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8 - Causal Modeling, Explanation and Severe Testing

Published online by Cambridge University Press:  29 January 2010

By
Clark Glymour ,
Deborah G. Mayo  and
Aris Spanos
Edited by
Deborah G. Mayo  and
Aris Spanos
Deborah G. Mayo
Affiliation:
Virginia Polytechnic Institute and State University
Aris Spanos
Affiliation:
Virginia Polytechnic Institute and State University
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Summary

The Issues

There are at least two aspects to understanding: comprehensibility and veracity. Good explanations are supposed to aid the first and to give grounds for believing the second – to provide insight into the “hidden springs” that produce phenomena and to warrant that our estimates of such structures are correct enough. For Copernicus, Kepler, Newton, Dalton, and Einstein, the virtues of explanations were guides to discovery, to sorting among hypotheses and forming beliefs. The virtues were variously named: simplicity, harmony, unity, elegance, and determinacy. These explanatory virtues have never been well articulated, but what makes them contributions to perspicacity is sometimes apparent, while what makes them a guide to truth is obscure.

We know that under various assumptions there is a connection between testing – doing something that could reveal the falsity of various claims – and the second aspect of coming to understand. Various forms of testing can be stages in strategies that reliably converge on the truth, and some cases even provide probabilistic guarantees that the truth is not too far away. But how can explaining be any kind of reliable guide to any truth worth knowing? What structure or content of thoughts or acts distinguishes explanations, and in virtue of what, if anything, are explanations indications of truth that should prompt belief in their content, or provide a guide in inquiry?

Type
Chapter
Information
Error and Inference
Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science
 , pp. 331 - 375
Publisher: Cambridge University Press
Print publication year: 2009

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References

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Druzdzel, M., and Voortman, M. (2008), “Insensitivity of Constraint-Based Causal Discovery Algorithms to Violations of the Assumption of Multivariate Normality,” Proceedings of the Florida Artificial Intelligence Research Conference (FLAIRS) (to come).Google Scholar
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Zhang, J. (December 2002), “Consistency in Causal Inference under a Variety of Assumptions,” M.S. Thesis, Department of Philosophy, Carnegie Mellon University.
Zhang, J., and Spirtes, P. (2007), “Detection of Unfaithfulness and Robust Causal Inference,” philsci-archive.pitt.edu.
Zhang, J. (2008), “Error Probabilities for Inference of Causal Directions,” Synthese (Error and Methodology in Practice: Selected Papers from ERROR 2006), 163(3): 409–18.Google Scholar
Laudan, L. (1997), “How about Bust? Factoring Explanatory Power Back into Theory Evaluation,” Philosophy of Science, 64: 306–16.CrossRefGoogle Scholar
Mayo, D.G. (1996), Error and the Growth of Experimental Knowledge, University of Chicago Press, Chicago.CrossRefGoogle Scholar
Mayo, D.G. (1997a), “Duhem's Problem, the Bayesian Way, and Error Statistics, or ‘What's Belief Got to Do with It?’” Philosophy of Science, 64: 222–44.CrossRefGoogle Scholar
Prusiner, S.B. (2003), Prion Biology and Diseases, 2nd ed., Cold Spring Harbor, Laboratory Press, Woodbury, NY.Google Scholar
Mayo, D.G. and Miller, J., (2008), “The Error Statistical Philosopher As Normative Naturalist,” Synthese (Error and Methodology in Practice: Selected Papers from ERROR 2006), 163(3): 305–14.Google Scholar
Parker, W.S. (2008), “Computer Simulation Through An Error-Statistical Lens,” Synthese (Error and Methodology in Practice: Selected Papers from ERROR 2006), 163(3): 371–84.Google Scholar

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