On the Logic of Causal Models

Geiger, Dan; Pearl, Judea

On the Logic of Causal Models

UAI 1988

/uai/1988/geiger1988uai-logic/

Abstract

This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of conditional independence relationships. We show that DAGs offer polynomially sound and complete inference mechanisms for inferring conditional independence relationships from a given causal set of such relationships. As a consequence, d-separation, a graphical criterion for identifying independencies in a DAG, is shown to uncover more valid independencies then any other criterion. In addition, we employ the Armstrong property of conditional independence to show that the dependence relationships displayed by a DAG are inherently consistent, i.e. for every DAG D there exists some probability distribution P that embodies all the conditional independencies displayed in D and none other.

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Text

Geiger and Pearl. "On the Logic of Causal Models." Conference on Uncertainty in Artificial Intelligence, 1988.

Markdown

[Geiger and Pearl. "On the Logic of Causal Models." Conference on Uncertainty in Artificial Intelligence, 1988.](https://mlanthology.org/uai/1988/geiger1988uai-logic/)

BibTeX

@inproceedings{geiger1988uai-logic,
  title     = {{On the Logic of Causal Models}},
  author    = {Geiger, Dan and Pearl, Judea},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1988},
  url       = {https://mlanthology.org/uai/1988/geiger1988uai-logic/}
}