Andrews Bryan, Ramsey Joseph, Cooper Gregory F
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Proc Mach Learn Res. 2019 Aug;104:4-21.
In recent years, great strides have been made for causal structure learning in the high-dimensional setting and in the mixed data-type setting when there are both discrete and continuous variables. However, due to the complications involved with modeling continuous-discrete variable interactions, the intersection of these two settings has been relatively understudied. The current paper explores the problem of efficiently extending causal structure learning algorithms to high-dimensional data with mixed data-types. First, we characterize a model over continuous and discrete variables. Second, we derive a degenerate Gaussian (DG) score for mixed data-types and discuss its asymptotic properties. Lastly, we demonstrate the practicality of the DG score on learning causal structures from simulated data sets.
近年来，在高维环境以及存在离散和连续变量的混合数据类型环境下，因果结构学习取得了长足进展。然而，由于对连续-离散变量交互进行建模存在复杂性，这两种环境的交叉领域相对较少受到研究。本文探讨了将因果结构学习算法有效扩展到具有混合数据类型的高维数据的问题。首先，我们刻画了一个关于连续和离散变量的模型。其次，我们推导出了混合数据类型的退化高斯（DG）分数，并讨论了其渐近性质。最后，我们通过模拟数据集展示了DG分数在学习因果结构方面的实用性。
