CUTS: Neural Causal Discovery from Irregular Time-Series Data
Tsinghua University（清华大学）；Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS)（清华大学脑与认知科学研究所）；Chinese PLA General Hospital（中国人民解放军总医院）
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios. To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register unstructured data which might be of high dimension and with complex distribution; (ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty. Experiments show that CUTS effectively infers causal graphs from unstructured time-series data, with significantly superior performance to existing methods. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations.
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Tsinghua University（清华大学）；Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS)（清华大学脑与认知科学研究院（THUIBCS））；Chinese PLA General Hospital（中国人民解放军总医院）
Feedzai（Feedzai公司）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；DCC, Faculdade de Ciêncidas da Universidade do Porto（波尔图大学理学院数据与计算机科学系）；Instituto de Telecomunicações（电信研究所）
Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Academy of Life and Natural Sciences, Xi’an Jiaotong-Liverpool University（西交利物浦大学生命与自然科学学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息研究院）
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Institute for Systems and Robotics, Instituto Superior Técnico（系统与机器人研究所，里斯本高等理工学院）；Digital Scholarship at Oxford, University of Oxford（牛津大学数字学术中心）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions
Digital Built Environment Group (DiBEG)（数字建筑环境集团）；Edinburgh Napier University（爱丁堡纳皮尔大学）；School of Computing, Engineering and the Built Environment (SCEBE)（计算、工程与建筑环境学院）；York School of Architecture（约克建筑学院）；University of York（约克大学）；Department of Civil, Environmental, and Architectural Engineering（土木、环境与建筑工程系）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Toward Temporal Causal Representation Learning with Tensor Decomposition
Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA（机械与工业工程系，东北大学）；Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA（工业与系统工程系，莱斯大学）；Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA（工业与系统工程系，佛罗里达大学）
Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Nephrology, TEDA Hospital, Tianjin University（天津大学TEDA医院肾内科）；Tianjin Medical University General Hospital（天津医科大学总医院）；Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine（天津中医药大学第一教学医院肾内科）
ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
School of Data Science and Engineering, East China Normal University, Shanghai, China（东华大学数据科学与工程学院，上海，中国）；Engineering Research Center of Blockchain Data Management, Ministry of Education, China（教育部区块链数据管理工程研究中心，中国）
Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
HES-SO Genève（日内瓦高等教育学院）；University of Manchester（曼彻斯特大学）；Idiap Research Institute（伊迪亚普研究所）；NBC, CRUK Manchester Institute（英国癌症研究中心曼彻斯特研究所（NBC））；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；IBM Research（IBM研究院）
National University of Singapore（新加坡国立大学）；Imperial College London（帝国理工学院伦敦分校）；Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore（新加坡国立大学数学系、量化金融中心、风险管理研究所）
Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping
Electrical and Computer Engineering, Northwestern University（西北大学电气与计算机工程系）；Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University（跨学科天体物理研究中心（CIERA），西北大学）；NSF-Simons AI Institute for the Sky (SkAI)（国家科学基金会-模拟人工智能天文研究所（SkAI））；Department of Physics and Astronomy, Northwestern University（西北大学物理与天文学系）；Department of Physics, University of Florida（佛罗里达大学物理系）；Institute for Fundamental Theory（基础理论研究所）；Gravitational Wave Science Center (GWSC), Université de Genève（引力波科学中心（GWSC），日内瓦大学）
TimeROME-DLM: Temporal Causal Tracing and Low-Rank Inference-Time Knowledge Editing for Masked Diffusion Language Models
Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）
Universal Time Series Generation with Neural Controlled Differential Equations
Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；IAR, Karlsruhe Institute of Technology（卡尔斯鲁厄技术大学IAR部门）；Max Planck Institute for Polymer Research（马克斯·普朗克聚合物研究所）；IWR, Heidelberg University（海德堡大学IWR部门）；Dept. of Computer Science, University of Toronto（多伦多大学计算机科学系）；Mathematical Institute, University of Oxford（牛津大学数学研究所）；Vector Institute, Toronto, Canada（多伦多向量研究所）
