Causal identifiability via Chain Event Graphs
| Autor: | Peter A. Thwaites |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
Discrete mathematics
Linguistics and Language Class (set theory) Theoretical computer science Front Door theorem Bayesian network Language and Linguistics Chain Event Graph Causal identifiability Causal manipulation Chain (algebraic topology) Conditional independence Artificial Intelligence Bayesian Network Back Door theorem Identifiability Graphical model Representation (mathematics) Computer Science::Databases Event (probability theory) Mathematics |
| Zdroj: | Artificial Intelligence. :291-315 |
| ISSN: | 0004-3702 |
| DOI: | 10.1016/j.artint.2012.09.003 |
| Popis: | We present the Chain Event Graph (CEG) as a complementary graphical model to the Causal Bayesian Network for the representation and analysis of causally manipulated asymmetric problems. Our focus is on causal identifiability - finding conditions for when the effects of a manipulation can be estimated from a subset of events observable in the unmanipulated system. CEG analogues of [email protected]?s Back Door and Front Door theorems are presented, applicable to the class of singular manipulations, which includes both [email protected]?s basic Do intervention and the class of functional manipulations possible on Bayesian Networks. These theorems are shown to be more flexible than their Bayesian Network counterparts, both in the types of manipulation to which they can be applied, and in the nature of the conditioning sets which can be used. |
| Databáze: | OpenAIRE |
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