论文标题
贝叶斯网络结构学习在存在潜在变量的情况下具有因果关系
Bayesian network structure learning with causal effects in the presence of latent variables
论文作者
论文摘要
潜在变量可能会导致虚假的关系，这些关系可能会被误解为因果关系。在贝叶斯网络（BNS）中，这一挑战被称为因果关系不足下的学习。假设因果关系不足的结构学习算法倾向于重建BN的祖先图，在该图形中，双向的边缘代表混杂的边缘和有向的边缘代表直接或祖先的关系。本文介绍了一种称为CCHM的混合结构学习算法，该算法将基于约束的CFCI部分与基于爬山的分数学习结合在一起。基于得分的过程结合了珍珠级钙库以测量因果关系和定向边缘，否则这些效果将保持无方向性，在假设下，BN是一个线性结构方程模型，其中数据遵循多元高斯分布。基于随机和知名网络的实验表明，CCHM在重建真正的祖先图方面改善了最新的。
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships. This paper describes a hybrid structure learning algorithm, called CCHM, which combines the constraint-based part of cFCI with hill-climbing score-based learning. The score-based process incorporates Pearl s do-calculus to measure causal effects and orientate edges that would otherwise remain undirected, under the assumption the BN is a linear Structure Equation Model where data follow a multivariate Gaussian distribution. Experiments based on both randomised and well-known networks show that CCHM improves the state-of-the-art in terms of reconstructing the true ancestral graph.
