Causal Structure Learning

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14 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Score-Based Learning of Cluster DAGs from Interventions
Oct 8, 2026Gaetano Tedesco, Alex MarkhamCausal Structure Learning Causal Discovery
Informatics Institute, University of Amsterdam · Department of Mathematical Sciences, University of Copenhagen
Executing Causal Structure Learning with Linear-Attention Transformers
Oct 7, 2026Amartya Roy, Sayar KarmakarNeural Algorithmic Reasoning Causal Structure Learning
School of Interdisciplinary Research Indian Institute of Technology Delhi · University of Florida
AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings
Oct 7, 2026Shun Yanashima, Kentaro Kanamori, Hirofumi SuzukiCausal Discovery Causal Structure Learning
Artiﬁcial Intelligence Laboratory, Fujitsu Limited, 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, 211-8588, Kanagawa, Japan.
Directional Evidence Guided Search-Space Reduction for Exact DAG Learning
Oct 6, 2026Upala Junaida Islam, Abdelmonem Elrefaey, Rong PanCausal Discovery Causal Structure Learning
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA.
GO-Based Clustering for Learning Cluster-Level Causal Gene Regulatory Networks
Oct 5, 2026Azlaan Mustafa Samad, Wei Zhang, Adèle H RibeiroClustering Causal Discovery
L3S Research Center, Leibniz University Hannover, Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed), Hannover, Germany · Institute for Causal and Explainable Artificial Intelligence in Life Sciences, Center for Computational Life Sciences, RWTH Aachen University, Germany
Structure-agnostic Causal Representation Learning
Oct 1, 2026Arman Behnam, Binghui WangCausal Representation Learning OOD Generalization
Department of Computer Science Illinois Institute of Technology Chicago, IL, USA
Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
Sep 29, 2026Sadegh Khorasani, Ali Najar, Saber Salehkaleybar +1Structural Causal Models Causal Discovery
School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland · Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands · College of Management of Technology, EPFL, Lausanne, Switzerland
Demistifying Data and Simulator Assumptions in Supervised Causal Discovery
Sep 29, 2026Pingchuan Ma, Rui Ding, Bojun Huang +1Causal Discovery Causal Structure Learning
Zhejiang University of Technology, China · Microsoft Research Asia · Sony Research +1
Do-JEPA: From Masking to Intervention in Latent World Models
Sep 29, 2026Hossein Resani, Javen Qinfeng ShiCausal Effect Estimation Causal World Models
Australian Institute for Machine Learning, Adelaide University · Responsible AI Research Centre
Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization
Sep 28, 2026Michael Coppedge, Dmitry Zaytsev, Valentina KuskovaTime-Series Causal Discovery Causal Discovery
Department of Political Science, University of Notre Dame, Notre Dame, IN, USA · Kellogg Institute, University of Notre Dame, Notre Dame, IN, USA · Lucy Family Institute for Data & Society, University of Notre Dame, Notre Dame, IN, USA
The Statistical Cost of Causal Discovery with Feedback
Sep 28, 2026Sunmin Oh, Seungsu Han, Gunwoong ParkSample Complexity Causal Discovery
Department of Statistics; Institute for Data Innovation in Science, Seoul National University, Korea · Department of Operations Research and Financial Engineering, Princeton University, USA · Department of Statistics; Interdisciplinary Program in Artificial Intelligence; Institute for Data Innovation in Science, Seoul National University, Korea.
xWhyL: Causal Interactive Learning
Sep 22, 2026Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig +4Causal Discovery Causal Structure Learning
Bosch Center for Artificial Intelligence, Germany · Computer Science Department, Technical University of Darmstadt, Germany · Hessian Center for AI (hessian.AI), Darmstadt, Germany
Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models
Sep 21, 2026Zijian Li, Ruichu Cai, Feng Xie +7Latent Variable Models Causal Discovery
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Carnegie Mellon University, Pittsburgh, PA, USA · Guangdong University of Technology, Guangzhou, China +3
Decoupled Causal Discovery
Sep 20, 2026Zhengkang Guan, Fei Wu, Kun KuangCausal Discovery Causal Structure Learning
College of Computer Science and Technology Zhejiang University
MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation
Sep 17, 2026Yudai Nakada, Yuichiro Nishiura, Jin Michael SplichalMulti-Agent LLM Systems Causal Discovery
SCSK Corporation Tokyo, Japan
Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders
Sep 16, 2026Weijian Yu, Jean HonorioStructural Causal Models Low-Rank Matrix Decomposition
CIS, The University of Melbourne
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
Sep 16, 2026Sambit Mishra, Urbashi MitraStructural Causal Models Causal Discovery
Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles, CA 90089, USA
Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems
Sep 14, 2026Nima NouriTime-Series Causal Discovery Causal Discovery
Artificial Intelligence for Science Innovation, AstraZeneca, Waltham, MA, USA
Tensor Network Moral Graph Recovery of Discrete Probability Distributions
Sep 8, 2026Á. Troyano Olivas, Chi-Hang Fred Fung, Hans H. Brunner +2Tensor Networks Graph Structure Learning
Heisenberg Research Center (Munich), Huawei Technologies Duesseldorf GmbH, Germany · Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
Support Selection Beyond Smooth DAG Exactness: Completion Geometry,Score Margins, and Selective Certificates
Aug 8, 2026Rui Wu, Zongyuan Chen, Hong XieCausal Discovery Causal Structure Learning
School of Computer Science and Engineering, University of Science and Technology of China
From probability to causality in probabilistic logic programming
Aug 7, 2026Zora Wurm, Kilian Rückschloß, Felix WeitkämperProbabilistic Logic Causal Structure Learning
Ludwig-Maximilians-Universität München, Oettingenstraße 67, 80538 München · Eberhard-Karls-Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen · German University of Digital Science, Marlene-Dietrich-Allee 14, 14482 Potsdam
GENESIS: Towards Explainable Causal Discovery
Aug 4, 2026Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat +2Causal Discovery Causal Structure Learning
User Engagement Research, Sony Research India
Learning Implicit Causal World Models from Multi-Agent Demonstrations
Jul 28, 2026Jasorsi GhoshCausal Representation Learning Causal World Models
Department of Computer Science Purdue University West Lafayette, Indiana, USA
EviDAG: Auditable Causal DAG Authoring with Biomedical Literature
Jul 23, 2026Yi-han Sheu, Michael R. Steigman, Yu Zhou +3Data Provenance Evidence-Grounded Reasoning
Massachusetts General Hospital, USA · Harvard University, USA · Northeastern University, USA
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Jul 22, 2026Zheng Li, Hao Zhang, Ruxin Wang +3Causal Discovery Causal Structure Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. · School of Computer Science, Guangdong University of Technology, Guangzhou, China. · Peng Cheng Laboratory, Shenzhen, China. +3
Causal Discovery on Irregular Time Series
Jul 20, 2026Martim Penim, Ricardo Ribeiro Pereira, Jacopo Bono +3Irregular Time-Series Modeling Time-Series Causal Discovery
1Feedzai, Portugal · 3DCC, Faculdade de Ciêncidas da Universidade do Porto, Portugal · 2Instituto Superior Técnico, Universidade de Lisboa, Portugal +1
Integrating Background Knowledge for Scalable Causal Discovery
Jul 11, 2026Mátyás Schubert, Theofanis Aslanidis, Tom Claassen +1Causal Discovery Causal Structure Learning
University of Amsterdam · Radboud University Nijmegen · Saarland University
DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data
Jul 10, 2026Xin Li, Jin Li, Shoujin Wang +2Causal Discovery Causal Structure Learning
University of Technology Sydney · Sydney, NSW, Australia
Structure Learning on Clustered Data
Jul 9, 2026Ryan Thompson, Matt P. Wand, Veerabhadran BaladandayuthapaniCausal Structure Learning Causal Identifiability
School of Mathematical and Physical Sciences University of Technology Sydney Ultimo, NSW 2007, Australia · Department of Biostatistics University of Michigan Ann Arbor, MI 48105, USA
Personalized Causal Recourse: A Human-In-The-Loop Approach
Jul 3, 2026Denise Tampieri, Giovanni De Toni, Paolo GiudiciStructural Causal Models Causal Structure Learning
The University of Edinburgh, Edinburgh, UK · ⋆Work conducted during the author’s MSc at the University of Trento, Trento, Italy. · Fondazione Bruno Kessler, Trento, Italy +1

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Score-Based Learning of Cluster DAGs from Interventions

Gaetano Tedesco, Alex Markham

Informatics Institute, University of Amsterdam · Department of Mathematical Sciences, University of Copenhagen

Causal Structure Learning Causal Discovery Causal Abstraction Graph Coarsening Causal Intervention

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Graphical approaches to causal abstraction transform a low-level causal directed acyclic graph (DAG) over many measured variables into a smaller, high-level DAG whose nodes cluster the original variables and whose edges summarize the causal relations between clusters. Such cluster DAGs are easier to interpret, but learning them requires finding the clusters and recovering the edges between them. Madaleno et al. (2026) learn the interventional coarsening (the cluster DAG that merges variables the interventions cannot distinguish) in two constraint-based phases: first the clusters, then the edges. We introduce COARSE, the first score-based method for this task: it keeps the two-phase structure but, under linear Gaussian assumptions, swaps the constraint-based edge phase for a score-based one. We show that the interventions themselves identify a causal order over the clusters, and learning the edges reduces to a single local search per cluster under a cluster-level BIC score. We prove that the procedure runs in polynomial time and, provided the variables affected by each intervention are correctly identified, that it is consistent. On synthetic and real-world interventional data, COARSE matches state-of-the-art edge recovery given enough samples, with an edge phase up to two orders of magnitude faster, including on dense graphs with hundreds of nodes.

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