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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PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
May 8, 2026Elahe Khatibi, Ziyu Wang, Saba A. Farahani +4Structural Causal Models Healthcare
University of California, Irvine · Washington University in St. Louis
Eliciting associations between clinical variables from LLMs via comparison questions across populations
May 7, 2026Fabian Kabus, Kian Kordtomeikel, Thomas Brox +3LLM Interpretability Causal Structure Learning
Institute of Medical Biometry and Statistics (IMBI), Medical Center, University of Freiburg · Department of Computer Science, Faculty of Engineering, University of Freiburg · Department of Neurology and Neurophysiology, Medical Center, University of Freiburg +1
Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
May 7, 2026Sunmin Oh, Sang-Yun Oh, Gunwoong ParkStructural Causal Models Causal Discovery
Department of Statistics, Seoul National University, South Korea · Department of Statistics and Applied Probability, University of California Santa Barbara, CA, USA · Scientific Data Division, Lawrence Berkeley National Laboratory, CA, USA +2
Order-based Rehearsal Learning
May 6, 2026Yu-Xuan Tao, Tian-Zuo Wang, Zhi-Hua ZhouCausal Discovery Causal Structure Learning
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artifcial Intelligence, Nanjing University, China
M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
May 1, 2026J. Jake Nichol, Michael Weylandt, G. Matthew Fricke +2Time-Series Causal Discovery Climate Science
Scientific Machine Learning, Center for Computing Research Sandia National Laboratories · Zicklin School of Business Baruch College, CUNY · Department of Computer Science University of New Mexico +2
Differentiable latent structure discovery for interpretable forecasting in clinical time series
Apr 30, 2026Ivan Lerner, Jean Feydy, Alexandre Kalimouttou +2Irregular Time-Series Modeling Healthcare
Universit´e Paris Cit´e and Universit´e Sorbonne Paris Nord, INRIA, Inserm, HEKA, Paris, 75015, France. · Department of Medical Informatics, Assistance Publique Hˆopitaux de Paris (AP-HP), Georges Pompidou European Hospital, Paris, France. · SIERRA, Inria Paris, Paris, 75015, France. +2
Causal Learning with Neural Assemblies
Apr 29, 2026Evangelia Kopadi, Dimitris KallesNeural Processes Causal Discovery
Hellenic Open University Greece
Optimization-Free Topological Sort for Causal Discovery via the Schur Complement of Score Jacobians
Apr 28, 2026Rui Wu, Hong XieCausal Discovery Causal Structure Learning
School of Computer Science and Engineering University of Science and Technology of China
TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
Apr 27, 2026Omar Faruque, Sahara Ali, Xue Zheng +1Time-Series Causal Discovery Causal Structure Learning
Department of Information Systems University of Maryland, Baltimore County Baltimore, Maryland, USA · Department of Data Science University of North Texas Denton, Texas, USA · Climate Science Section Lawrence Livermore National Laboratory Livermore, California, USA
Grounding Before Generalizing: How AI Differs from Humans in Causal Transfer
Apr 27, 2026Liangru Xiang, Yuxi Ma, Zhihao Cao +2LLM Grounding Causal Structure Learning
Department of Automation, Tsinghua University · State Key Laboratory of General Artificial Intelligence · Institute for Artificial Intelligence, Peking University +2
Causal Representation Learning from General Environments under Nonparametric Mixing
Apr 26, 2026Ignavier Ng, Shaoan Xie, Xinshuai Dong +2Causal Representation Learning Causal Discovery
1Carnegie Mellon University · 2Mohamed bin Zayed University of Artificial Intelligence
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations
Apr 16, 2026Xiangning Yu, Yuwei Guo, Yuqi Hou +2Causal Discovery Causal Structure Learning
College of Intelligence and Computing, Tianjin University, Tianjin, China · Tianjin Key Laboratory of Healthy Habitat and Smart Technology, Tianjin, China · Laboratory of Computation and Analytics of Complex Management Systems, Tianjin University, Tianjin, China
Learning Causal Structure of Time Series using Best Order Score Search
Mar 5, 2026Irene Gema Castillo Mansilla, Urmi NinadTime-Series Causal Discovery Bayesian Networks
University of Potsdam, Potsdam, Germany · Department of Computer Science, University of Potsdam, Potsdam, Germany
Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions
Mar 2, 2026Amir Asiaee, Kavey Aryan, James P. LongSelective Inference Causal Structure Learning
Department of Biostatistics, Vanderbilt University Medical Center, TN 37203, USA · Department of Informatics, King’s College London, London, UK · Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions
Oct 25, 2025Hans Jarett Ong, Yoichi Chikahara, Tomoharu IwataMeta-Learning Causal Discovery
Nara Institute of Science and Technology, Nara, Japan · NTT Communication Science Laboratories, Kyoto, Japan
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

May 8, 2026, cs.LGJ/K move · Enter open · S save

PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

Elahe Khatibi, Ziyu Wang, Saba A. Farahani, Di Huang+3

University of California, Irvine · Washington University in St. Louis

Structural Causal Models Healthcare Time-Series Causal Discovery Causal Discovery Causal Structure Learning Clinical Reasoning+1

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Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing temporal causal discovery methods are poorly matched to this setting: cohort-level models provide stable but non-personalized structures, while per-patient discovery is unreliable because individual trajectories are short, noisy, irregular, and non-stationary. This creates a fundamental gap between population-level causal modeling and the patient-specific, time-varying mechanisms needed for intervention reasoning. We introduce PerCaM-Health, a framework for learning personalized dynamic causal graphs from longitudinal health data. The framework learns a knowledge-guided population temporal graph, then conservatively adapts and evolves it using patient-specific temporal evidence and rolling-window updates, producing interpretable and auditable graph sequences. By coupling these graphs with temporal structural equations, the framework enables patient-level counterfactual queries, such as estimating short-horizon outcome changes under hypothetical behavioral interventions. Experiments on a semi-synthetic dynamic health benchmark show that PerCaM-Health improves graph recovery, dynamic edge tracking, and intervention direction accuracy compared to cohort-level, per-patient, and non-personalized temporal baselines. These results demonstrate that jointly modeling personalization and temporal evolution yields more reliable causal structure and intervention reasoning.

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