Uncertainty in Artificial Intelligence
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On the Equivalence of Causal Models
Tom Verma, Judea Pearl
Abstract:
Scientists often use directed acyclic graphs (days) to model the qualitative structure of causal theories, allowing the parameters to be estimated from observational data. Two causal models are equivalent if there is no experiment which could distinguish one from the other. A canonical representation for causal models is presented which yields an efficient graphical criterion for deciding equivalence, and provides a theoretical basis for extracting causal structures from empirical data. This representation is then extended to the more general case of an embedded causal model, that is, a dag in which only a subset of the variables are observable. The canonical representation presented here yields an efficient algorithm for determining when two embedded causal models reflect the same dependency information. This algorithm leads to a model theoretic definition of causation in terms of statistical dependencies.
Keywords:
Pages: 220-227
PS Link:
PDF Link: /papers/90/p220-verma.pdf
BibTex:
@INPROCEEDINGS{Verma90,
AUTHOR = "Tom Verma and Judea Pearl",
TITLE = "On the Equivalence of Causal Models",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "220--227"
}


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