Hostname: page-component-5c6d5d7d68-pkt8n Total loading time: 0 Render date: 2024-08-14T21:27:06.891Z Has data issue: false hasContentIssue false
  • English
  • Français

Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence

Published online by Cambridge University Press:  01 January 2022

Jiji Zhang  and
Kun Zhang
Get access Rights & Permissions [Opens in a new window]

Abstract

Forster presented some interesting examples having to do with distinguishing the direction of causal influence between two variables, which he argued are counterexamples to the likelihood theory of evidence. In this article, we refute Forster’s arguments by carefully examining one of the alleged counterexamples. We argue that the example is not convincing as it relies on dubious intuitions that likelihoodists have forcefully criticized. More important, we show that contrary to Forster’s contention, the consilience-based methodology he favored is accountable within the framework of the likelihood theory of evidence.

Type
Confirmation Theory
Information
Philosophy of Science , Volume 82 , Issue 5 , December 2015 , pp. 930 - 940
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.)

Footnotes

†

This research was supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU342213.

References

Cover, Thomas M., and Thomas, Joy A.. 1991. Elements of Information Theory. New York: Wiley.CrossRefGoogle Scholar
Forster, Malcolm R. 1988. “Unification, Explanation, and the Composition of Causes in Newtonian Mechanics.” Studies in History and Philosophy of Science 19:55–101.CrossRefGoogle Scholar
Forster, Malcolm R. 2006. “Counterexamples to a Likelihood Theory of Evidence.” Minds and Machines 16 (3): 319–38.CrossRefGoogle Scholar
Hacking, Ian. 1965. The Logic of Statistical Inference. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hyvärinen, Aapo, and Smith, Stephen M.. 2013. “Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models.” Journal of Machine Learning Research 14:111–52.Google ScholarPubMed
Royall, Richard. 1997. Statistical Evidence: A Likelihood Paradigm. Boca Raton, FL: Chapman & Hall.Google Scholar
Sober, Elliott. 2008. Evidence and Evolution: The Logic Behind the Science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Whewell, William. 1858. Novum organon renovatum. London: Parker.Google Scholar
Woodward, James. 2003. Making Things Happen: A Theory of Causal Explanation. Oxford: Oxford University Press.Google Scholar
Zhang, Kun, Wang, Zhikun, Zhang, Jiji, and Schölkopf, Bernhard. 2015. “On Estimation of Functional Causal Models: Post-nonlinear Causal Model as an Example.” ACM Transactions on Intelligent Systems and Technologies, forthcoming.CrossRefGoogle Scholar