论文标题
序数因果发现
Ordinal Causal Discovery
论文作者
论文摘要
纯观测数据的因果发现是一个长期的挑战性问题。与连续数据不同，绝大多数用于分类数据的现有方法仅着眼于推断马尔可夫等效类别，这使某些因果关系的方向尚不确定。本文提出了一种可识别的顺序因果发现方法，该方法利用许多现实世界应用中包含的序数信息以唯一识别因果结构。提出的方法可通过数据离散化超出序数数据。通过现实世界和综合实验，我们证明了与简单的分数和搜索算法结合使用的拟议的序数因果发现方法，与在序数分类和非类别数据中的最先进的替代方法相比，相比之下。在cran和https://web.stat.tamu.edu/~yni/files/files/ordcd_1.0.0.0.0.tar.gz上免费获得伴随的R包ORDCD。
Causal discovery for purely observational, categorical data is a long-standing challenging problem. Unlike continuous data, the vast majority of existing methods for categorical data focus on inferring the Markov equivalence class only, which leaves the direction of some causal relationships undetermined. This paper proposes an identifiable ordinal causal discovery method that exploits the ordinal information contained in many real-world applications to uniquely identify the causal structure. The proposed method is applicable beyond ordinal data via data discretization. Through real-world and synthetic experiments, we demonstrate that the proposed ordinal causal discovery method combined with simple score-and-search algorithms has favorable and robust performance compared to state-of-the-art alternative methods in both ordinal categorical and non-categorical data. An accompanied R package OrdCD is freely available on CRAN and at https://web.stat.tamu.edu/~yni/files/OrdCD_1.0.0.tar.gz.
