Causal discovery, the task of inferring causal structure from data, has the potential to uncover mechanistic insights from biological experiments, especially those involving perturbations. However, causal discovery algorithms over larger sets of variables tend to be brittle against misspecification or when data are limited. For example, single-cell transcriptomics measures thousands of genes, but the nature of their relationships is not known, and there may be as few as tens of cells per intervention setting. To mitigate these challenges, we propose a foundation model-inspired approach: a supervised model trained on large-scale, synthetic data to predict causal graphs from summary statistics -- like the outputs of classical causal discovery algorithms run over subsets of variables and other statistical hints like inverse covariance. Our approach is enabled by the observation that typical errors in the outputs of a discovery algorithm remain comparable across datasets. Theoretically, we show that the model architecture is well-specified, in the sense that it can recover a causal graph consistent with graphs over subsets. Empirically, we train the model to be robust to misspecification and distribution shift using diverse datasets. Experiments on biological and synthetic data confirm that this model generalizes well beyond its training set, runs on graphs with hundreds of variables in seconds, and can be easily adapted to different underlying data assumptions.
Causally-interpretable meta-analysis using aggregate data
University of Groningen（格罗宁根大学）；University Medical Center Utrecht（乌得勒支大学医学中心）；University Medical Center Groningen（格罗宁根大学医学中心）；Groningen Research Institute of Pharmacy（格罗宁根药学研究所）；Dutch National Institute of Public Health and the Environment (RIVM)（荷兰国家公共卫生与环境研究所（RIVM））；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
The University of Melbourne（墨尔本大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Peking University（北京大学）；Adelaide University（阿德莱德大学）；Hong Kong Baptist University（香港浸会大学）；The University of Sydney（悉尼大学）；Carnegie Mellon University（卡内基梅隆大学）
Stable Causal Discovery via Directed Acyclic Graph Aggregation
Yau Mathematical Sciences Center, Tsinghua University（丘成桐数学科学中心，清华大学）；Department of Statistics, University of Virginia（弗吉尼亚大学统计系）；Dr. Bing Zhang Department of Statistics, University of Kentucky（肯塔基大学张炳章统计系）
Scalable Contrastive Causal Discovery under Unknown Soft Interventions
Department of Systems Biology, Columbia University Irving Institute for Cancer Dynamics, Columbia University（系统生物学系，哥伦比亚大学伊万尼癌症动力学研究所，哥伦比亚大学）；Irving Institute for Cancer Dynamics, Columbia University（伊万尼癌症动力学研究所，哥伦比亚大学）；Department of Computer Science, Columbia University（计算机科学系，哥伦比亚大学）；Department of Biomedical Engineering, Columbia University（生物医学工程系，哥伦比亚大学）；Data Science Institute, Columbia University（数据科学研究所，哥伦比亚大学）
Feedzai（Feedzai公司）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；DCC, Faculdade de Ciêncidas da Universidade do Porto（波尔图大学理学院数据与计算机科学系）；Instituto de Telecomunicações（电信研究所）
Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants
School of Computer Science, Guangdong University of Technology（广东技术大学计算机科学学院）；Peng Cheng Laboratory, Shenzhen, China（深圳鹏城实验室）；College of Science, Shantou University（汕头大学理学院）；Department of Philosophy, Carnegie Mellon University（卡内基梅隆大学哲学系）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Institute for Systems and Robotics, Instituto Superior Técnico（系统与机器人研究所，里斯本高等理工学院）；Digital Scholarship at Oxford, University of Oxford（牛津大学数字学术中心）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
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。
Resilient Concurrent Causal Discovery for Topological Event Sequences
School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Computer Science and Artificial Intelligence, Liaoning Normal University（辽宁师范大学计算机科学与人工智能学院）；University of Natural Resources and Life Sciences (BOKU)（自然资源与生命科学大学（BOKU））；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）；College of Computing & Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）；Empirical Inference, MPI for Intelligent Systems（马克斯·普朗克智能系统研究所经验推断部）；Machine Learning Group, University of Cambridge（剑桥大学机器学习组）；Max Planck ETH Center for Learning Systems（马克斯·普朗克-苏黎世联邦理工学院学习系统中心）
我们提出了一个域一般帐户的因果关系，适用于设置在两个系统之间的宏观层次的因果关系是感兴趣的，但相关的因果关系的功能知之甚少，必须从大量的微观测量聚合。我们的方法将Chalupka et al.（2015）的方法推广到宏观水平效应未指定的环境中。我们正式的微观和宏观变量之间的连接，在这种情况下，并提供了一个连贯的框架，描述因果关系在多个层次的分析。我们提出了一种算法，发现宏观变量的原因和影响，从实验中获得的微观测量。我们进一步展示了如何设计实验，从观测的微观变量数据中发现宏观变量。最后，我们表明，在特定条件下，可以识别多层次的因果结构。在本文中，我们使用模拟神经科学多单元记录实验来说明这些想法和算法。
Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models
Data Science and its Applications, German Research Centre for Artificial Intelligence (DFKI)（德国人工智能研究中心数据科学与应用部门）；Dept. of Computer Science, University of Kaiserslautern–Landau (RPTU)（科隆-兰道大学计算机科学系）；Digital Health - Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering（哈索·普朗纳研究所数字工程学院数字健康-机器学习研究组）；Institute of Informatics, University of Munich (LMU)（慕尼黑大学信息学院）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山医学院哈索·普朗纳研究所数字健康中心）；Munich Center for Machine Learning (MCML), Germany（慕尼黑机器学习中心）
Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
Department of Statistics, University of Washington, Seattle, WA, USA（华盛顿大学统计学系）；Department of Health Care Policy, Harvard Medical School, Boston, MA, USA（哈佛医学院医疗政策系）
Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models
SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoning
Jimei University（集美大学）；Hebei University of Technology（河北工业大学）；R. M. K. Engineering College（R.M.K.工程学院）；University of Science and Technology of China（中国科学技术大学）；Waseda University（早稻田大学）；University of Luxembourg（卢森堡大学）
Federated Causal Inference in Heterogeneous Observational Data
Emory University（埃默里大学）；Cornell University（康奈尔大学）；United States Military Academy（美国军事学院）；Harvard University（哈佛大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Stanford University（斯坦福大学）
