摘要翻译：
本文提出了基于差分的因果关系学习器(DBCL)，它是一种学习一类离散时间动态模型的算法，该模型通过驱动系统变化的差分方程来表示跨时间的所有因果关系。我们用真实世界的机械系统来激励这种表示，并证明DBCL从时间序列数据中学习结构的正确性，这一努力因必须检测的潜在导数的存在而变得复杂。我们还证明，在因果发现的一般假设下，DBCL将识别反馈环的存在或不存在，使模型更有助于预测当系统处于平衡状态时操纵变量的影响。我们从分析和实证两个方面论证了DBCL相对于向量自回归(VAR)和Granger因果关系模型的优势，以及贝叶斯和基于约束的结构发现算法的改进形式。最后，我们证明了我们的算法可以从脑电数据中发现人脑中α节律的因果方向。
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英文标题：
《Learning Why Things Change: The Difference-Based Causality Learner》
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作者：
Mark Voortman, Denver Dash, Marek J. Druzdzel
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In this paper, we present the Difference- Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL's correctness for learning structure from time series data, an endeavour that is complicated by the existence of latent derivatives that have to be detected. We also prove that, under common assumptions for causal discovery, DBCL will identify the presence or absence of feedback loops, making the model more useful for predicting the effects of manipulating variables when the system is in equilibrium. We argue analytically and show empirically the advantages of DBCL over vector autoregression (VAR) and Granger causality models as well as modified forms of Bayesian and constraintbased structure discovery algorithms. Finally, we show that our algorithm can discover causal directions of alpha rhythms in human brains from EEG data.
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PDF链接：
https://arxiv.org/pdf/1203.3525