中文

Decision-Theoretic Foundations for Causal Reasoning

人工智能 2014-11-17 v1

摘要

We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.

关键词

引用

@article{arxiv.cs/9512104,
  title  = {Decision-Theoretic Foundations for Causal Reasoning},
  author = {D. Heckerman and R. Shachter},
  journal= {arXiv preprint arXiv:cs/9512104},
  year   = {2014}
}

备注

See http://www.jair.org/ for any accompanying files