Bayesian Network Structure Learning Using Causality
Bayesian Network Structure Learning Using Causality
复制标题
使用因果关系进行贝叶斯网络结构学习
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Srihari
中科院分区：
文献类型：
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作者：
Zhen Xu;S. Srihari
贝叶斯网络是数据的概率模型，可用于回答概率查询。现有的算法要么使用局部度量偏离独立性，要么使用全局似然度量。它们基于概率相关性，因此模型的方向性缺乏我们所期望的因果意义。我们从一个新的角度来解决这个问题，使用因果关系，这是一个比相关性更基本的衡量标准。该算法结合了全局和局部因果推理的观点，在不使用任何基于分数的搜索的情况下学习了一个高质量的贝叶斯网络。给定一个部分有向无环图，用最少数量的对因果推理推断出精度最高的因果对。具体来说，对于离散数据，使用χ2统计检验来识别最相关和可能的因果对。此外，学习到的因果关系是前向传播的。在手写体数据上的实验表明，该算法除了具有因果推理能力外，还优于之前的两种算法，即基于分支定界搜索的算法和基于χ2检验和对数损失函数的贪心算法。学习结构不仅在表示数据方面损失最小，而且还揭示了对科学发现有用的潜在因果关系。
Bayesian Networks are probabilistic models of data that are useful to answer probabilistic queries. Existing algorithms use either local measures of deviation from independence or global likelihood measures. They are based on probabilistic correlation, so the directionality of the model lacks the causal meaning as we expected. We tackle this problem from a new perspective using causality, which is a more fundamental measure than correlation. Integrating both the global and local views of causal inference, the proposed computationally efficient algorithm learns a high quality Bayesian network without using any score-based searching. Given a partial directed acyclic graph, causal pairs with the highest accuracy are inferred with the fewest number of pair wise causal inferences. Specifically, with discrete data, the χ2 statistical test is used to identify the most dependent and possible causal pairs. Furthermore, the learned causality is forward-propagated. Experiments on handwriting data show that, besides the ability of causal inference, our algorithm performs better than two previous algorithms, one based on branch-and-bound search, and the other a greedy algorithm using χ2 tests and a log-loss function. The learned structure not only has lowest loss in representing the data, but also reveals underlying causal relationships which are useful for scientific discovery.