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）
Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the single-domain data only. In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for Latent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results. The model enriches the causal representation for multi-domain data. We propose an integrated two-phase algorithm to estimate the model. In particular, we first locate the latent factors and estimate the factor loading matrix. Then to uncover the causal structure among shared latent factors of interest, we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multi-domain latent factors and latent factors of interest. We show that the proposed method provides locally consistent estimators. Experimental results on both synthetic and real-world data demonstrate the efficacy and robustness of our approach.
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）
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）
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）
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）
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）
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。
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）
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）
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）
Causal Effect Inference with Deep Latent-Variable Models
University of Amsterdam（阿姆斯特丹大学）；TNO Intelligent Imaging（荷兰应用科学研究组织智能成像）；New York University（纽约大学）；CIMS（库朗数学科学研究所）；Massachusetts Institute of Technology（麻省理工学院）；CSAIL & IMES（计算机科学与人工智能实验室与医学工程与科学研究所）；University of Toronto（多伦多大学）；CIFAR（加拿大高等研究院）
Identification of Causal Direction under an Arbitrary Number of Latent Confounders
School of Computer Science, Guangdong University of Technology（广东技术大学计算机科学学院）；Department of Philosophy, Carnegie Mellon University（卡内基梅隆大学哲学系）；College of Engineering, Shantou University（汕头大学工程学院）；Peng Cheng Laboratory, Shenzhen, China（深圳鹏城实验室）；Mohamed bin Zayed University of Artificial Intelligence（莫扎德大学人工智能大学）
Identification of Nonlinear Latent Hierarchical Models
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加州大学圣迭戈分校）；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）
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients
A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（深圳先进技术研究院，中国科学院，中国深圳）；College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China（复旦大学计算机科学与人工智能学院，中国上海）
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）
