Learning Causal Bayesian Network Structures From Experimental Data
Byron Ellis, Wing Hung Wong · Journal of the American Statistical Association · 2008
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.