Learning causal Bayesian networks from incomplete observational data and interventions

被引：0

作者：

Borchani, Hanen ^{[1
]}

Chaouachi, Maher ^{[1
]}

Ben Amor, Nahla ^{[1
]}

机构：

[1] Inst Super Gest Tunis, LARODEC, 41 Ave La Liberte, Le Bardo 2000, Tunisia

来源：

SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, PROCEEDINGS | 2007年 / 4724卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a new method for learning causal Bayesian networks from incomplete observational data and interventions. We extend our Greedy Equivalence Search-Expectation Maximization (GES-EM) algorithm [2], initially proposed to learn Bayesian networks from incomplete observational data, by adding a new step allowing the discovery of correct causal relationships using interventional data. Two intervention selection approaches are proposed: an adaptive one, where interventions are done sequentially and where the impact of each intervention is considered before starting the next one, and a non-adaptive one, where the interventions are executed simultaneously. An experimental study shows the merits of the new version of the GES-EM algorithm by comparing the two selection approaches.

引用

页码：17 / +

页数：3