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What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

Published online by Cambridge University Press:  01 January 2022

Alexander Murray-Watters  and
Clark Glymour
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Abstract

Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.

Type
Research Article
Information
Philosophy of Science , Volume 82 , Issue 4 , October 2015 , pp. 556 - 586
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

This research is undertaken under the auspices of the University of Pittsburgh Carnegie Mellon Center for Causal Discovery, supported by the National Institutes of Health under award U54HG008540. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additional support was received from the James S. McDonnell Foundation. We thank Gregory Cooper, Xinghua Lu, and Richard Scheines for their help.

References

Cancer Genome Atlas Research Network. 2011. “Integrated Genomic Analyses of Ovarian Carcinoma.” Nature 474 (7353): 609–15.Google Scholar
Gebharter, Alexander. 2014a. “Addendum to ‘A Formal Framework for Representing Mechanisms?’.” Unpublished manuscript, http://philpapers.org/archive/GEBATQ.pdf.CrossRefGoogle Scholar
Gebharter, Alexander 2014b. “A Formal Framework for Representing Mechanisms?” Philosophy of Science 81 (1): 138–53.CrossRefGoogle Scholar
Giles, David E. A. 1999. “Measuring the Hidden Economy: Implications for Econometric Modelling.” Economic Journal 109 (456): 370–80.CrossRefGoogle Scholar
Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, Hastie, T., Friedman, J., and Tibshirani, R.. 2009. The Elements of Statistical Learning. New York: Springer.CrossRefGoogle Scholar
Hempel, Carl. 1985. “Thoughts on the Limitations of Discovery by Computer.” In Logic of Discovery and Diagnosis in Medicine, ed. Schaffner, Kenneth F., 115–22. Berkeley: University of California Press.Google Scholar
Hughes, C., Ensor, R., Wilson, A., and Graham, A.. 2009. “Tracking Executive Function across the Transition to School: A Latent Variable Approach.” Developmental Neuropsychology 35 (1): 20–36.CrossRefGoogle Scholar
Kalisch, M., Mächler, M., Colombo, D., Maathuis, M. H., and Bühlmann, P.. 2012. “Causal Inference Using Graphical Models with the R Package pcalg.” Journal of Statistical Software 47 (11): 1–26.CrossRefGoogle Scholar
Kummerfeld, Erich, and Ramsey, Joseph. 2015. “Finding One-Factor Clusters.” Unpublished manuscript, Department of Philosophy, Carnegie Mellon University.Google Scholar
Lester, Laurence H. 2008. “A Multiple Indicators and Multiple Causes (MIMIC) Model of Immigrant Settlement Success.” Working Paper no. 160, National Institute of Labour Studies, Adelaide.Google Scholar
Murray-Watters, Alexander. 2014. “The DM Algorithm: A Causal Search Algorithm for the Discovery of MIMIC Models, with an Attempt to Recover a Protein Signalling Network from a High-Dimensional Ovarian Cancer Dataset.” MS thesis, Carnegie Mellon University.Google Scholar
Pearl, Judea. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.Google Scholar
Pearl, Judea 2000. Causality: Models, Reasoning and Inference. Vol. 29. Cambridge, MA: MIT Press.Google Scholar
Richardson, Thomas, and Spirtes, Peter. 2002. “Ancestral Graph Markov Models.” Annals of Statistics 30 (4): 962–1030.CrossRefGoogle Scholar
Rosenhouse, J. 2009. The Monty Hall Problem: The Remarkable Story of Math’s Most Contentious Brain Teaser. Oxford: Oxford University Press.Google Scholar
Silva, R., Scheines, R., Glymour, C., and Spirtes, P.. 2006. “Learning the Structure of Linear Latent Variable Models.” Journal of Machine Learning Research 7:191–246.Google Scholar
Sober, Elliott. 1998. “Black-Box Inference: When Should Intervening Variables Be Postulated?” British Journal for the Philosophy of Science 49 (3): 469–98.CrossRefGoogle Scholar
Spirtes, Peter, and Glymour, Clark. 1991. “An Algorithm for Fast Recovery of Sparse Causal Graphs.” Social Science Computer Review 9 (1): 62–72.CrossRefGoogle Scholar
Spirtes, Peter, Glymour, Clark, and Scheines, Richard. 2000. Causation, Prediction, and Search. Cambridge, MA: MIT Press.Google Scholar
Tedds, Lindsay M. 1998. “Measuring the Size of the Hidden Economy in Canada: A Latent Variable/MIMIC Model Approach.” MA extended essay, University of Victoria.Google Scholar
Tian, Jin, and Pearl, Judea. 2002. “On the Testable Implications of Causal Models with Hidden Variables.” In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, 519–27. San Francisco: Morgan Kaufmann.Google Scholar
Verma, T., and Pearl, J.. 1992. “An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation.” In Proceedings of the Eighth International Conference on Uncertainty in Artificial Intelligence, 323–30. San Francisco: Morgan Kaufmann.Google Scholar
Williamson, Jon, and Gabbay, Dov. 2005. “Recursive Causality in Bayesian Networks and Self-Fibring Networks.” In Laws and Models in the Sciences, ed. Gillies, Donald, 173–221. London: King’s College.Google Scholar
Zhang, Jiji. 2008. “Causal Reasoning with Ancestral Graphs.” Journal of Machine Learning Research 9:1437–74.Google Scholar

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