Learning Causal Bayesian Network Structures From Experimental Data

This is the authors' abstract. We don't add key points for this paper.

AbstractWe 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.