The problem of finding an ancestral acyclic directed mixed graph (ADMG) that represents the causal relationships between a set of variables is an important area of research on causal inference. Most existing score-based structure learning methods focus on learning directed acyclic graph (DAG) models without latent variables. A number of score-based methods have recently been proposed for the ADMG learning, yet they are heuristic in nature and do not guarantee an optimal solution. We propose a novel exact score-based method that solves an integer programming (IP) formulation and returns a score-maximizing ancestral ADMG for a set of continuous variables that follow a multivariate Gaussian distribution. We generalize the state-of-the-art IP model for DAG learning problems and derive new classes of valid inequalities to formulate an IP model for ADMG learning. Empirically, our model can be solved efficiently for medium-sized problems and achieves better accuracy than state-of-the-art score-based methods as well as benchmark constraint-based methods.
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）
Identifiable Latent Polynomial Causal Models Through the Lens of Change
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；School of Computer Science and Engineering, The University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Halicioğlu Data Science Institute (HDSI), University of California San Diego（加州大学圣地亚哥分校哈利西奥格鲁数据科学研究所）；Carnegie Mellon University（卡内基梅隆大学）
LNML-based Causal Discovery with Latent Confounders: Theory and Experiments
Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；Graduate School of Arts and Sciences, The University of Tokyo（东京大学文理研究生院）
Responsible AI Research Centre（负责任人工智能研究中心）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；School of Computer Science and Engineering（计算机科学与工程学院）；School of Mathematics and Statistics（数学与统计学学院）；Halicioğlu Data Science Institute（Halicioğlu数据科学研究所）；Department of Philosophy（哲学系）
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Guangdong University of Technology（广东工业大学）；RIKEN（理化学研究所）；Shiga University（滋贺大学）；Pazhou Lab（琶洲实验室）；Peking University（北京大学）；Okayama University（冈山大学）；Foshan University（佛山科学技术学院）
Score-Based Causal Discovery of Latent Variable Causal Models
Carnegie Mellon University（卡内基梅隆大学）；University of California, San Diego（加州大学圣地亚哥分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
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）
Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
School of Computer Science, Guangdong University of Technology（广东工业大学计算机科学学院）；Peng Cheng Laboratory（鹏城实验室）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；DiDi China Ride Hailing Business Group（滴滴出行（中国）有限公司）
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
School of Computer Science, Guangdong University of Technology（广东工业大学计算机科学学院）；Peng Cheng Laboratory（鹏城实验室）；Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学机器学习系）；College of Mathematics and Computer Science, Shantou University（汕头大学数学与计算机科学学院）
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Sharif University of Technology（谢里夫理工大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）
On the Identification of Temporally Causal Representation with Instantaneous Dependence
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）；The University of Melbourne（墨尔本大学）
Time Series Domain Adaptation via Latent Invariant Causal Mechanism
School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computing, Guangdong University of Technology（广东工业大学计算机学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究所）；Centre for Frontier AI Research, A*STAR（新加坡科技研究局前沿人工智能研究中心）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data
Department of Biomedical Informatics Vanderbilt University Medical Center（生物医学信息学系范德堡大学医学中心）；Department of Biomedical Informatics University of Pittsburgh（生物医学信息学系匹兹堡大学）；Departments of Medicine & Biomedical Informatics Vanderbilt University Medical Center（医学与生物医学信息学系范德堡大学医学中心）；Departments of Biomedical Informatics & Computer Science Vanderbilt University Medical Center & Vanderbilt University（生物医学信息学与计算机科学系范德堡大学医学中心及范德堡大学）
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
Guangdong University of Technology（广东工业大学）；Peng Cheng Laboratory（鹏城实验室）；Shantou University（汕头大学）；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）
