We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Order-space Markov chain Monte Carlo, equi-energy sampling, importance weighting, and stream-based computation are combined to create a fast algorithm for learning causal Bayesian network structures. © 2008 American Statistical Association.
CITATION STYLE
Ellis, B., & Wong, W. H. (2008). Learning causal Bayesian network structures from experimental data. Journal of the American Statistical Association, 103(482), 778–789. https://doi.org/10.1198/016214508000000193
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