Causal Explanation

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Causal explanation studies which factors make a difference to an outcome, and how to communicate those dependencies as useful reasons. Methods include structural causal models and counterfactual tests, actual-cause algorithms, causal discovery, and learned explanations for images, videos, control systems, and other settings. Researchers evaluate explanations for properties such as causal faithfulness, minimality, completeness, and performance on benchmarks with known causes. A central challenge is making explanations both computationally scalable and faithful to complex dependencies, while distinguishing causal reasons from correlations or persuasive but unsupported attributions.

Latest papers

26 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Causal Analysis of Failures of Cyber-Physical Systems with Continuous Inputs
Oct 22, 2026Tom Yaacov, David A. Kelly, Khen Elimelech +1Causal Explanation Actual Causality
King’s College London
Sufficient and Necessary Causal Explanations for the Analysis of Autonomous Systems
Oct 21, 2026Tom Yaacov, Khen Elimelech, Hana Chockler +1Causal Explanation
King’s College London · Rice University
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
A Computationally Feasible Framework for Causal Probabilistic Explanation
Sep 3, 2026Rafal Urbaniak, Sam Witty, Daniel Waxman +7Structural Causal Models Actual Causality
Basis Research Institute · Sorbus AI · Massachusetts Institute of Technology +3
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
CAS: A Causal Attribution Score for Local and Global Explainable Artificial Intelligence
Aug 12, 2026Michael Georgiades, Charalambia VarnavaFeature Attribution Causal Attribution
Department of Computer Science Neapolis University Pafos Cyprus · CaSToRC The Cyprus Institute Nicosia, Cyprus
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
Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination
Jul 16, 2026Remo PareschiAbductive Reasoning Causal Attribution
Stake Lab, University of Molise, Italy
Solution of the Hempel's statistical ambiguity problem and Causal AI
Jul 14, 2026Evgenii VityaevCausal Inference Causal Explanation
Sobolev Institute of Mathematics of the SB RAS, Koptuga 4, Novosibirsk, 630090, Russia.
From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond
Jul 6, 2026Spyridon Evangelatos, Christos Diou, Georgios Th. Papadopoulos +2Cyber-Physical Systems Feature Attribution
Research & Innovation Development Department, Netcompany S.A., Luxembourg · Department of Informatics and Telematics, Harokopio University of Athens, Greece · Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Greece +1
Validating Causal Abstraction Metrics on Simulated Complex Systems
Jun 30, 2026Maxime Méloux, Tiago Pimentel, François Portet +1Causal Abstraction Benchmark Validity
Université Grenoble Alpes, CNRS, Grenoble INP, LIG · ETH Zürich
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
From Correlation to Causation in Lane Change Prediction for Automated Driving: A Causal Explanation Framework
Jun 14, 2026Mohamed Manzour, Aditya Kumar, Augusto Luis Ballardini +1Structural Causal Models Autonomous Driving
Computer Engineering Department, University of Alcalá, Madrid, Spain
Formalizing and falsifying causal pathways of rare events
May 29, 2026Anahita Haghighat, Dominik JanzingStructural Causal Models Causal Abstraction
Independent · Amazon Research, Tübingen, Germany
They Are Not the Same: Direct Causes Are Not Grounded Emotion Explanations
May 24, 2026Zhuangzhuang Pan, Yan Xia, Chee Seng ChanRelation Extraction Causal Attribution
Universiti Malaya, Malaysia · Suzhou University of Technology, China · VinUniversity, Vietnam
A Causal Argumentation Method for Explainability of Machine Learning Models
May 20, 2026Henry Salgado, Meagan R. Kendall, Martine CeberioExplainable Artificial Intelligence Causal Explanation
Department of Computer Science, The University of Texas at El Paso, El Paso, TX, USA · Department of Engineering Education and Leadership, The University of Texas at El2026 Paso, El Paso, TX, USA
Mechanism Plausibility in Generative Agent-Based Modeling
May 12, 2026Patrick Zhao, David Huu Pham, Nicholas VincentLLM Agent Evaluation Agent-Based Modeling
Simon Fraser University, Burnaby, BC, Canada
Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
May 11, 2026Ramesh Arvind Naagarajan, Zühal Wagner, Stefan StreifNonlinear MPC Model Predictive Control
Professorship for Automatic Control and System Dynamics, Chemnitz University of Technology, Chemnitz, Germany · Department of Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology, Giessen, Germany.
Causal Explanations from the Geometric Properties of ReLU Neural Networks
May 11, 2026Hector Woods, Philippa Ryan, Rob AlexanderNeural Network Interpretability Causal Explanation
Department of Computer Science University of York York, United Kingdom
Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations
May 9, 2026Leopoldo BertossiExplainable Artificial Intelligence Actual Causality
Carleton University, Canada · IMFD, Chile · Emeritus Professor. Adjunct Professor Western University, Canada.
Position: Explainable AI is Causality in Disguise
Mar 30, 2026Amir-Hossein KarimiExplainable Artificial Intelligence Causal Explanation
Department of Electrical & Computer Engineering, University of Waterloo & Vector Institute, Canada
Avoiding Epiphenomenalism in Theories of Consciousness: A Causal Framework Based on Asymmetry
Nov 6, 2025Yoshiyuki Ohmura, Yasuo KuniyoshiCausal Explanation
Department of Mechano Informatics, The University of Tokyo, Hogo 7-3-1, 113-8656, Tokyo, Japan
ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
Aug 28, 2025Paritosh Parmar, Eric Peh, Basura FernandoVideo QA Multimodal CoT Reasoning
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore
A Communication-First Account of Explanation
May 6, 2025Jacqueline Harding, Tobias Gerstenberg, Thomas IcardCausal Reasoning Causal Explanation
Stanford University
Causal Explanations for Image Classifiers
Nov 13, 2024Hana Chockler, David A. Kelly, Daniel Kroening +1Actual Causality Explainable Image Classification
King’s College London, London, UK · Amazon.com, Inc.,, USA · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE +1
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Apr 1, 2024Paritosh Parmar, Eric Peh, Ruirui Chen +4Video QA Causal Reasoning
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · Nanyang Technological University · National University of Singapore +1

Oct 22, 2026J/K move · Enter open · S save

Causal Analysis of Failures of Cyber-Physical Systems with Continuous Inputs

Tom Yaacov, David A. Kelly, Khen Elimelech, Hana Chockler

King’s College London

Causal Explanation Actual Causality Cyber-Physical Systems

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Modern autonomous Cyber-Physical Systems (CPSs), such as self-driving cars, operate in increasingly complex environments while expected to maintain high reliability. The black-box nature of many of these systems makes it impossible to fully verify their behavior prior to deployment. When a failure occurs, it is important to analyze it to guide improvements to system robustness. A recent work introduced the novel concept of leveraging the framework of actual causality for CPS failure explanation. However, that work considered only a discrete, binary causal model that cannot capture continuous changes in system inputs. In this paper, we address these limitations by extending the actual causality framework with the notion of distance to account for the continuous characteristics of the system. Specifically, we introduce the novel concepts of distance-weighted actual cause and distance-weighted degree of responsibility, enabling causal influence to account for the magnitude of changes required to avoid a failure, rather than only its cardinality. While motivated by the continuous nature of CPSs, this constitutes a general extension of the theory of actual causality and enables more fine-grained analysis of continuous inputs in any causal model. Building on these notions, we present an efficient, practical, system-agnostic explanation-derivation algorithm that uses distance-weighted responsibility to guide the explanation search. We experimentally evaluate our approach across a number of environments from two autonomous-driving simulators with black-box neural controllers. The results show that the proposed extension provides more effective guidance than the current discrete approach.