Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
Tulane University（杜兰大学）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；Mind Research Network（心智研究网络）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Aiying Zhang, Gemeng Zhang, Biao Cai, Wenxing Hu, Li Xiao, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang
Functional connectivity (FC) has become a primary means of understanding brain functions by identifying brain network interactions and, ultimately, how those interactions produce cognitions. A popular definition of FC is by statistical associations between measured brain regions. However, this could be problematic since the associations can only provide spatial connections but not causal interactions among regions of interests. Hence, it is necessary to study their causal relationship. Directed acyclic graph (DAG) models have been applied in recent FC studies but often encountered problems such as limited sample sizes and large number of variables (namely high-dimensional problems), which lead to both computational difficulty and convergence issues. As a result, the use of DAG models is problematic, where the identification of DAG models in general is nondeterministic polynomial time hard (NP-hard). To this end, we propose a $ψ$-learning incorporated linear non-Gaussian acyclic model ($ψ$-LiNGAM). We use the association model ($ψ$-learning) to facilitate causal inferences and the model works well especially for high-dimensional cases. Our simulation results demonstrate that the proposed method is more robust and accurate than several existing ones in detecting graph structure and direction. We then applied it to the resting state fMRI (rsfMRI) data obtained from the publicly available Philadelphia Neurodevelopmental Cohort (PNC) to study the cognitive variance, which includes 855 individuals aged 8-22 years. Therein, we have identified three types of hub structure: the in-hub, out-hub and sum-hub, which correspond to the centers of receiving, sending and relaying information, respectively. We also detected 16 most important pairs of causal flows. Several of the results have been verified to be biologically significant.
Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration
Nara Institute of Science and Technology（奈良科学技术研究所）；Ateneo de Manila University（雅典耀大学）
Causal Effect Identification in LiNGAM Models with Latent Confounders
Technical University of Munich（慕尼黑工业大学）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Leiden University（莱顿大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
Graduate School of Engineering Science Osaka University（大阪大学工学研究科）；Department of Statistics, Operations, and Data Science（统计学、运营与数据科学系）；Machine Learning Department（机器学习系）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·拉希德智能大学）
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）
Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学）；The Institute of Statistical Mathematics（统计数理研究所）；RIKEN Center for Advanced Intelligence Project (AIP)（日本理化学研究所先进智能研究中心）
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
Inria（法国国家信息与自动化技术研究院）；CEA（法国国家科学研究中心）；University of Paris-Saclay（巴黎-萨克勒大学）；University of Carnegie Mellon（卡内基梅隆大学）；Apple（苹果公司）；University of Helsinki（赫尔辛基大学）
Discovery of non-gaussian linear causal models using ICA
HIIT Basic Research Unit（赫尔辛基信息技术研究所基础研究单元）；University of Helsinki（赫尔辛基大学）；Osaka University（大阪大学）
近年来，已经提出了几种方法来从非实验数据中发现因果结构（Spirtes et al. 2000; Pearl 2000）。此类方法对数据生成过程做出各种假设，以便于从纯粹的观测数据中识别它。继续这条研究路线，我们展示了如何发现连续值数据的完整因果结构，假设（a）数据生成过程是线性的，（b）没有未观察到的混杂因素，（c）干扰变量具有非零方差的非高斯分布。该解决方案依赖于使用被称为独立成分分析（ICA）的统计方法，并且不需要任何预先指定的变量时间排序。我们提供了一个完整的Matlab软件包来执行这种LiNGAM分析（线性非高斯非循环模型的缩写），并使用人工生成的数据证明了该方法的有效性。
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-南洋理工大学人工智能联合研究中心）；Chongqing Uni. of Posts and Telecom.（重庆邮电大学）；MOE Key Laboratory of Big Data Intelligent Computing, Chongqing Uni. of Posts and Telecom.（重庆邮电大学大数据智能计算教育部重点实验室）
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients
Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
Kyoto University Graduate School of Medicine（京都大学医学研究科）；SANKEN The University of Osaka（SANKEN 大阪大学）；Faculty of Data Science Shiga University（Shiga大学数据科学学部）；AIP RIKEN；Faculty of Medicine, University of Miyazaki（Miyazaki大学医学部）；Hiroshima University Graduate School of Biomedical and Health Sciences（广岛大学生物医学和健康科学研究生院）
Causal Discovery in Structural VAR Models Under Equal Noise Variance
Bourns College of Engineering, University of California, Riverside（加州大学河滨分校工程学院）；University of California, San Francisco（加州大学旧金山分校）；Department of Mathematics and Statistics, Boston University（波士顿大学数学与统计学系）
