Spurred by tremendous success in pattern matching and prediction tasks, researchers increasingly resort to machine learning to aid original scientific discovery. Given large amounts of observational data about a system, can we uncover the rules that govern its evolution? Solving this task holds the great promise of fully understanding the causal interactions and being able to make reliable predictions about the system's behavior under interventions. We take a step towards answering this question for time-series data generated from systems of ordinary differential equations (ODEs). While the governing ODEs might not be identifiable from data alone, we show that combining simple regularization schemes with flexible neural ODEs can robustly recover the dynamics and causal structures from time-series data. Our results on a variety of (non)-linear first and second order systems as well as real data validate our method. We conclude by showing that we can also make accurate predictions under interventions on variables or the system itself.
Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations
Department of Computer Science, Columbia University, New York, U.S.（哥伦比亚大学计算机科学系）；Department of Industrial Engineering and Operations Research, Columbia University, New York, U.S.（哥伦比亚大学工业工程与运筹学系）；Department of Applied Mathematics and Applied Physics, Columbia University, New York, U.S.（哥伦比亚大学应用数学与应用物理系）；New York Genome Center, New York, U.S.（纽约基因组中心）；Irving Institute of Cancer Dynamics, New York, U.S.（伊万·罗伯特癌症动力学研究所）；Institute of Computational Biology, Helmholtz Munich, Munich, Germany（海德堡医学院计算生物学研究所）；Department of Mathematics, Technische Universität München, Munich, Germany（慕尼黑技术大学数学系）
Identification Methods With Arbitrary Interventional Distributions as Inputs
Johns Hopkins University（约翰斯·霍普金斯大学）
因果推断通过估计数据中的反事实参数来量化因果关系。这需要使用\emph{identification theory}在感兴趣的反事实参数和数据可用的分布之间建立联系。一系列工作的特点是非参数识别的各种因果参数的\emph{observed data distribution}。最近，识别结果已扩展到设置中的实验数据从干预分布也是可用的。在本文中，我们使用单世界干预图和嵌套因式分解的模型与混合图给出了一个非常简单的视图，现有的识别理论的实验数据。我们使用这种观点来产生一般的识别算法的设置，其中输入分布包括一组任意的观测和实验分布，包括边际和条件分布。我们表明，对于问题的输入是干预边缘分布的某种类型（祖先边缘），我们的算法是完整的。
Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling
Department of Computer and Information Science and Engineering, University of Florida（佛罗里达大学计算机与信息科学与工程系）；Regenstrief Institute（Regenstrief研究所）；Department of Biostatistics and Health Data Science, Indiana University（印第安纳大学生物统计学与健康数据科学系）；Department of Pharmacy Practice, Purdue University（普渡大学药学实践系）
Robust Moment Identification for Nonlinear PDEs via a Neural ODE Approach
Department of Mathematics and Statistics, University of Massachusetts Amherst（数学与统计学系，马萨诸塞大学阿默斯特分校）；School of Mathematics, Georgia Institute of Technology（数学学院，佐治亚理工学院）
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；National Key Laboratory of Space Integrated Information System（空间信息集成系统国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
Department of Industrial Systems Engineering and Management, National University of Singapore（工业系统工程与管理系，新加坡国立大学）；Department of Civil, Environmental and Geomatic Engineering, ETH Zürich（土木、环境与测绘工程系，苏黎世联邦理工学院）；Future Resilient Systems, Singapore-ETH Centre（未来韧性系统，新加坡-苏黎世中心）
Large-Scale Bayesian Causal Discovery with Interventional Data
Department of Computer and Information Science, University of Pennsylvania（计算机与信息科学系，宾夕法尼亚大学）；Genomics and Computational Biology Graduate Group, University of Pennsylvania（基因组与计算生物学研究生组，宾夕法尼亚大学）；Division of Informatics, University of Pennsylvania（信息学 division，宾夕法尼亚大学）；Department of Genetics, University of Pennsylvania（遗传学系，宾夕法尼亚大学）
Massachusetts Institute of Technology（麻省理工学院）；Julia Computing Inc.（朱莉娅计算公司）；Pumas AI（普马斯人工智能公司）；University of New South Wales at Canberra（堪培拉新南威尔士大学）；National Autonomous University of Mexico（墨西哥国立自治大学）；Pramati Technologies（普拉马蒂科技公司）；University of Maryland Baltimore（马里兰大学巴尔的摩分校）
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Cornell University（康奈尔大学）；ANSYS Inc.（ANSYS公司）；Korea Institute for Advanced Study（韩国高等研究院）；University of Arizona（亚利桑那大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；North Carolina State University（北卡罗来纳州立大学）；Pohang University of Science and Technology（浦项科技大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Learning Neural Causal Models with Active Interventions
ETH Zurich（苏黎世联邦理工学院）；Mila（米拉人工智能研究所）；Universite de Montréal（蒙特利尔大学）；GlaxoSmithKline（葛兰素史克）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Google Research（谷歌研究院）；KTH Stockholm（瑞典皇家理工学院）；DeepMind（深度思维）
Relaxing Faithfulness with Intervention-Only Causal Discovery
Thayer School of Engineering Dartmouth College（达特茅斯学院塞耶工程学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）；Massachusetts Institute of Technology（麻省理工学院）
Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States
Department of Mechanical & Industrial Engineering, Montana State University（机械与工业工程系，蒙大拿州立大学）；Gianforte School of Computing, Montana State University（计算机学院，蒙大拿州立大学）
Adjustment Identification Distance: A gadjid for Causal Structure Learning
School of Mathematics and Statistics, University College Dublin（都柏林大学学院数学与统计学院）；Pioneer Centre for AI, University of Copenhagen（哥本哈根大学人工智能先锋中心）；Department of Mathematical Sciences, University of Copenhagen（哥本哈根大学数学科学系）
Learning Domain Invariant Representations for Generalizable Person Re-Identification
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of Chinese Academy of Sciences（中国科学院大学）
