Uncertainty in Artificial Intelligence
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On the Semi-Markov Equivalence of Causal Models
Benoit Desjardins
Abstract:
The variability of structure in a finite Markov equivalence class of causally sufficient models represented by directed acyclic graphs has been fully characterized.    Without causal sufficiency, an infinite semi-Markov equivalence class of models has only been characterized by the fact that each model in the equivalence class entails the same marginal statistical dependencies.  In this paper, we study the variability of structure of causal models within a semi-Markov equivalence class and propose a systematic approach to construct models entailing any specific marginal statistical dependencies.
Keywords: Causal modeling, latent variables, semi-Markov equivalence.
Pages: 105-112
PS Link:
PDF Link: /papers/98/p105-desjardins.pdf
BibTex:
@INPROCEEDINGS{Desjardins98,
AUTHOR = "Benoit Desjardins ",
TITLE = "On the Semi-Markov Equivalence of Causal Models",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "105--112"
}


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