In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

中文翻译：

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因果和非因果特征选择的统一视图

在本文中，我们旨在开发因果和非因果特征选择方法的统一视图。统一的观点将填补两类方法关系研究的空白。基于贝叶斯网络框架和信息论，我们首先表明因果和非因果特征选择方法具有相同的目标。也就是找到一个类属性的马尔可夫毯，理论上最优的分类特征集。然后，我们检查因果和非因果特征选择方法在搜索最优特征集时所做的假设，并通过将它们映射到所研究问题的贝叶斯网络模型结构的限制来统一这些假设。我们进一步详细分析了结构假设如何导致方法在其搜索中采用的不同级别的近似，然后导致方法找到的特征集中关于最优特征集的近似。有了统一的观点，我们可以从因果的角度解释非因果方法的输出，并推导出这两种方法的误差界限。最后，我们通过对合成数据和各种类型的现实世界数据的广泛实验，提出了对因果方法和非因果方法之间关系的实际理解。我们可以从因果的角度解释非因果方法的输出，并推导出这两种方法的误差范围。最后，我们通过对合成数据和各种类型的现实世界数据的广泛实验，提出了对因果方法和非因果方法之间关系的实际理解。我们可以从因果的角度解释非因果方法的输出，并推导出这两种方法的误差范围。最后，我们通过对合成数据和各种类型的现实世界数据的广泛实验，提出了对因果方法和非因果方法之间关系的实际理解。