ML Anthology

A New Characterization of the Experimental Implications of Causal Bayesian Networks

Jin Tian, Judea Pearl
AAAI 2002 pp. 574-580
doi:10.5555/777092.777181 /aaai/2002/tian2002aaai-new/

Abstract

We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network. We show that such distributions must adhere to three norms of coherence, and we demonstrate the use of these norms as inferential tools in tasks of learning and identification. Testable coherence norms are subsequently derived for networks containing unmeasured variables.

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Text

Tian and Pearl. "A New Characterization of the Experimental Implications of Causal Bayesian Networks." AAAI Conference on Artificial Intelligence, 2002. doi:10.5555/777092.777181

Markdown

[Tian and Pearl. "A New Characterization of the Experimental Implications of Causal Bayesian Networks." AAAI Conference on Artificial Intelligence, 2002.](https://mlanthology.org/aaai/2002/tian2002aaai-new/) doi:10.5555/777092.777181

BibTeX

@inproceedings{tian2002aaai-new,
  title     = {{A New Characterization of the Experimental Implications of Causal Bayesian Networks}},
  author    = {Tian, Jin and Pearl, Judea},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2002},
  pages     = {574-580},
  doi       = {10.5555/777092.777181},
  url       = {https://mlanthology.org/aaai/2002/tian2002aaai-new/}
}