摘要翻译：
我们针对有限数据集因果推理的准确性和鲁棒性问题。一些最先进的算法产生清晰的输出，具有坚实的理论保证，但容易传播错误的决策，而另一些算法非常擅长处理和表示不确定性，但需要依赖于不希望的假设。我们的目标是将贝叶斯方法固有的鲁棒性与基于约束的方法的理论强度和清晰度结合起来。我们使用贝叶斯得分来获得在基于约束的过程中使用的输入语句的概率估计。这些数据随后按可靠性的递减顺序进行处理，在con icts的情况下，让更可靠的决策优先，直到获得单个输出模型。测试表明，基于贝叶斯约束的因果发现(BCCD)算法的基本实现已经优于FCI和保守PC等既定程序。它还可以指示输出中哪些因果决策具有高可靠性，哪些没有。
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英文标题：
《A Bayesian Approach to Constraint Based Causal Inference》
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作者：
Tom Claassen, Tom Heskes
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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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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  We target the problem of accuracy and robustness in causal inference from finite data sets. Some state-of-the-art algorithms produce clear output complete with solid theoretical guarantees but are susceptible to propagating erroneous decisions, while others are very adept at handling and representing uncertainty, but need to rely on undesirable assumptions. Our aim is to combine the inherent robustness of the Bayesian approach with the theoretical strength and clarity of constraint-based methods. We use a Bayesian score to obtain probability estimates on the input statements used in a constraint-based procedure. These are subsequently processed in decreasing order of reliability, letting more reliable decisions take precedence in case of con icts, until a single output model is obtained. Tests show that a basic implementation of the resulting Bayesian Constraint-based Causal Discovery (BCCD) algorithm already outperforms established procedures such as FCI and Conservative PC. It can also indicate which causal decisions in the output have high reliability and which do not.
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PDF链接：
https://arxiv.org/pdf/1210.4866