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 OCD is freely available at https://web.stat.tamu.edu/~yni/files/OCD_0.1.0.tar.gz.

翻译：与连续数据不同,现有绝大多数绝对数据方法仅侧重于推断Markov等同等级,这导致某些因果关系方向未定。本文件建议采用可识别的因果发现方法,利用许多真实世界应用中包含的正统信息,独一地确定因果关系结构。拟议方法的适用范围超过数据分解的奥氏数据。通过现实世界和合成实验,我们证明,与简单分数和搜索算法相结合的拟议因果发现方法与最先进的非分类绝对数据替代方法相比,与最先进的分数和最先进的方法相比,具有有利和有力的性能。随附的R包 OCD可免费查阅https://web.stat.tamu.edu/~yni/files/OCD_01.0.tar.gz。