AIST（产业技术综合研究所）；PRESTO, JST（日本科学技术振兴机构 战略性研究推进项目）；Tokyo Institute of Technology（东京工业大学）；Kyoto University（京都大学）
Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga
备注：
Full version for the paper accepted at JELIA 2023 (the 18th European Conference on Logics in Artificial Intelligence)
发表信息：
Proc. of the 18th European Conference on Logics in Artificial Intelligence (JELIA 2023), Lecture Notes in Computer Science, Vol.14281, pp.681-696, 2023
We propose a formal language for describing and explaining statistical causality. Concretely, we define Statistical Causality Language (StaCL) for expressing causal effects and specifying the requirements for causal inference. StaCL incorporates modal operators for interventions to express causal properties between probability distributions in different possible worlds in a Kripke model. We formalize axioms for probability distributions, interventions, and causal predicates using StaCL formulas. These axioms are expressive enough to derive the rules of Pearl's do-calculus. Finally, we demonstrate by examples that StaCL can be used to specify and explain the correctness of statistical causal inference.
From Neural Networks to Logical Theories: The Correspondence between Fibring Modal Logics and Fibring Neural Networks
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Sony AI Barcelona（巴塞罗那索尼人工智能实验室）；Sony AI Tokyo（东京索尼人工智能实验室）；City St George’s, University of London（伦敦大学城圣乔治学院）
Revealing Multimodal Causality with Large Language Models
University of Technology Sydney（技术大学悉尼大学）；Tongji University（同济大学）；University of Melbourne（墨尔本大学）；University of Sydney（悉尼大学）；Macquarie University（麦考瑞大学）
How Vision Becomes Language: A Layer-wise Information-Theoretic Analysis of Multimodal Reasoning
Duke kunshan University, Duke University（杜克昆山大学、杜克大学）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学）；Fudan University, Shanghai, China（复旦大学）
