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A Modeling Approach for Mechanisms Featuring Causal Cycles

Published online by Cambridge University Press:  01 January 2022

Alexander Gebharter  and
Gerhard Schurz
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Abstract

Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal representation of mechanisms. Gebharter’s “A Formal Framework for Representing Mechanisms?” suggests to represent mechanisms by means of arrows in an acyclic causal net. In this article we show how this approach can be extended in such a way that it can also be fruitfully applied to mechanisms featuring causal feedback.

Type
Adequacy of Causal Graphs and Bayes Networks
Information
Philosophy of Science , Volume 83 , Issue 5 , December 2016 , pp. 934 - 945
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

This work was supported by Deutsche Forschungsgemeinschaft (DFG), research unit Causation|Laws|Dispositions|Explanation (FOR 1063). We thank Lorenzo Casini, David Danks, Christian J. Feldbacher, Clark Glymour, Marie I. Kaiser, Daniel Koch, Marcel Weber, and Naftali Weinberger for helpful remarks and discussions.

References

Bechtel, W. 2007. “Reducing Psychology While Maintaining Its Autonomy via Mechanistic Explanation.” In The Matter of the Mind: Philosophical Essays on Psychology, Neuroscience, and Reduction, ed. Schouton, M. and de Jong, H. L., 172–98. Oxford: Blackwell.Google Scholar
Bechtel, W., and Abrahamsen, A.. 2005. “Explanation: A Mechanist Alternative.” Studies in History and Philosophy of Biological and Biomedical Sciences 36:421–41.CrossRefGoogle ScholarPubMed
Casini, L. 2016. “How to Model Mechanistic Hierarchies.” Philosophy of Science, in this issue.CrossRefGoogle Scholar
Casini, L., lllari, P. M., Russo, F., and Williamson, J.. 2011. “Models for Prediction, Explanation and Control: Recursive Bayesian Networks.” Theoria 26 (70): 5–33.Google Scholar
Clarke, B., Leuridan, B., and Williamson, J.. 2014. “Modelling Mechanisms with Causal Cycles.” Synthese 191 (8): 1651–81.CrossRefGoogle Scholar
Craver, C. 2007. Explaining the Brain. Oxford: Clarendon.CrossRefGoogle Scholar
Danks, D., and Plis, S.. 2014. “Learning Causal Structure from Undersampled Time Series.” Paper presented at the NIPS 2013 Workshop on Causality.Google Scholar
Gebharter, A. 2014. “A Formal Framework for Representing Mechanisms?” Philosophy of Science 81 (1): 138–53.CrossRefGoogle Scholar
Gebharter, A. 2016. “Another Problem with RBN Models of Mechanisms.” Theoria 31 (2): 177–88.Google Scholar
Glennan, S. 1996. “Mechanisms and the Nature of Causation.” Erkenntnis 44 (1): 49–71.CrossRefGoogle Scholar
Kaiser, M. I. 2016. “On the Limits of Causal Modeling: Spatially-Structurally Complex Biological Phenomena.” Philosophy of Science, in this issue.CrossRefGoogle Scholar
Lauritzen, S. L., Dawid, A. P., Larsen, B. N., and Leimer, H. G.. 1990. “Independence Properties of Directed Markov Fields.” Networks 20 (5): 491–505.CrossRefGoogle Scholar
Machamer, P., Darden, L., and Craver, C.. 2000. “Thinking about Mechanisms.” Philosophy of Science 67 (1): 1–25.CrossRefGoogle Scholar
Murphy, K. P. 2002. Dynamic Bayesian Networks. Berkeley: University of California Press.Google Scholar
Murray-Watters, A., and Glymour, C.. 2015. “What’s Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models.” Philosophy of Science 82 (4): 556–86.CrossRefGoogle Scholar
Pearl, J. 2000. Causality. 1st ed. Cambridge: Cambridge University Press.Google Scholar
Pearl, J., and Dechter, R.. 1996. “Identifying Independencies in Causal Graphs with Feedback.” In Proceedings of the 12th International Conference on Uncertainty in Artificial Intelligence, 420–26. San Francisco: Kaufmann.Google Scholar
Richardson, T. 2009. “A Factorization Criterion for Acyclic Directed Mixed Graphs.” In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, ed. Bilmes, J. and Ng, A., 462–70. Arlington, VA: Association for Uncertainty in Artificial Intelligence.Google Scholar
Richardson, T., and Spirtes, P.. 2002. “Ancestral Graph Markov Models.” Annals of Statistics 30 (4): 962–1030.CrossRefGoogle Scholar
Schurz, G., and Gebharter, A.. 2016. “Causality as a Theoretical Concept: Explanatory Warrant and Empirical Content of the Theory of Causal Nets.” Synthese 193 (4): 1073–1103.CrossRefGoogle Scholar
Spirtes, P. 1995. “Directed Cyclic Graphical Representations of Feedback Models.” In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 491–98. San Francisco: Kaufmann.Google Scholar
Spirtes, P., Glymour, C., and Scheines, R.. 1993. Causation, Prediction, and Search. 1st ed. Dordrecht: Springer.CrossRefGoogle Scholar
Steel, D. 2005. “Indeterminism and the Causal Markov Condition.” British Journal for the Philosophy of Science 56 (1): 3–26.CrossRefGoogle Scholar
Weber, M. 2016. “On the Incompatibility of Dynamical Biological Mechanisms and Causal Graphs.” Philosophy of Science, in this issue.CrossRefGoogle Scholar
Zhang, J. 2008. “Causal Reasoning with Ancestral Graphs.” Journal of Machine Learning Research 9:1437–74.Google Scholar