论文标题
通过最佳标签置换分类的分类数据的双变量因果发现
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
论文作者
论文摘要
定量数据的因果发现已经进行了广泛的研究，但对于分类数据而言，鲜为人知。我们根据新的分类模型提出了一个新的分类数据因果模型，该模型称为“最佳标签置换”（COLP）的分类。根据设计，COLP是一个简约的分类器，它产生了可识别的因果模型。一种简单的学习算法，通过比较因果关系的可能性功能，足以学习因果方向。通过使用合成和真实数据的实验，我们证明了与最先进的方法相比，基于COLP的因果模型的有利性能。我们还提供了一个随附的R软件包COLP，其中包含所提出的因果发现算法和分类原因对的基准数据集。
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.
