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摘要翻译：
本文讨论了在因果贝叶斯网络结构（即表示因果关系的有向无环图）中从非实验数据中识别偶然效应的图形判据。我们首先回顾了Pearl在这个主题上的工作[Pearl，1995]，其中提出了几个有用的图形标准。然后我们给出了一个完整的算法[Huang and Valtorta,2006b]。通过利用该算法的完备性，我们证明了Pearl提出的三个基本do-calculus规则是完备的，即如果一个因果效应是可识别的，则存在一系列do-calculus规则的应用，将因果效应公式转化为只包含观测量的公式。
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英文标题：
《Pearl's Calculus of Intervention Is Complete》
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作者：
Yimin Huang, Marco Valtorta
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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：
This paper is concerned with graphical criteria that can be used to solve the problem of identifying casual effects from nonexperimental data in a causal Bayesian network structure, i.e., a directed acyclic graph that represents causal relationships. We first review Pearl's work on this topic [Pearl, 1995], in which several useful graphical criteria are presented. Then we present a complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem. By exploiting the completeness of this algorithm, we prove that the three basic do-calculus rules that Pearl presents are complete, in the sense that, if a causal effect is identifiable, there exists a sequence of applications of the rules of the do-calculus that transforms the causal effect formula into a formula that only includes observational quantities.
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PDF链接：
https://arxiv.org/pdf/1206.6831
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