Causal interaction in bayesian networks

Abstract

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—that of understanding causality. Bayesian network techniques have recently been used by Judea Pearl in a new approach to understanding causality and causal processes (Pearl, 2000). Pearl’s approach has great promise, but needs to be supplemented with an explicit account of causal interaction. Thus far, despite considerable interest, philosophy has provided no useful account of causal interaction. Here we provide one, employing the concepts of Bayesian networks. With it we demonstrate the failure of one of philosophy’s more sophisticated attempts to deal with the concept of causal interaction, that of Ellery Eells’ Probabilistic Causality (1991).

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 90,593

External links

  • This entry has no external links. Add one.
Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

  • Only published works are available at libraries.

Similar books and articles

Analytics

Added to PP
2009-01-28

Downloads
11 (#975,863)

6 months
0

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Charles R. Twardy
George Mason University

Citations of this work

No citations found.

Add more citations

References found in this work

No references found.

Add more references