Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation

• URL: http://arxiv.org/abs/2209.08579v1
• Date: Sun, 18 Sep 2022 15:04:55 GMT
• Title: Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
• Authors: Yang Ni
• Abstract summary: We propose a novel causal model for categorical data based on a new classification model, termed classification with optimal label permutation (COLP) A simple learning algorithm via comparing likelihood functions of causal and anti-causal models suffices to learn the causal direction.
• Score: 2.0305676256390934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal discovery for quantitative data has been extensively studied but less is known for categorical data. We propose a novel causal model for categorical data based on a new classification model, termed classification with optimal label permutation (COLP). By design, COLP is a parsimonious classifier, which gives rise to a provably identifiable causal model. A simple learning algorithm via comparing likelihood functions of causal and anti-causal models suffices to learn the causal direction. Through experiments with synthetic and real data, we demonstrate the favorable performance of the proposed COLP-based causal model compared to state-of-the-art methods. We also make available an accompanying R package COLP, which contains the proposed causal discovery algorithm and a benchmark dataset of categorical cause-effect pairs.

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