Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.
Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Computer and Information, Hefei University of Technology（合肥工业大学计算机与信息学院）；UniSA STEM, University of South Australia（南澳大学UniSA STEM）
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；University of South Australia（澳大利亚南岸大学）；School of Hong Kong Polytechnic University, Department of Computing（香港理工大学计算机学院）；School of Computer Science and Information Technology, Hefei University of Technology（合肥工业大学计算机科学与信息技术学院）
针对基于约束方法的级联错误及全局评分方法受局部等价类影响的问题，本文提出 HLCD 混合算法，结合 OR 规则候选骨架、评分剪枝与结构评分定向，并在 14 个数据集上验证其优于 7 种现有方法。
Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
Department of Applied Statistics, Beijing Technology and Business University（北京技术与商业大学应用统计系）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学机器学习系）；Carnegie Mellon University（卡内基梅隆大学）
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
School of Software Engineering, Beijing Jiaotong University（北京交通大学软件学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）
Coarse-to-Fine Learning of Dynamic Causal Structures
School of Software（软件学院）；Shandong University（山东大学）；Department of Computer Science（计算机科学系）；New York University（纽约大学）；George Mason University（乔治·马歇尔大学）；Shandong Institutes of Industrial Technology（山东省工业技术研究院）
Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems
Ludwig-Maximilians-Universität München（慕尼黑大学）；Institut für Theoretische Physik, Universität zu Köln（科隆大学理论物理研究所）；Institut für KI-Sicherheit, Deutsches Zentrum für Luft- und Raumfahrt (DLR)（德国航空航天中心人工智能安全研究所）；Entrox Systems（恩特罗克斯系统公司）；Institut für Frontier Materials auf der Erde und im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR)（德国航空航天中心地球与太空前沿材料研究所）
