Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood. In this paper we provide a novel perspective on the relationship between Ordinary Differential Equations and Structural Causal Models. We show how, under certain conditions, the asymptotic behaviour of an Ordinary Differential Equation under non-constant interventions can be modelled using Dynamic Structural Causal Models. In contrast to earlier work, we study not only the effect of interventions on equilibrium states; rather, we model asymptotic behaviour that is dynamic under interventions that vary in time, and include as a special case the study of static equilibria.
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）
Factored space models: Towards causality between levels of abstraction
Machine Intelligence Research Institute（机器智能研究所）；University of Amsterdam（阿姆斯特丹大学）；Goethe University Frankfurt（法兰克福大学）；IT University of Copenhagen（哥本哈根信息技术大学）
Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
Department of Mechanical & Industrial Engineering（机械与工业工程系）；Northeastern University（东北大学）；Department of Industrial Engineering and Management Sciences（工业工程与管理科学系）；Department of Mechanical Engineering（机械工程系）；Northwestern University（西北大学）
