Actual Causality

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Actual causality asks which events made a particular outcome happen, rather than merely predicting outcomes or finding correlations. Common approaches use Halpern–Pearl definitions in structural causal models, testing counterfactual interventions and refining but-for tests to handle preemption and overdetermination. These methods support explanations and responsibility judgments in settings such as image classifiers, system failures, and multi-agent decisions, with algorithms increasingly designed to find minimal causes at practical scales. A central tension is balancing formal properties and computational efficiency while ensuring that model-based attributions capture relevant dependencies and match intuitive or human judgments.

Latest papers

16 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

A Hybrid Framework for Explainable Aviation Risk: Causal Chain Extraction from Safety Reports with Physics-Based Failure Quantification and Interventional Analysis
Oct 26, 2026Yutian Pang, Yifang Tian, Jiacheng Xie +4Aviation Safety Causal Inference
Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA. · Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada. · Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. +2
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
Slaying the Hydra: Interaction-Aware Circuit Discovery in Language Models
Oct 2, 2026Sankaran Vaidyanathan, Rafal Urbaniak, Emily Bunnapradist +2Circuits in Language Models Mechanistic Interpretability
Basis Research Institute · University of Massachusetts Amherst · MIT
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
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
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
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
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
Neural Information Causality
May 10, 2026Jeongho Bang, Marcin PawłowskiRepresentation Learning Actual Causality
Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul 03722, Republic of Korea · Department of Quantum Information, Yonsei University, Incheon 21983, Republic of Korea · International Centre for Theory of Quantum Technologies, University of Gda´nsk, 80-308 Gda´nsk, Poland +1
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.
From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities
Apr 5, 2026Agam Goyal, Yian Wang, Eshwar Chandrasekharan +1Counterfactual Evaluation Large Language Model-Based Social Simulation
University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Using Feasible Action-Space Reduction by Groups to fill Causal Responsibility Gaps in Spatial Interactions
Feb 25, 2026Ashwin George, Vassil Guenov, Arkady Zgonnikov +2Causal Attribution Actual Causality
Delft University of Technology, The Netherlands · Centre for Meaningful Human Control
On the Semantics of Primary Cause in Hybrid Dynamic Domains
Feb 16, 2026Shakil M. Khan, Asim Mehmood, Sandra ZillesActual Causality Logical Reasoning
University of Regina Regina, Saskatchewan, Canada
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

Oct 26, 2026J/K move · Enter open · S save

A Hybrid Framework for Explainable Aviation Risk: Causal Chain Extraction from Safety Reports with Physics-Based Failure Quantification and Interventional Analysis

Yutian Pang, Yifang Tian, Jiacheng Xie, Xinyu Zhao+3

Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA. · Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada. · Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.+2

Aviation Safety Causal Inference Actual Causality

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Aviation authorities hold decades of accident reports whose narratives record how investigators reconstructed each accident. Computational work on these archives has mostly sorted reports into fixed taxonomies. In this work, we propose a hybrid framework in which a text-based corpus identifies the leading factors of a failure mode. Physics-based models then quantify how likely that failure is at a stated operating point. Ordered causal chains extracted from 56,202 American accident reports with a local large language model aggregate into a causation knowledge graph. On an evaluation sample of 2,000 reports, the chains recover 90.8% of investigator-flagged causes. Each physical factor whose drivers the record carries receives an occurrence model calibrated to the accident population. A leaky noisy-OR fitted to the corpus combines the factors into a failure mode probability that supports intervention. For engine power loss, the carburetor icing model separates 1,205 icing accidents from other accidents using temperature and dewpoint alone, reaching 0.61 in area under the receiver operating characteristic curve against a density ratio ceiling of 0.64. Routine weather hours give no valid control, because flying concentrates in fair weather. On later years the physics adds 0.03 to that area for occurrence and nothing for severity, so its value lies in interpretability. On four named accidents, abduction gives a probability of necessity of 0.29 to 0.50 for the heat omission, under monotonicity. The Fast Causal Inference algorithm does not license the largest backdoor adjustment on the learned networks.