Causal Networks: Semantics and Expressiveness
Abstract
Dependency knowledge of the form "x is independent of y once z is known" invariably obeys the four graphoid axioms, examples include probabilistic and database dependencies. Often, such knowledge can be represented efficiently with graphical structures such as undirected graphs and directed acyclic graphs (DAGs). In this paper we show that the graphical criterion called d-separation is a sound rule for reading independencies from any DAG based on a causal input list drawn from a graphoid. The rule may be extended to cover DAGs that represent functional dependencies as well as conditional dependencies.
Cite
Text
Verma and Pearl. "Causal Networks: Semantics and Expressiveness." Conference on Uncertainty in Artificial Intelligence, 1988.Markdown
[Verma and Pearl. "Causal Networks: Semantics and Expressiveness." Conference on Uncertainty in Artificial Intelligence, 1988.](https://mlanthology.org/uai/1988/verma1988uai-causal/)BibTeX
@inproceedings{verma1988uai-causal,
title = {{Causal Networks: Semantics and Expressiveness}},
author = {Verma, Tom S. and Pearl, Judea},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1988},
url = {https://mlanthology.org/uai/1988/verma1988uai-causal/}
}