因果结构的时序逻辑

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摘要翻译：
具有潜在因果结构的时程数据的计算分析在许多领域都是需要的，包括神经尖峰列车、股票价格运动和基因表达水平。然而，仅从数值时间过程数据来确定是什么协调了可见的过程，将潜在的表面原因分为真正的和虚假的原因，并以可行的计算复杂度来这样做是具有挑战性的。为此，我们一直在开发一种新的算法，该算法基于一个框架，该框架将哲学中的因果关系概念与基于模型检验和统计技术的算法方法相结合，用于多假设测试。因果关系用时态逻辑公式描述，用模型检验的方法重构推理问题。所使用的逻辑，PCTL，允许描述因果之间的时间和这种关系被观察到的概率。通过多重假设检验（将每个因果关系视为假设）和错误发现控制的概念，我们可以计算一个原因对其结果的平均影响，从而发现统计上显著的原因。通过探索一个精心选择的具有合理最小描述长度的潜在所有重要假设族，在探索所有表面原因的几乎完全搜索空间的同时，有可能驯服算法的计算复杂性。我们已经在许多领域测试了这些想法，并在这里用两个例子来说明它们。
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英文标题：
《The Temporal Logic of Causal Structures》
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作者：
Samantha Kleinberg, Bud Mishra
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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：
  Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene expression levels. However, it can be challenging to determine from just the numerical time course data alone what is coordinating the visible processes, to separate the underlying prima facie causes into genuine and spurious causes and to do so with a feasible computational complexity. For this purpose, we have been developing a novel algorithm based on a framework that combines notions of causality in philosophy with algorithmic approaches built on model checking and statistical techniques for multiple hypotheses testing. The causal relationships are described in terms of temporal logic formulae, reframing the inference problem in terms of model checking. The logic used, PCTL, allows description of both the time between cause and effect and the probability of this relationship being observed. We show that equipped with these causal formulae with their associated probabilities we may compute the average impact a cause makes to its effect and then discover statistically significant causes through the concepts of multiple hypothesis testing (treating each causal relationship as a hypothesis), and false discovery control. By exploring a well-chosen family of potentially all significant hypotheses with reasonably minimal description length, it is possible to tame the algorithm's computational complexity while exploring the nearly complete search-space of all prima facie causes. We have tested these ideas in a number of domains and illustrate them here with two examples.
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PDF链接：
https://arxiv.org/pdf/1205.2634

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关键词：relationship Intelligence Coordinating Presentation Computation algorithm 因果关系 prima facie underlying