摘要翻译：
Galles和Pearl声称，“对于递归模型，除了刘易斯的[可能世界]框架所施加的限制之外，因果模型框架并没有对反事实增加任何限制。”对这一主张进行了仔细的审查，目的是澄清因果模型和刘易斯框架之间的确切关系。递归模型被证明精确地对应于（可能世界）反事实结构的一个子类。另一方面，递归模型，即所有方程都有唯一解的模型的一个轻微的推广，在表达能力上被证明是反事实结构所无法比拟的，尽管事实上Galles和Pearl的论点也应该适用于它们。Galles和Pearl论证的问题被确定了：一个他们认为无关紧要的公理，因为它涉及到分离（在他们的语言中没有），根本不是无关紧要的。
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英文标题：
《From Causal Models To Counterfactual Structures》
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作者：
Joseph Y. Halpern
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最新提交年份：
2013
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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：
  Galles and Pearl claimed that "for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis's [possible-worlds] framework." This claim is examined carefully, with the goal of clarifying the exact relationship between causal models and Lewis's framework. Recursive models are shown to correspond precisely to a subclass of (possible-world) counterfactual structures. On the other hand, a slight generalization of recursive models, models where all equations have unique solutions, is shown to be incomparable in expressive power to counterfactual structures, despite the fact that the Galles and Pearl arguments should apply to them as well. The problem with the Galles and Pearl argument is identified: an axiom that they viewed as irrelevant, because it involved disjunction (which was not in their language), is not irrelevant at all.
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PDF链接：
https://arxiv.org/pdf/1106.2647