Hostname: page-component-6bf8c574d5-w79xw Total loading time: 0 Render date: 2025-02-27T15:28:06.355Z Has data issue: false hasContentIssue false
  • English
  • Français

Experimental Indistinguishability of Causal Structures

Published online by Cambridge University Press:  01 January 2022

Frederick Eberhardt
Get access Rights & Permissions [Opens in a new window]

Abstract

Using a variety of different results from the literature, I show how causal discovery with experiments is limited unless substantive assumptions about the underlying causal structure are made. These results undermine the view that experiments, such as randomized controlled trials, can independently provide a gold standard for causal discovery. Moreover, I present a concrete example in which causal underdetermination persists despite exhaustive experimentation and argue that such cases undermine the appeal of an interventionist account of causation as its dependence on other assumptions is not spelled out.

Type
General Philosophy of Science
Information
Philosophy of Science , Volume 80 , Issue 5 , December 2013 , pp. 684 - 696
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Eberhardt, Frederick. 2013. “Direct Causes.” Unpublished manuscript, PhilSci Archive. http://philsci-archive.pitt.edu/9502/.Google Scholar
Eberhardt, Frederick, Glymour, Clark, and Scheines, Richard. 2005. “On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations among n Variables.” In Proceedings of the 21st Conference on Uncertainty and Artificial Intelligence, ed. Bacchus, Fahiem and Jaakkola, Tommi, 178–84. Arlington, VA: AUAI.Google Scholar
Eberhardt, Frederick, Hoyer, Patrik O., and Scheines, Richard. 2010. “Combining Experiments to Discover Linear Cyclic Models with Latent Variables.” AISTATS 2010 conference proceedings. Journal of Machine Learning Research: Workshop and Conference Proceedings. Vol. 9. http://machinelearning.wustl.edu/mlpapers/papers/AISTATS2010_EberhardtHS10.Google Scholar
Fisher, Ronald A. 1935. The Design of Experiments. New York: Hafner.Google Scholar
Geiger, Dan, Verma, Thomas, and Pearl, Judea. 1990. “Identifying Independence in Bayesian Networks.” Networks 20:507–34.CrossRefGoogle Scholar
Hyttinen, Antti, Eberhardt, Frederick, and Hoyer, Patrik O.. 2011. “Noisy-or Models with Latent Confounding.” In Proceedings of the 27th Conference on Uncertainty and Artificial Intelligence, ed. Cozman, Fabio G. and Pfeffer, Avi. Corvallis, OR: AUAI.Google Scholar
Pearl, Judea. 2000. Causality. Oxford: Oxford University Press.Google Scholar
Shimizu, Shohei, Hoyer, Patrik O., Hyvarinen, Aapo, and Kerminen, Antti J.. 2006. “A Linear Non-Gaussian Acyclic Model for Causal Discovery.” Journal of Machine Learning Research 7:2003–30.Google Scholar
Spirtes, Peter, Glymour, Clark, and Scheines, Richard. 2000. Causation, Prediction and Search. Cambridge, MA: MIT Press.Google Scholar
Strevens, Michael. 2008. “Comments on Woodward, Making Things Happen.” Philosophy and Phenomenological Research 77:171–92.CrossRefGoogle Scholar
Woodward, James. 2003. Making Things Happen. Oxford: Oxford University Press.Google Scholar
Supplementary material: PDF

Eberhardt supplementary material

Eberhardt supplementary material

Download Eberhardt supplementary material(PDF)
PDF 434 KB
Supplementary material: PDF

Eberhardt supplementary material

Eberhardt supplementary material

Download Eberhardt supplementary material(PDF)
PDF 435.8 KB