Causal Discovery

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Causal discovery studies how to recover directed relationships among variables when experiments are limited or observational data alone are ambiguous. Methods include conditional-independence tests, score-based and ordering searches, functional models such as additive-noise and LiNGAM, and approaches that use interventions or changes across environments; time-series methods also model lagged effects and feedback. Latent confounding, missing data, nonstationarity, and measurement noise complicate recovery, so results often depend on assumptions and may identify only an equivalence class rather than one graph. Current work seeks methods that scale to large and diverse datasets while quantifying uncertainty and testing robustness, with a continuing challenge of translating benchmark performance into reliable real-world discoveries.

Latest papers

222 papers31 papers in the last four weeks, up 72% on the four weeks before. 0.2% of all new papers.

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
When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data
May 6, 2026Miguel Fernandez-de-Retana, Ruben Sanchez-Corcuera, Unai Zulaika +2Causal Discovery Gene Regulatory Network Inference
Faculty of Engineering University of Deusto Bilbao, Spain
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
May 6, 2026Thomas S. Robinson, Ranjit LallPermutation Testing Incomplete Data Imputation
Department of Methodology London School of Economics and Political Science London, UK · Department of Politics and International Relations University of OxfordMay Oxford, UK
Causal discovery under mean independence and linearity
May 6, 2026Geert Mesters, Alvaro Ribot, Anna Seigal +1Structural Causal Models Causal Discovery
Partial Effective Information Decomposition for Synergistic Causality
May 5, 2026Mingzhe Yang, Shuo Wang, Jiang ZhangCausal Discovery Causal Intervention
School of Systems Science Beijing Normal University
TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
May 4, 2026Gideon Stein, Niklas Penzel, Tristan Piater +1Time-Series Causal Discovery Causal Discovery
Computer Vision Group Jena Friedrich Schiller University Jena Jena, Thuringia, 07743, DE
PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
May 3, 2026Xihang Shan, Da ZhouTime-Series Causal Discovery Bayesian Inference
School of Mathematical Sciences Xiamen University Fujian, 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
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 XieScore-Based Causal Discovery Causal Discovery
School of Computer Science and Engineering University of Science and Technology of China
Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft
Apr 27, 2026Zhou Ziheng, Huacong Tang, Jinyuan Zhang +9AI Agents for Scientific Discovery Circuit Discovery
University of California, Los Angeles · Peking University · Amazon
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
Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework
Apr 26, 2026Sheng Wei, Yulin Chen, Beishui LiaoCausal Discovery Computational Argumentation
The College of Computer Science and Technology, Zhejiang University · School of Philosophy, Zhejiang University · ZLAIRE, Zhejiang University +1
From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
Apr 24, 2026Zongyu LiCausal Effect Estimation Causal Discovery
Separable Pathways for Causal Reasoning: How Architectural Scaffolding Enables Hypothesis-Space Restructuring in LLM Agents
Apr 21, 2026John Alderete, Sebastian Benthal, Connie Xu +1Causal Discovery LLM Agents
Amigo AI · Simon Fraser University · International Computer Science Institute
Implicit Causal Graph Construction in Text via Chain Discovery
Apr 20, 2026Liesbeth Allein, Marie-Francine MoensCausal Discovery Causal Reasoning in Language Models
Department of Computer Science, KU Leuven, Belgium · Department of Electronics and Information Systems, Ghent University, Belgium
ClimateCause: Complex and Implicit Causal Structures in Climate Reports
Apr 16, 2026Liesbeth Allein, Nataly Pineda-Castañeda, Andrea Rocci +1Relation Extraction Climate Science
Department of Computer Science, KU Leuven, Belgium · Institute of Argumentation, Linguistics, and Semiotics (IALS), Universit`a della Svizzera italiana, Switzerland
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
A novel hybrid approach for positive-valued DAG learning
Apr 10, 2026Yao ZhaoScore-Based Causal Discovery Causal Discovery
Department of Statistics, Operations, and Data Science Fox School of Business, Temple University Philadelphia, PA, USA
iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations
Apr 8, 2026Wenshuo Wang, Boyu Cao, Nan Zhuang +1Synthetic Data Generation for Language Models Inverse Design
School of Future Technology, South China University of Technology, Guangzhou, China · China Mobile GBA Innovation Institute, Guangzhou, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approach
Apr 7, 2026Tiago Brogueira, Mário A. T. FigueiredoCausal Discovery Minimum Description Length
Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Portugal
Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps
Apr 6, 2026Jose L. SalmeronFuzzy Cognitive Maps Fuzzy Logic
School of Engineering, CUNEF Universidad, Madrid, Spain
SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions
Apr 3, 2026Matthias Weiß, Falk Dettinger, Elias Detrois +2Fault Diagnosis Causal Discovery
Institute of Industrial Automation and Software Engineering (IAS), University of Stuttgart, Pfaffenwaldring 47, 70550 Stuttgart, Germany
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Apr 2, 2026Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1Time-Series Causal Discovery Causal Discovery
Institute for Systems and Robotics, Instituto Superior Técnico, 1049-001 Lisbon, Portugal · Digital Scholarship at Oxford, University of Oxford, Oxford, OX1 3BG, United Kingdom · Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives
Apr 2, 2026Hao Zhu, Di Zhou, Donna SlonimCausal Discovery Causal Structure Learning
Beth Israel Deaconess Medical Center Harvard Medical School Boston, MA, USA · Department of Computer Science Tufts University Medford, MA, USA
CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing
Mar 31, 2026Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson +1Smart Manufacturing Root Cause Analysis
Artificial Intelligence Institute, University of South Carolina · Stride Lab, University of Michigan, Dearborn · Bosch Center for Artificial Intelligence, Pittsburgh
Identification of Bivariate Causal Directionality Based on Anticipated Asymmetric Geometries
Mar 27, 2026Alex GlushkovskyCausal Discovery
Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure
Mar 26, 2026Benjamin Redden, Hui Wang, Shuyan LiCausal Interpretability Causal Discovery
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, United Kingdom
Enes Causal Discovery
Mar 25, 2026Alexis KafantarisCausal Discovery
Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis
Mar 24, 2026Mohyeu Hussain, David Koblah, Reiner Dizon-Paradis +1Electronic Design Automation Causal Interpretability
University of Florida

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Order-based Rehearsal Learning

Yu-Xuan Tao, Tian-Zuo Wang, Zhi-Hua Zhou

National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artifcial Intelligence, Nanjing University, China

Causal Discovery Causal Structure Learning

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When a machine learning (ML) model forecasts an undesired event, one often seeks a decision to avoid it, known as the avoiding undesired future (AUF) problem. Many rehearsal learning methods have been proposed for AUF, but they rely on an underlying graph structure; learning such a graph from observational data is challenging and can incur substantial estimation error. In this work, we demonstrate that the order structure can be sufficient for AUF decision-making, and propose the first order-based rehearsal learning method. Although an order is less informative than a graph, it can be sufficient to identify the influence of decisions from observational data, suggesting that learning the entire graph is not always necessary. To learn the order, we develop an information-theoretic method that imposes no restrictions on the form of structural functions or the type of noise distributions. For AUF decision-making, we construct an order-based sampler to approximate the influence of decisions and, combined with a surrogate objective for maximizing the post-decision success probability, reduce the AUF task to a differentiable optimization problem. Experiments show that our order learning method outperforms existing methods, and that our AUF approach not only surpasses methods relying on learned graphs or learned orders, but also matches or even exceeds oracle baselines that are given the true graph.