Causal Discovery

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17 papers in the last four weeks, up 183% on the four weeks before. 0.2% of all new papers.

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Latest papers 148

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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
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.
DrugTargetWorld: A Synthetic Biobank for Training and Benchmarking AI Scientists
Oct 7, 2026Samuel Margolis, Paul Schmiedmayer, Alan Huang +12Drug Discovery AI Agent Benchmarks
Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA · Department of Medicine, Stanford University, Stanford, CA 94305, USA · Division of Computational Medicine, Department of Medicine, Stanford University, Stanford, CA 94305, USA +1
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
Inferring Causal Relations between Two Sequences of Events with Language Models
Sep 30, 2026Nishchal Prasad, Eric Gaussier, Emilie Devijver +3Time-Series Causal Discovery Causal Discovery
INRIA, Univ. Grenoble Alpes, CNRS, LIG · Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG · Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG +1
Discrete Score Matching Enables Causal Discovery from Count Data
Sep 30, 2026Euijong Song, Hyewon Park, Gunwoong ParkScore Matching Causal Discovery
Department of Statistics Seoul National University, Korea · Interdisciplinary Program in Artificial Intelligence; Institute for Data Innovation in Science, Seoul National University, Korea
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
DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery
Sep 28, 2026Yulong Li, Rong Xia, Yuxuan Zhang +12Causal Discovery Causal Attribution
MBZUAI
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
ITSY: Causal Discovery From Irregular Time-Series Data
Sep 20, 2026Wenbo Xu, Yue He, Yunhai Wang +2Irregular Time-Series Modeling Structural Causal Models
Renmin University of China, China · Zhejiang University, China
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sep 17, 2026Sambit Mishra, Yingying Wang, Christine K. Johnson +1Structural Causal Models Causal Discovery
University of Southern California · University of California, Davis
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
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
Causal Discovery via Transformed Low-Rank Quantile Surfaces
Sep 15, 2026Ryo Kamimura, Thong PhamCausal Discovery Causal Identifiability
1Shiga University · 3RIKEN AIP · 2The University of Osaka
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
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Sep 10, 2026Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1Causal Foundation Models Structural Causal Models
School of Artificial Intelligence, Nanjing University
Federated Causal Discovery via Regression-Directed Cumulants
Sep 3, 2026Pablo Torrijos, Fabio Stella, José A. Gámez +1Structural Causal Models Causal Discovery
Departamento de Sistemas Inform´aticos Universidad de Castilla-La Mancha Albacete, Spain · Dipartimento di Informatica, Sistemistica e Comunicazione Universit`a degli Studi di Milano Bicocca Milano, Italy · Departamento de Sistemas Inform´aticos2026 Universidad de Castilla-La Mancha Albacete, Spain
Guide, Not Bind: Why Defeasible Priors Fail in Augmented Lagrangian Causal Discovery
Sep 3, 2026Sairam Sundararaman, Sara Girdhar, Manit Narasimha Murthy +2Causal Discovery Causal Identifiability
Department of Computer Science (AI & ML) PES University · Department of Computer Science2026 PES University · Department of Computer Science PES University
Replicating TRACE: A Practitioner's Guide to Its Threshold and Particle Budget
Sep 1, 2026Alex Chadyuk, Alicia Zhang, Roy KucukatesTime-Series Causal Discovery Causal Discovery
LotusFlare Inc.
Meteorology-driven Causal Nowcasting of Fugitive Landfill Emissions from Measured Coupling Timescales
Aug 14, 2026Timothy C. Pearce, David J. T. Smith, Alec Dobney +1Causal Discovery Air Quality Prediction
Biomedical Engineering Research Group, School of Engineering, University of Leicester, Leicester, United Kingdom. · Environmental Hazards and Emergency Department, UKHSA, Nottingham, United Kingdom.
Interpretable Causal Discovery via Causal-Effect Constraints
Aug 12, 2026Cixuan Zhang, Guy Van den Broeck, Benjie WangBayesian Inference Causal Discovery
Computer Science Dept., Yale University, New Haven, Connecticut, USA · Computer Science Dept., University of California, Los Angeles, Los Angeles, California, USA
Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey
Aug 11, 2026Pavel Averin, Theodoros Moysiadis, Ioannis KatakisCausal Discovery Causal Inference
Department of Computer Science School of Sciences and Engineering University of Nicosia 2417, Nicosia, Cyprus
Human-Guided Causal Knowledge Injection for Virtual Cells
Aug 9, 2026Pengcheng Wang, Changjian Chen, Zhuo Tang +4Causal Discovery Gene Regulatory Network Inference
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

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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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Latest papers 148