Previous works on Treatment Effect Estimation (TEE) are not in widespread use because they are predominantly theoretical, where strong parametric assumptions are made but untractable for practical application. Recent work uses multilayer perceptron (MLP) for modeling casual relationships, however, MLPs lag far behind recent advances in ML methodology, which limits their applicability and generalizability. To extend beyond the single domain formulation and towards more realistic learning scenarios, we explore model design spaces beyond MLPs, i.e., transformer backbones, which provide flexibility where attention layers govern interactions among treatments and covariates to exploit structural similarities of potential outcomes for confounding control. Through careful model design, Transformers as Treatment Effect Estimators (TransTEE) is proposed. We show empirically that TransTEE can: (1) serve as a general purpose treatment effect estimator that significantly outperforms competitive baselines in a variety of challenging TEE problems (e.g., discrete, continuous, structured, or dosage-associated treatments) and is applicable to both when covariates are tabular and when they consist of structural data (e.g., texts, graphs); (2) yield multiple advantages: compatibility with propensity score modeling, parameter efficiency, robustness to continuous treatment value distribution shifts, explainable in covariate adjustment, and real-world utility in auditing pre-trained language models
Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
National Engineering Research Center for Big Data Technology；System Lab, Cluster；Grid Computing Lab, School of Computer Science；Technology, Huazhong University of Science；DIRO, Université de Montréal \& Mila \& Canada CIFAR AI Chair, Canada
Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning
Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University（教育部医学图像智能计算重点实验室）；College of Artificial Intelligence, Shandong Normal University（山东师范大学人工智能学院）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization（工业智能与系统优化国家级前沿科学中心）；AiShiWeiLai AI Research（AiShiWeiLai人工智能研究）；Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University（南京医科大学附属南京脑科医院精神科早期干预单元）；Amii, University of Alberta（阿尔伯塔大学Amii）
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices
University of Macau（澳门大学）；Zhejiang University of Technology（浙江工业大学）；Guangdong University of Technology（广东工业大学）；Nanyang Technological University（南洋理工大学）；University of Waterloo（滑铁卢大学）
