Advertisement

Erkenntnis

pp 1–16 | Cite as

The Compatibility of Differential Equations and Causal Models Reconsidered

  • Wes Anderson
Article
  • 33 Downloads

Abstract

Weber argues that causal modelers face a dilemma when they attempt to model systems in which the underlying mechanism operates according to some set of differential equations. The first horn is that causal models of these systems leave out certain causal effects. The second horn is that causal models of these systems leave out time-dependent derivatives, and doing so distorts reality. Either way causal models of these systems leave something important out. I argue that Weber’s reasons for thinking causal modeling is limited in this domain are lacking.

Notes

Acknowledgements

I wish to thank James DiFrisco, Bruce Glymour, Valerie Racine, Marcel Weber, and two anonymous referees for various helpful suggestions on previous drafts of this paper.

References

  1. Aalen, O., Røysland, K., Gran, J., & Ledergerber, B. (2012). Causality, mediation, and time: A dynamic viewpoint. Journal of the Royal Statistical Society: Series A (Statistics in Society), 175, 831–861.CrossRefGoogle Scholar
  2. Dash, D. (2003). Caveats for causal reasoning with equilibrium models. University of Pittsburgh Ph.D. Dissertation.Google Scholar
  3. Glymour, B. (2011). Modeling environments: Interactive causation and adaptations to environmental conditions. Philosophy of Science, 78, 448–471.CrossRefGoogle Scholar
  4. Glymour, C. (2008). When is a brain like the planet? Philosophy of Science, 74, 330–347.CrossRefGoogle Scholar
  5. Goldbeter, A. (1995). A model for circadian oscillations in the Drosophila period protein (PER). In Proceedings of the Royal Society of London. Part B: Biological Sciences (Vol. 261, pp. 319–324).Google Scholar
  6. Hyttinen, A., Plis, S., Järvisalo, M., Eberhardt, F., & Danks, D. (2016). Causal discovery from subsampled time series data by constraint optimization. Journal of Machine Learning Research Workshop and Conference Proceedings, 52, 216–227.Google Scholar
  7. Iwasaki, Y., & Simon, H. A. (1994). Causality and model abstraction. Artificial Intelligence, 67, 143–194.CrossRefGoogle Scholar
  8. Jantzen, B. (2015). Projection, symmetry, and natural kinds. Synthese, 192, 3617–3646.CrossRefGoogle Scholar
  9. Kuorikoski, J. (2012). Mechanisms, modularity, and constitutive explanation. Erkenntnis, 78, 1–20.Google Scholar
  10. Maier, M. (2014). Causal discovery for relational domains: Representation, reasoning, and learning. Ph.D. Dissertation. University of Massachusetts Amherst.Google Scholar
  11. Mooij, J. M., Janzing, D., & Schölkopf, B. (2013). From ordinary differential equations to structural causal models: The deterministic case. In Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (pp. 440–448).Google Scholar
  12. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.Google Scholar
  13. Plis, S., Danks, D., & Yang, J. (2015). Mesochronal structure learning. In Uncertainty in artificial intelligence (Vol. 31, pp. 702–711).Google Scholar
  14. Sokol, A. (2013). On martingales, causality, identifiability, and model selection. University of Copenhagen Ph.D. Dissertation.Google Scholar
  15. Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. Cambridge, MA: MIT Press.Google Scholar
  16. Voortman, M., Dash, D., & Druzdzel, M. J. (2010). Learning why things change: The difference-based causality learner. In Proceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (pp. 641–650).Google Scholar
  17. Weber, M. (2016). On the incompatibility of dynamical biological mechanisms and causal graphs. Philosophy of Science, 83, 959–971.CrossRefGoogle Scholar
  18. Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Western New England UniversitySpringfieldUSA

Personalised recommendations