摘要翻译：
识别和控制偏见是经验科学中的一个关键问题。因果图理论为决定是否以及如何通过协变量调整从观察（非实验）数据中识别因果效应提供了图形标准。在这里，我们证明了现有的和新的调整准则之间的等价性，并提供了一个新的简化但仍然等价的D-分离概念。这些导致了因果图分析中两个重要任务的有效算法：（1）列出最小协变量调整（具有多项式延迟）；以及（2）识别（在线性时间内）偏置路径所涉及的子图。我们的结果改进了这些问题的现有指数时间解，使用户能够实时交互地评估协变量调整对几十到几百个变量的图的影响。
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英文标题：
《Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective》
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作者：
Johannes Textor, Maciej Liskiewicz
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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：
  Identifying and controlling bias is a key problem in empirical sciences. Causal diagram theory provides graphical criteria for deciding whether and how causal effects can be identified from observed (nonexperimental) data by covariate adjustment. Here we prove equivalences between existing as well as new criteria for adjustment and we provide a new simplified but still equivalent notion of d-separation. These lead to efficient algorithms for two important tasks in causal diagram analysis: (1) listing minimal covariate adjustments (with polynomial delay); and (2) identifying the subdiagram involved in biasing paths (in linear time). Our results improve upon existing exponential-time solutions for these problems, enabling users to assess the effects of covariate adjustment on diagrams with tens to hundreds of variables interactively in real time.
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PDF链接：
https://arxiv.org/pdf/1202.3764