Efficient Computation of Intervention in Causal Bayesian Networks

In causal Bayesian networks (CBNs),it is a fundamental problem to compute the causal effect of sum product.From the perspective of a directed acyclic graph,we show every CBN has a corresponding Bayesian network.Intervention is a fundamental operation in CBNs.Similar to Bayesian networks,CBNs also have the pruning strategy.After pruning the barren nodes,this paper devises an optimized jointree algorithm to compute the full atomic intervention on each node in a CBN.Then,this paper explores the multiple interventions on multiple nodes,and finds that multiple interventions have the commutative property.On the basis of the commutative property in multiple interventions,this paper proves the strategies,which can be used to optimize the computation of the causal effect of multiple interventions.Finally,we report experimental results to demonstrate the efficiency of our algorithm to compute the causal effects in CBNs.