{"type":"ScholarlyArticle","title":"Active Learning for Structure in Bayesian Networks","authors":[{"name":"Simon Tong","slug":"simon-tong","given":"Simon","family":"Tong","affiliation":""},{"name":"Daphne Koller","slug":"daphne-koller","given":"Daphne","family":"Koller","affiliation":""}],"abstract":"The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments are typically expensive, and must be selected with great care. This paper uses active learning to determine the experiments that are most informative towards uncovering the underlying structure. We formalize the causal learning task as that of learning the structure of a causal Bayesian network. We consider an active learner that is allowed to conduct experiments, where it intervenes in the domain by setting the values of certain variables. We provide a theoretical framework for the active learning problem, and an algorithm that actively chooses the experiments to perform based on the model learned so far. Experimental results show that active learning can substantially reduce the number of observations required to determine the structure of a domain.","doi":null,"url":"https://ijcai.org/proceedings/2001/118","pdf":"https://ijcai.org/proceedings/2001/0118.pdf","markdown":"https://ijcai.org/proceedings/2001/118.md","bibtex":"https://ijcai.org/proceedings/2001/bibtex/118","volume":{"slug":"2001","title":"Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2001","url":"https://ijcai.org/proceedings/2001/","year":2001,"location":"Seattle, Washington, USA","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":118,"pages":{"first":863,"last":868},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}