亨佩尔统计歧义问题的解决方案与因果人工智能
Solution of the Hempel's statistical ambiguity problem and Causal AI
浏览论文内容
中文总结 AI 辅助
研究亨佩尔统计歧义问题，采用卡特赖特因果定义，引入因果规则概念，定义语义概率推理程序得出最大特异性因果关系，证明其预测一致，解决了该问题，提供概率因果学习系统用于新领域。
中文摘要 AI 辅助
本文探讨卡尔·亨佩尔在归纳统计推理中存在的长期统计歧义问题，即从统计定律得出相互矛盾的预测。为避免此类预测，亨佩尔提出推理中使用的统计定律的最大特异性要求（RMS）。对韦斯利·萨蒙、阿尔韦托·科法和詹姆斯·费策尔对RMS的改进分析后，得出了最大特异性统计定律的定义。但基于此定义的统计歧义问题尚无解决方案证明。我们采用南希·卡特赖特关于在背景情境中提高概率的原因定义，引入因果规则概念，定义了一种特殊的语义概率推理程序，通过纳入所有统计相关信息逐步完善这些因果规则，得出最大特异性因果关系（MSCRs），并证明其预测一致，解决了统计歧义问题。该语义概率推理程序提供了一个概率因果学习系统，可用于因果人工智能和因果机器学习等新领域，从根本上探索因果推理作为理解复杂系统中因果关系的工具。与RMS类似的性质仍在讨论中，还考虑了几个与RMS相关的概念：不变特征学习、不变因果预测和虚假关联。
英文摘要
This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws. To avoid such predictions, Carl Hempel proposed the Requirement of Maximal Specificity (RMS) for the statistical laws used in the inference. An analysis of the RMS refinements made by Wesley Salmon, Alberto Coffa, and James Fetzer led to the following definition of maximally specific statistical laws: "the lawlike premises of an adequate explanation must specify all and only those properties whose presence or absence made a difference to the occurrence of its explanandum-phenomenon." However, there was no proof of a solution to the statistical ambiguity problem based on this definition. We use Nancy Cartwright's definition of causes that raise probabilities across background contexts, and then introduce the concept of Causal Rules. Then we define a special semantic probabilistic inference procedure that incrementally refines these causal rules by incorporating all statistically relevant information. This procedure yields Maximally Specific Causal Relationships (MSCRs), for which we prove (Theorem 1) that predictions derived from them are consistent. This resolves the statistical ambiguity problem. The semantic probabilistic inference procedure provides a probabilistic causal learning system, which may be used in such new areas as Causal AI and Causal Machine Learning. They fundamentally explore causal inference as a tool for understanding cause-and-effect relationships within complex systems. Properties similar to RMS remain under discussion. Several notions related to RMS are considered: invariant feature learning, invariant causal prediction, and spurious association.
发表机构
- Sobolev Institute of Mathematics of the SB RAS（俄罗斯科学院西伯利亚分院索伯列夫数学研究所）
机构由 AI 辅助整理，请以论文原文为准。
