As an emerging concept cognitive learning model, partial order formal structure analysis (POFSA) has been widely used in the field of knowledge processing. In this paper, we propose the method named three-way causal attribute partial order structure (3WCAPOS) to evolve the POFSA from set coverage to causal coverage in order to increase the interpretability and classification performance of the model. First, the concept of causal factor (CF) is proposed to evaluate the causal correlation between attributes and decision attributes in the formal decision context. Then, combining CF with attribute partial order structure, the concept of causal attribute partial order structure is defined and makes set coverage evolve into causal coverage. Finally, combined with the idea of three-way decision, 3WCAPOS is formed, which makes the purity of nodes in the structure clearer and the changes between levels more obviously. In addition, the experiments are carried out from the classification ability and the interpretability of the structure through the six datasets. Through these experiments, it is concluded the accuracy of 3WCAPOS is improved by 1% - 9% compared with classification and regression tree, and more interpretable and the processing of knowledge is more reasonable compared with attribute partial order structure.
Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
Centre for AI, DS\&AI, Astrazeneca, UK（英国阿斯利康人工智能中心）；Institute for Imaging, Data and Communications (IDCOM), School of Engineering, University of Edinburgh, Edinburgh, UK（爱丁堡大学工程学院影像、数据与通信研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Tuning Derivatives for Causal Fairness in Machine Learning
Department of Statistics, Umeå School of Business, Economics and Statistics（统计系，乌梅亚商学院、经济学和统计学学院）；Integrated Science Lab, Department of Physics（整合科学实验室，物理系）
Multi-View Causal Representation Learning with Partial Observability
Institute of Science and Technology Austria（奥地利科学技术研究所）；Max Planck Institute for Intelligent Systems, Tübingen（图宾根马克斯·普朗克智能系统研究所）；University of Amsterdam（阿姆斯特丹大学）；Samsung - SAIT AI Lab（三星SAIT AI实验室）；Mila, Université de Montréal（蒙特利尔大学Mila研究所）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；ServiceNow Research（ServiceNow研究院）；University of Tübingen（图宾根大学）；University of Cambridge（剑桥大学）
Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference
Tsinghua University（清华大学）；University of Science and Technology Beijing（北京科技大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of Pittsburgh（匹兹堡大学）；University of North Carolina（北卡罗来纳大学）
Partial Identification under Causal Orders by Linear Programming
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（达勒·莫勒人工智能研究所）；Scuola Universitaria Professionale della Svizzera Italiana (SUPSI)（瑞士意大利语区应用科技大学）
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
Korteweg-de Vries Institute（科特韦格-德弗里斯研究所）；University of Amsterdam（阿姆斯特丹大学）；Institute for Computing and Information Sciences（计算与信息科学研究所）；Radboud University Nijmegen（拉德堡德大学奈梅亨分校）
Root Cause Analysis with Latent Confounders using Partial Ancestral Graphs
Signal Processing Laboratory, São Carlos School of Engineering, University of São Paulo（圣保罗大学圣卡洛斯工程学院信号处理实验室）；Guaratinguetá School of Engineering, São Paulo State University（圣保罗州立大学瓜拉廷加工程学院）
