Abstract
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.
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Borchani, H., Chaouachi, M., Ben Amor, N. (2007). Learning Causal Bayesian Networks from Incomplete Observational Data and Interventions. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_5
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DOI: https://doi.org/10.1007/978-3-540-75256-1_5
Publisher Name: Springer, Berlin, Heidelberg
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