Ordinal Causal Discovery
- URL: http://arxiv.org/abs/2201.07396v1
- Date: Wed, 19 Jan 2022 03:11:26 GMT
- Title: Ordinal Causal Discovery
- Authors: Yang Ni and Bani Mallick
- Abstract summary: 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.
We show that the proposed ordinal causal discovery method has favorable and robust performance compared to state-of-the-art alternative methods in both ordinal categorical and non-categorical data.
- Score: 2.0305676256390934
- License: http://creativecommons.org/licenses/by/4.0/
- 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. 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. Simple score-and-search
algorithms are developed for structure learning. 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 has favorable and robust performance compared to
state-of-the-art alternative methods in both ordinal categorical and
non-categorical data.
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