Identification of conditional interventional distributions

Ilya Shpitser, Judea Pearl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The subject of this paper is the elucidation of effects of actions from causal assumptions represented as a directed graph, and statistical knowledge given as a probability distribution. In particular, we are interested in predicting distributions on post-action outcomes given a set of measurements. We provide a necessary and sufficient graphical condition for the cases where such distributions can be uniquely computed from the available information, as well as an algorithm which performs this computation whenever the condition holds. Furthermore, we use our results to prove completeness of do-calculus [Pearl, 1995] for the same identification problem, and show applications to sequential decision making.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Pages437-444
Number of pages8
StatePublished - Dec 1 2006
Event22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 - Cambridge, MA, United States
Duration: Jul 13 2006Jul 16 2006

Publication series

NameProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Other

Other22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Country/TerritoryUnited States
CityCambridge, MA
Period7/13/067/16/06

ASJC Scopus subject areas

  • Artificial Intelligence

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