Measuring causal interaction in bayesian networks


Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—understanding causality. Bayesian networks have recently been used by Judea Pearl in a new approach to understanding causality (Pearl, 2000). Part of understanding causality is understanding causal interaction. Bayes nets can represent any degree of causal interaction, and researchers normally try to limit interactions, usually by replacing the full CPT with a noisy-OR function. But we show that noisy-OR and another common model are merely special cases of the general linear systems definition of noninteraction. However, they apply in different situations, and we can measure the degree of causal interaction relative to any such model.



    Upload a copy of this work     Papers currently archived: 74,466

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.


Added to PP

5 (#1,160,914)

6 months

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