Title: Ordinal causal discovery
Authors: Yang Ni - Texas AM University (United States) [presenting]
Abstract: 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. An identifiable ordinal causal discovery method is proposed that exploits the ordinal information contained in many real-world applications to identify the causal structure uniquely. 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.