Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DCILP. In the divide phase, the Markov blanket MB($X_i$) of each variable $X_i$ is identified, and causal learning subproblems associated with each MB($X_i$) are independently addressed in parallel. This approach benefits from a more favorable ratio between the number of data samples and the number of variables considered. In counterpart, it can be adversely affected by the presence of hidden confounders, as variables external to MB($X_i$) might influence those within it. The reconciliation of the local causal graphs generated during the divide phase is a challenging combinatorial optimization problem, especially in large-scale applications. The main novelty of DCILP is an original formulation of this reconciliation as an integer linear programming (ILP) problem, which can be delegated and efficiently handled by an ILP solver. Through experiments on medium to large scale graphs, and comparisons with state-of-the-art methods, DCILP demonstrates significant improvements in terms of computational complexity, while preserving the learning accuracy on real-world problem and suffering at most a slight loss of accuracy on synthetic problems.
McGill University（麦吉尔大学）；ETH Zürich（苏黎世联邦理工学院）；GlaxoSmithKline（葛兰素史克）；TU Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能研究所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Causal Discovery Should Embrace the Wisdom of the Crowd
Department of Industrial and Systems Engineering, University of Washington（华盛顿大学工业与系统工程系）；Department of Applied Mathematics, Illinois Institute of Technology（伊利诺伊理工学院应用数学系）；Department of Electrical & Computer Engineering, Texas A&M University（德克萨斯阿灵顿大学电气与计算机工程系）
Do Finetti: On Causal Effects for Exchangeable Data
Toyota Research Institute（丰田研究所）；Oregon State University（俄勒冈州立大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Cambridge（剑桥大学）
Disentangle Estimation of Causal Effects from Cross-Silo Data
University of Electronic Science and Technology of China（电子科技大学）；Huazhong University of Science and Technology（华中科技大学）；Hong Kong Polytechnic University（香港理工大学）；Nuowei Technology（诺威科技）；Inner Mongolia Normal University（内蒙古师范大学）
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Cambridge（剑桥大学）；ELLIS Institute Tübingen（埃利斯研究所蒂宾根分所）；Tübingen AI Center（蒂宾根人工智能中心）
Department of Chemistry, Technical University of Denmark（丹麦技术大学化学系）；Departments of Statistics and Computer Science, Columbia University（哥伦比亚大学统计学与计算机科学系）
Department of Technology, Management and Economics, Danish Technical University（丹麦技术大学技术、管理与经济学系）；Department of Mathematics and Statistics, Binghamton University, State University of New York（纽约州立大学宾汉姆顿分校数学与统计系）；Department of Mathematical Sciences, University of Copenhagen（哥本哈根大学数学科学系）
Causal Discovery on Dependent Mixed Data with Applications to Gene Regulatory Network Inference
Department, University or Company Name [Additional affiliations should be put in the Acknowledgments section] e1；Department, University or Company Name [Additional affiliations should be put in the Acknowledgments section] e2,e3；Department of Statistics；Data Science, University of California, Los Angeles e2；Data Science, University of California, Los Angeles
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）
A Distributed Framework for Causal Modeling of Performance Variability in GPU Traces
Texas State University（德克萨斯州立大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Argonne National Laboratory（阿贡国家实验室）；Sandia National Laboratories（桑迪亚国家实验室）
FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings
Owkin, Inc.（Owkin公司）；Georges Pompidou European Hospital（乔治·蓬皮杜欧洲医院）；AP-HP（巴黎公立医院集团）；Centre de Recherche des Cordeliers（科尔得利研究中心）；Inserm（法国国家健康与医学研究院）；Institut du Cancer Paris CARPEM（巴黎CARPEM癌症研究所）；AP-HP Centre（巴黎公立医院集团中心）；Sorbonne University（索邦大学）；Centre de Recherche sur l’Inflammation (CRI)（炎症研究中心）；CNRS（法国国家科学研究中心）；Beaujon Hospital（博容医院）；Catalan Institute of Oncology（加泰罗尼亚肿瘤研究所）；Doctor Josep Trueta University Hospital（约瑟普·特鲁埃塔大学医院）；Pancreatic Cancer Action Network（胰腺癌行动网络）
Federated Causality Learning with Explainable Adaptive Optimization
School of Software, Shandong University（山东大学软件学院）；SDU-NTU Joint Centre for AI Research, Shandong University（山东大学-新加坡南洋理工大学人工智能联合研究中心）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）；Departiment of Computer Science, George Mason University（乔治梅森大学计算机系）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
针对隐私保护下分散异构数据的因果发现问题，提出 FedCausal 联邦策略，通过可解释的自适应优化统一局部与全局 DAG 学习，在 non-iid 合成及真实数据上表现优越。
Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI)（达勒莫勒人工智能研究所（IDSIA USI-SUPSI））；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
