Optimal structure identification with greedy search
The Journal of Machine Learning Research
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning causal bayesian networks from observations and experiments: a decision theoretic approach
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
The Journal of Machine Learning Research
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From observational data alone, a causal DAG is only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the gain of an intervention strongly depends on the intervention target, that is, the intervened variables. We present active learning (that is, optimal experimental design) strategies calculating optimal interventions for two different learning goals. The first one is a greedy approach using single-vertex interventions that maximizes the number of edges that can be oriented after each intervention. The second one yields in polynomial time a minimum set of targets of arbitrary size that guarantees full identifiability. This second approach proves a conjecture of Eberhardt (2008) [1] indicating the number of unbounded intervention targets which is sufficient and in the worst case necessary for full identifiability. In a simulation study, we compare our two active learning approaches to random interventions and an existing approach, and analyze the influence of estimation errors on the overall performance of active learning.