Causal Reasoning

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Causal reasoning asks what produces an outcome, what would change under an intervention, and why an event occurred. Researchers use structural causal models and graphs, discovery and effect-estimation methods, do-calculus, and counterfactual approaches to explanation and responsibility. Recent work also tests and builds language, vision, and agent systems using explicit causal graphs, reasoning traces, simulators, and interactive tasks. A central challenge is distinguishing genuine use of causal structure from reliance on familiar wording or correlations, while making assumptions and uncertainty explicit.

Latest papers

77 papers5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

Causal-based approaches to explain and learn from self-extension - A review
Oct 23, 2026Rebeca Marfil, Pablo Bustos, Antonio BanderaSelf-Evolving Agents Cognitive Architectures for AI Agents
Universidad de M´alaga, Spain · Universidad de Extremadura, Spain
Automatic Cause Determination in Road Scene Understanding Using Qualitative Reasoning and Four-Valued Logic
Oct 14, 2026Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar +1Autonomous Driving Causal Reasoning in Videos
Simula Research Laboratory, Oslo, Norway · LISN, Université Paris-Saclay, Saclay, France
Cognition-Oriented Emotion Tracing from Causes to Consequences in Real-World Social Scenes
Oct 8, 2026Hao Li, Jinye Zhang, Bobo Li +5Affective Computing Multimodal Emotion Recognition
School of Computer Science, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan 430072, China
What Can Analogy Tell Us About Artificial Consciousness?
Oct 1, 2026Keith J. Holyoak, Martin M. MontiAnalogical Reasoning Machine Consciousness
Department of Psychology, University of California, Los Angeles · Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles
Abductive World Modeling via Causal Representation Learning
Sep 29, 2026Ziqi Liu, Songhan Yang, Linfan Zhou +4Causal Representation Learning Abductive Reasoning
BAIZ Team
From Anomalies to Failures: Constructing Causal Error Graphs for Agentic Trace Diagnosis
Sep 26, 2026Shu-Xun Yang, Yidong Wang, Zhuoer Feng +7Agent Failure Analysis Reasoning Trace Analysis
Beijing Institute of Technology, Beijing, China · Zhipu AI, Beijing, China · Tsinghua University, Beijing, China
Externalized CPDAG Summaries Improve LLM Causal Deduction
Sep 25, 2026Wentao Sun, João Paulo Nogueira, Dominique Verchere +2Causal Reasoning in Language Models Causal Reasoning
Nokia Bell Labs · Télécom SudParis · IRISA
Regional Explanations via Causal Sufficiency and Necessity
Sep 16, 2026Xuexin Chen, Peng Liang, Zijian Li +2Causal Reasoning Causal Inference
School of Artificial Intelligence, Guangdong Polytechnic Normal University, Guangzhou 510665, China · Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, United Arab Emirates · School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Causal Reasoning with Bipartite Graphical Causal Models
Aug 20, 2026Joris M. MooijCausal Reasoning Causal Intervention
Korteweg-De Vries Institute for Mathematics, University of Amsterdam, Amsterdam, the Netherlands
CoEvo: Oracle-Grounded Self-Evolution of a Single Model for Multi-Step Causal Reasoning
Aug 18, 2026Jian Zhang, Bingyi Wang, Yizhi LiuCausal Reasoning in Language Models Causal Reasoning
Zhejiang University
A Minimal $κ$ -- $τ$ Logic for Risk-Sensitive Abduction
Aug 8, 2026Remo PareschiAbductive Reasoning Causal Reasoning
STAKE Lab, University of Molise 86100 Campobasso, Italy
Toward a Causal Data Management Ecosystem for Decision Making and Agentic AI
Aug 7, 2026Dazhuo Qiu, Yingli Zhou, Amedeo Pachera +2AI-Assisted Decision Making Causal Reasoning
Lyon 1 University, CNRS Liris Lyon, France
Abstract Event Causal Rules: Induction and Application
Aug 5, 2026Ziwei Zheng, Peiqiong Chen, Bang WangCausal Abstraction Causal Reasoning
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
Responsibility in Multi-Agent Sequential Decision-Making: Comparing Human Judgments to Formal Models of Causal Attribution
Aug 5, 2026Nripsuta Ani Saxena, Stelios Triantafyllou, Goran RadanovićActual Causality Multi-Agent Systems
University of Southern California Los Angeles, California, USA · Max Planck Institute for Software Systems Saarbrücken, Germany
Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs
Aug 4, 2026Jannick Strobel, Muqsit Azeem, Stefan LeueBranch and Bound Actual Causality
University of Konstanz
CausalOPD: First-Wrong-Step Supervision for Distilling Causal Chain Reasoning
Aug 4, 2026Jian Zhang, Bingyi Wang, Yizhi LiuCurriculum RL Causal Reasoning in Language Models
Zhejiang University
Logic Programming Semantics for Causal Processes
Jul 23, 2026Felix WeitkämperLogic Programming Causal Reasoning
German University Of Digital Science Potsdam, Germany
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Jul 23, 2026Kilian Rueckschloss, Felix WeitkaemperProbabilistic Logic Logic Programming
Eberhard Karls Universitaet Tuebingen · University of Tübingen Tübingen, Germany · German University of Digital Science +1
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
CausalDS: Benchmarking Causal Reasoning in Data-Science Agents
Jul 9, 2026Andrej Leban, Theo Saarinen, Yuekai SunStructural Causal Models AI Agent Benchmarks
Department of Statistics, University of Michigan, Ann Arbor, MI, United States
CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
Jul 5, 2026Zhenhao Chen, Yongqiang Chen, Chenxi Liu +7AI Agents for Scientific Discovery AI Agent Benchmarks
1MBZUAI · 2Carnegie Mellon University · 3TMLR Group, Hong Kong Baptist University +2
Actual causality in fault trees
Jul 2, 2026Georgiana Caltais, Milan Lopuhaä-Zwakenberg, Mariëlle StoelingaActual Causality Causal Attribution
University of Twente, The Netherlands · Radboud University, The Netherlands
Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
Jun 30, 2026Martina Mattioli, Marcello PelilloExplainable Artificial Intelligence Causal Reasoning
College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China · DAIS, Ca’ Foscari University of Venice, Italy · College of Mathematical Medicine, Zhejiang Normal University, China +1
Beyond But-for Test: Counterfactual Explanation in Abstract Argumentation via Actual Causality (Extended Version)
Jun 30, 2026Siyi Liu, Muyun Shao, Beishui LiaoCounterfactual Explanations Actual Causality
Zhejiang University, Hangzhou, China
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Jun 29, 2026Ryohei Oura, Georgios Fainekos, Hideki Okamoto +1Reachability Analysis Markov Decision Processes
Toyota Research Institute of North America, Toyota Motor North America · Frontier Research Center, Toyota Motor Corporation
Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
Jun 26, 2026Roberto Pietrantuono, Luca Giamattei, Stefano RussoSoftware Engineering Causal Reasoning
University of Naples Federico II, Italy
Difference-Making without Making a Difference
Jun 23, 2026Sander BeckersActual Causality Causal Attribution
University College London
Explaining Failures of Cyber-Physical Systems with Actual Causality
Jun 23, 2026Khen Elimelech, Tom Yaacov, David A. Kelly +2Agent Failure Analysis Cyber-Physical Systems
King’s College London · Rice University
Beyond Simpson's Paradox: A Cascade of Confounders in AI Agent Pull-Request Co-Authorship
Jun 21, 2026Haoran Yu, Xiaochong Jiang, Lifei Liu +3Software Engineering AI Coding Agents
Independent Researcher Seattle, WA, USA · Carnegie Mellon University Pittsburgh, PA, USA · Georgia Institute of Technology Atlanta, GA, USA
Faithful Action-unit Causal Reasoning for Counterfactually Faithful Emotion Explanations
Jun 14, 2026Van Thong Huynh, Hong Hai Nguyen, Thuy Pham +2Counterfactual Evaluation Counterfactual Explanations
Faculty of CSE, Ho Chi Minh City University of Technology (HCMUT), VNUHCM, Vietnam · Dept. of AI, FPT University, Vietnam · Faculty of DSAI, College of Technology, National Economic University, Vietnam +1

Oct 23, 2026J/K move · Enter open · S save

Causal-based approaches to explain and learn from self-extension - A review

Rebeca Marfil, Pablo Bustos, Antonio Bandera

Universidad de M´alaga, Spain · Universidad de Extremadura, Spain

Self-Evolving Agents Cognitive Architectures for AI Agents Explainable Artificial Intelligence Causal Reasoning Trust in HRI

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The last decades have seen a revolution in autonomous robotics. Deep learning approaches and their hardware implementations have made it possible to endow robots with extraordinary perceptual capabilities. In addition, they can also benefit from advances in Automated Planning, allowing them to autonomously solve complex tasks. However, on many occasions, the robot still acts without internalising and understanding the reasons behind a perception or an action, beyond an immediate response to a current state of the context. This gap results in limitations that affect its performance, reliability and trustworthiness. Deep learning alone cannot bridge this gap, because the reasons behind behaviour, when it emanates from a model in which the world is a black-box, are not accessible. What is really needed is an underlying architecture based on deeper reasoning. Among other issues, this architecture should enable the robot to generate explanations, allowing people to know why the robot is performing, or has performed, a certain action, or the reasons that may have caused a certain plan failure or perceptual anomaly. Furthermore, when these explanations arise from a cognitive process and are shared, and thus validated, with people, the robot should be able to incorporate these explanations into its knowledge base, and thus use this understanding to improve future behaviour. Our article looks at recent advances in the development of self-aware, self-evolving robots. These robots are designed to provide the necessary explanations to their human counterparts, thereby enhancing their functional capabilities, in the quest to gain their trust.