论文标题
可靠的因果发现，并改进了精确的搜索和较弱的假设
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
论文作者
论文摘要
许多因果发现方法都依赖于忠诚的假设来保证渐近的正确性。但是，该假设可以在许多方面大致违反，从而导致了亚最佳解决方案。尽管贝叶斯网络结构学习中有一系列研究，重点是削弱假设，例如具有明确定义得分函数的精确搜索方法，但它们不能很好地扩展到大图。在这项工作中，我们介绍了几种策略，以提高线性高斯环境中基于得分的精确方法的可扩展性。特别是，我们基于支持逆协方差矩阵的支持，开发了一种超结构估计方法，该方法需要严格比忠诚的假设，并将其应用于限制精确搜索的搜索空间。我们还提出了一个本地搜索策略，该策略在超结构中的两个啤酒花中在每个变量及其邻居形成的本地群集上执行精确搜索。数值实验验证了所提出的程序的功效，并证明它以高精度缩放到数百个节点。
Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, leading to sub-optimal solutions. Although there is a line of research in Bayesian network structure learning that focuses on weakening the assumption, such as exact search methods with well-defined score functions, they do not scale well to large graphs. In this work, we introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting. In particular, we develop a super-structure estimation method based on the support of inverse covariance matrix which requires assumptions that are strictly weaker than faithfulness, and apply it to restrict the search space of exact search. We also propose a local search strategy that performs exact search on the local clusters formed by each variable and its neighbors within two hops in the super-structure. Numerical experiments validate the efficacy of the proposed procedure, and demonstrate that it scales up to hundreds of nodes with a high accuracy.
