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APPROPRIATE CAUSAL MODELS AND THE STABILITY OF CAUSATION

Published online by Cambridge University Press:  07 January 2016

JOSEPH Y. HALPERN
JOSEPH Y. HALPERN*
Affiliation:
Cornell University
*
*COMPUTER SCIENCE DEPARTMENT CORNELL UNIVERSITY E-mail: halpern@cs.cornell.edu
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Abstract

Causal models defined in terms of structural equations have proved to be quite a powerful way of representing knowledge regarding causality. However, a number of authors have given examples that seem to show that the Halpern–Pearl (HP) definition of causality (Halpern & Pearl, 2005) gives intuitively unreasonable answers. Here it is shown that, for each of these examples, we can give two stories consistent with the description in the example, such that intuitions regarding causality are quite different for each story. By adding additional variables, we can disambiguate the stories. Moreover, in the resulting causal models, the HP definition of causality gives the intuitively correct answer. It is also shown that, by adding extra variables, a modification to the original HP definition made to deal with an example of Hopkins & Pearl (2003) may not be necessary. Given how much can be done by adding extra variables, there might be a concern that the notion of causality is somewhat unstable. Can adding extra variables in a “conservative” way (i.e., maintaining all the relations between the variables in the original model) cause the answer to the question “Is X = x a cause of Y = y?” to alternate between “yes” and “no”? It is shown that we can have such alternation infinitely often, but if we take normality into consideration, we cannot. Indeed, under appropriate normality assumptions. Adding an extra variable can change the answer from “yes’ to “no”, but after that, it cannot change back to “yes”.

Type
Research Article
Information
The Review of Symbolic Logic , Volume 9 , Issue 1 , March 2016 , pp. 76 - 102
Copyright
Copyright © Association for Symbolic Logic 2016 

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