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Measuring Causal Specificity

Published online by Cambridge University Press:  01 January 2022

Paul E. Griffiths ,
Arnaud Pocheville ,
Brett Calcott ,
Karola Stotz ,
Hyunju Kim  and
Rob Knight
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Abstract

Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the specificity of coding DNA and other factors in a simple model of the production of mRNA.

Type
Research Article
Information
Philosophy of Science , Volume 82 , Issue 4 , October 2015 , pp. 529 - 555
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

This publication was made possible through the support of a grant from the Templeton World Charity Foundation. The opinions expressed are those of the author(s) and do not necessarily reflect the views of the Templeton World Charity Foundation. Brett Calcott was supported by Joshua Epstein’s NIH Director’s Pioneer Award DP1OD003874 from the Office of the Director, National Institutes of Health. The article is the result of a workshop held at the University of Colorado, Boulder, with support from Templeton World Charity Foundation. BC, PG, AP, and, KS wrote the manuscript, and all authors agreed on the final content. We would like to thank two anonymous referees for their helpful comments.

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