We study the problem of local causal discovery learning which identifies direct causes and effects of a target variable of interest in a causal network. The existing constraint-based local causal discovery approaches are inefficient, since these approaches do not take a triangular structure formed by a given variable and its child variables into account in learning local causal structure, and hence need to spend much time in distinguishing several direct effects. Additionally, these approaches depend on the standard MB (Markov Blanket) or PC (Parent and Children) discovery algorithms which demand to conduct lots of conditional independence tests to obtain the MB or PC sets. To overcome the above problems, in this paper, we propose a novel Efficient Local Causal Discovery algorithm via MB (ELCD) to identify direct causes and effects of a given variable. More specifically, we design a new algorithm for Efficient Oriented MB discovery, name EOMB. EOMB not only utilizes fewer conditional independence tests to identify MB, but also is able to identify more direct effects of a given variable with the help of triangular causal structures and determine several direct causes as much as possible. In addition, based on the proposed EOMB, ELCD is presented to learn a local causal structure around a target variable. The benefits of ELCD are that it not only can determine the direct causes and effects of a given variable accurately, but also runs faster than other local causal discovery algorithms. Experimental results on eight Bayesian networks (BNs) show that our proposed approach performs better than state-of-the-art baseline methods.

中文翻译：

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基于马尔可夫毯的高效局部因果发现

我们研究了局部因果发现学习的问题，该学习识别因果网络中感兴趣的目标变量的直接原因和影响。现有的基于约束的局部因果发现方法效率低下，因为这些方法在学习局部因果结构时没有考虑给定变量及其子变量形成的三角形结构，因此需要花费大量时间来区分几个直接影响. 此外，这些方法依赖于标准 MB（马尔可夫毯）或 PC（父子）发现算法，这些算法需要进行大量条件独立性测试以获得 MB 或 PC 集。为克服上述问题，在本文中，我们通过 MB (ELCD) 提出了一种新颖的高效局部因果发现算法来识别给定变量的直接原因和影响。更具体地说，我们设计了一种用于高效定向 MB 发现的新算法，名为 EOMB。EOMB 不仅利用较少的条件独立性测试来识别 MB，而且能够借助三角因果结构识别给定变量的更多直接影响，并尽可能确定几个直接原因。此外，基于所提出的 EOMB，ELCD 被提出来学习围绕目标变量的局部因果结构。ELCD 的好处在于它不仅可以准确地确定给定变量的直接因果关系，而且运行速度比其他局部因果发现算法更快。