Hybrid Local Causal Structure Learning

Abstract

ocal causal structure learning focuses on identifying the direct causes and direct effects of a given target variable without learning an entire causal network. Existing local causal structure learning algorithms are usually completed by two steps. Step 1 uses constraint-based methods to learn the Markov blanket (MB) or parents and children (PC) set of the target variable by conditional independence tests. However, due to small sample sizes, this may lead to unreliable tests and the accuracy of this step is usually not very high. Step 2 uses found V structures and Meek rules for distinguishing direct causes from direct effects of the target variable. But this step depends on the discovery of V structure extremely and synchronous sampling is also affected by limited samples. The accuracy of the algorithm is not very high. To solve the above problems, this paper proposes a hybrid local causal structure learning algorithm based on the combination of scoring and constraint. In step 1, a new PC learning algorithm SIAPC (score-based incremental association parents and children) is proposed by adding scoring idea into the constraint based algorithm. In step 2, the direction of the edge is determined by using the intersection of the orientation result obtained by PC algorithm and the orientation result obtained by grading some data sets, so as to reduce the dependence on V structure and alleviate the finite sample problem. After that, this paper uses independence test to modify the orientation results of the edges to further improve the accuracy of the algorithm, and then proposes HLCS (hybrid local causal structure learning) algorithm. Using benchmark Bayesian networks, the experimental results show that the algorithm proposed in this paper has better performance than the existing algorithms in terms of learning accuracy and reducing the data efficiency.