Causal Structure Learning

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19 papers in the last four weeks, up 90% on the four weeks before. 0.1% of all new papers.

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Simulation-free Structure Learning for Stochastic Population Dynamics
Oct 18, 2025Noah El Rimawi-Fine, Adam Stecklov, Lucas Nelson +4Dynamical Systems Causal Structure Learning
McGill University · Mila – Quebec AI Institute · AITHYRA +5
Tags for DAGs: Graph Refinement with Meta-Informed Relations
Jun 24, 2025Florian Peter Busch, Moritz Willig, Florian Guldan +2Causal Discovery Causal Structure Learning
Computer Science Department, Technical University of Darmstadt · Hessian Center for AI (hessian.AI) · German Research Center for AI (DFKI) +2
The observational partial order of causal structures with latent variables
Feb 11, 2025Marina Maciel Ansanelli, Elie Wolfe, Robert W. SpekkensLatent Variable Models Causal Discovery
Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, Ontario Canada N2L 2Y5 · Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1
IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs
Sep 9, 2024Jinwei He, Feng LuAbductive Reasoning Causal Structure Learning
Beihang University, Beijing, China
Towards Complete Causal Explanation with Expert Knowledge
Jul 10, 2024Aparajithan Venkateswaran, Emilija PerkovićStructural Causal Models Causal Discovery
Microsoft · Redmond, WA 98052, USA · Department of Statistics +2
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Apr 9, 2024Yu Zhou, Xingyu Wu, Jibin Wu +2LLM Evaluation Causal Discovery
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China · College of Computer Science, Chongqing University, Chongqing 400044, China

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Simulation-free Structure Learning for Stochastic Population Dynamics

Noah El Rimawi-Fine, Adam Stecklov, Lucas Nelson, Mathieu Blanchette+3

McGill University · Mila – Quebec AI Institute · AITHYRA+5

Dynamical Systems Causal Structure Learning Conditional Generative Modeling

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Modeling dynamical systems and unraveling their underlying structural dependencies is central to many domains in the natural sciences. Various physical systems, such as those arising in cell biology, are inherently high-dimensional and stochastic in nature, and admit only partial, noisy state measurements. Our primary motivating setting is single-cell biology, where destructive measurements yield unpaired population snapshots rather than longitudinal trajectories of the same cells. This poses a significant challenge for addressing the problems of modeling the underlying dynamics and inferring the network structure of these systems. Existing methods are typically tailored either for structure learning or modeling dynamics at the population level, but are limited in their ability to address both problems together. In this work, we address both problems simultaneously: we present StructureFlow, a novel and principled simulation-free training approach for jointly learning the structure and stochastic population dynamics of physical systems. We showcase the utility of StructureFlow for the tasks of structure learning from interventions and dynamical (trajectory) inference of conditional population dynamics. We empirically evaluate our approach on high-dimensional synthetic systems, a set of biologically plausible simulated systems, and an experimental single-cell dataset. We show that StructureFlow can learn the structure of underlying systems while simultaneously modeling their conditional population dynamics --- a key step toward model-based mechanistic understanding of systems behavior.

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Related topics

Latest papers 96

Simulation-free Structure Learning for Stochastic Population Dynamics

Tags for DAGs: Graph Refinement with Meta-Informed Relations

The observational partial order of causal structures with latent variables

IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs

Towards Complete Causal Explanation with Expert Knowledge

CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs