Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
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DOI：
10.48550/arxiv.2209.08579
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Yang Ni
中科院分区：
文献类型：
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作者：
Yang Ni
定量数据的因果发现已被广泛研究，但对分类数据的因果发现知之甚少。本文提出了一种基于最优标签排列（COLP）分类模型的分类数据因果模型。通过设计，COLP是一个简约的分类器，它产生了一个可证明可识别的因果模型。通过比较因果模型和反因果模型的似然函数，一个简单的学习算法就足以学习因果方向。通过合成数据和真实数据的实验，我们证明了与现有方法相比，所提出的基于colp的因果模型具有良好的性能。我们还提供了附带的R包COLP，其中包含所提出的因果发现算法和分类因果对的基准数据集。
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.