In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization. REASON consists of Topological Causal Discovery and Individual Causal Discovery. The Topological Causal Discovery component aims to model the fault propagation in order to trace back to the root causes. To achieve this, we propose novel hierarchical graph neural networks to construct interdependent causal networks by modeling both intra-level and inter-level non-linear causal relations. Based on the learned interdependent causal networks, we then leverage random walks with restarts to model the network propagation of a system fault. The Individual Causal Discovery component focuses on capturing abrupt change patterns of a single system entity. This component examines the temporal patterns of each entity's metric data (i.e., time series), and estimates its likelihood of being a root cause based on the Extreme Value theory. Combining the topological and individual causal scores, the top K system entities are identified as root causes. Extensive experiments on three real-world datasets with case studies demonstrate the effectiveness and superiority of the proposed framework.
Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks for Explainable Depression Identification
MOE Frontier Science Center for Brain Science and Brain-machine Integration（脑科学与脑机融合前沿科学中心）；Nanhu Brain-computer Interface Institute（南湖脑机接口研究院）；School of Computer Science and Technology（计算机科学与技术学院）；Qiushi Academy for Advanced Studies（启硕高级研究院）；State Key Laboratory of Brain-machine Intelligence（脑机智能国家重点实验室）；The Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University Hangzhou, China（浙江大学医学院附属第二医院神经外科）
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
The Hong Kong Polytechnic University（香港理工大学）；Sichuan University（四川大学）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong Baptist University（香港浸会大学）；NEOMA Business School（诺欧商学院）
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Carnegie Mellon University（卡内基梅隆大学）；Beijing Technology and Business University（北京工商大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Differentiable Causal Discovery For Latent Hierarchical Causal Models
University of California San Diego（加利福尼亚大学圣迭戈分校）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Cambricon Technologies（寒武纪科技）；Southern University of Science and Technology（南方科技大学）；Institute of Software, CAS（中国科学院软件研究所）；University of Science and Technology of China（中国科学技术大学）
D3HRL: A Distributed Hierarchical Reinforcement Learning Approach Based on Causal Discovery and Spurious Correlation Detection
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；Intelligent Game and Decision Lab (IGDL)（智能游戏与决策实验室）；College of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Electronic information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
System Dynamics, Chemnitz University of Technology, Chemnitz, Germany（化学特伦茨大学系统动力学系）；Department of Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology（弗劳恩霍夫分子生物学与应用生态学研究所生物资源系）
Identification of Nonlinear Latent Hierarchical Models
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加州大学圣迭戈分校）；Beijing Technology and Business University（北京工商大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
The University of Melbourne（墨尔本大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Peking University（北京大学）；Adelaide University（阿德莱德大学）；Hong Kong Baptist University（香港浸会大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
Graduate School of Interdisciplinary Science and Engineering in Health Systems（交叉学科科学与工程健康系统研究生院）；Toshiba Corporation（东芝公司）；System AI Laboratory（系统人工智能实验室）；Faculty of Data Science（数据科学学院）
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；Intelligent Game and Decision Lab (IGDL)（智能游戏与决策实验室）；Institute of Military Transportation（军事交通运输研究院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Tensor Vision Technologies（张量视觉技术公司）
视觉对象是由一个递归层次的知觉整体和部分，其属性，如形状，反射率和颜色，构成了一个层次的内在因果因素的对象外观。然而，物体外观是物体的内在和外在因果因素的组成结果，其中外在因果因素与照明和成像条件有关。因此，本文提出了一个统一的张量模型的整体和部分，并介绍了一个组成层次张量分解，解开层次因果结构的对象图像形成，并纳入多线性块张量分解作为一个特殊情况。所得到的对象表示是整体和部分表示的可解释的组合选择，其使得对象识别对遮挡鲁棒并且减少训练数据要求。我们通过在一个极其简化的合成图像数据集上进行训练，在人脸识别的背景下演示了我们的方法，并在两个数据集上报告了人脸验证结果-Freiburg数据集和由真实世界图像组成的Labeled Face in the Wild（LFW）数据集，从而证实了我们的方法适用于数据匮乏的领域。
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Guangdong University of Technology（广东技术大学）；University of California San Diego（加州大学圣地亚哥分校）
University of Dublin, Trinity College（都柏林大学，三一学院）；IBM；Gatsby Computational Neuroscience Unit, University College London（大学学院伦敦的加布里埃尔计算神经科学单位）；Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Saarbrücken, Germany（德国萨尔布吕肯德意志人工智能研究中心（DFKI））；Department of Philosophy, University of Bergen（卑尔根大学哲学系）
Hierarchical Reinforcement Learning with Targeted Causal Interventions
School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland（计算机与通信科学系，瑞士洛桑联邦理工学院）；College of Management of Technology, EPFL, Lausanne, Switzerland（技术管理学院，瑞士洛桑联邦理工学院）；Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands（莱顿高级计算机科学研究所（LIACS），莱顿大学）
Learning Discrete Concepts in Latent Hierarchical Models
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of California San Diego（加州大学圣地亚哥分校）
Root Cause Analysis of Outliers with Missing Structural Knowledge
University of Cambridge（剑桥大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Amazon Tübingen（亚马逊图宾根）
Analysis of Public Schools Educational Performance Based on Causal Models and Hierarchical Clustering
Labcin - Nature Inspired Computing Lab, Federal University of Esp\'irito Santo, Vit\'oria, Brazil PPGI - Graduate Program in Computer Science, Federal University of Esp\'irito Santo, Vit\'oria, Brazil
CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems
NEC Laboratories America（NEC美国实验室）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Computing Technologies, RMIT University（皇家墨尔本理工学院计算技术学院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯威本技术大学科学、计算与工程技术学院）
Department of Technology, Management and Economics, Danish Technical University（丹麦技术大学技术、管理与经济系）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
From Baseline to Follow-Up: Counterfactual Spine DXA Image Synthesis in UK Biobank Using a Causal Hierarchical Variational Autoencoder
School of Electronics and Computer Science（电子与计算机科学学院）；University of Southampton（萨塞克斯大学）；MRC Lifecourse Epidemiology Centre（英国医学研究理事会生命周期流行病学中心）；University of Southampton, Southampton General Hospital（萨塞克斯大学索马塞特医院）；Computer Science University of Southampton（计算机科学萨塞克斯大学）
