01 January 2022

Marie I. Kaiser
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Abstract

This article examines the adequacy of causal graph theory as a tool for modeling biological phenomena. I argue that the causal graph approach reaches its limits when it comes to modeling biological phenomena that involve complex spatial and chemical-structural relations. Using a case study from molecular biology, I show why causal graph models fail to adequately represent and explain biological phenomena of this kind. The inadequacy of these models is due to their failure to include relevant spatial-structural information in a way that does not render the models nonexplanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory.

Information

Type
Adequacy of Causal Graphs and Bayes Networks
Information
Philosophy of Science , Volume 83 , Issue 5 , December 2016 , pp. 921 - 933
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

This paper arose from a collaboration with Marcel Weber during my time at the University of Geneva, and it profited much from our stimulating discussions. I also thank Benjamin Jantzen, Christopher Hitchcock, Lorenzo Casini, Alexander Gebharter, Maximilian Huber, and the members of my DFG Research Group “Causation and Explanation” for their helpful comments on earlier versions of the paper. This material is based on work supported by the German Research Foundation (DFG; FOR 1063).

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