Ordinal Causal Discovery
This addresses the challenge of causal discovery in categorical data for researchers and practitioners, offering improved identifiability and performance, though it is incremental as it builds on existing score-and-search algorithms.
The paper tackles the problem of causal discovery for observational categorical data, which often leaves causal directions undetermined, by proposing an identifiable ordinal causal discovery method that uniquely identifies causal structures using ordinal information, and demonstrates favorable and robust performance compared to state-of-the-art methods in experiments.
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.