Actual Causality

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 10

All topics

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.
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

Sep 3, 2026, cs.AIJ/K move · Enter open · S save

A Computationally Feasible Framework for Causal Probabilistic Explanation

Rafal Urbaniak, Sam Witty, Daniel Waxman, Andy Zane+6

Basis Research Institute · Sorbus AI · Massachusetts Institute of Technology+3

Structural Causal Models Actual Causality Causal Attribution Causal Inference Causal Explanation

Open paper AskPDF arXiv

Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis. Existing tools split into two camps. The theory of actual causality (AC) gives principled verdicts, but only for toy-sized models, because computing them requires enumerating counterfactual scenarios. Scalable attribution methods like SHAP (or even causal SHAP) at least partially ignore the causal structure that generated the data, and can give answers that conflict with a careful causal analysis. We close this gap with Probabilistic Causal Impact (PCI). PCI builds on actual causality and on Pearl's notions of probability of necessity and sufficiency, but recasts the question of explainability as an estimation problem on a probabilistic causal model that is easily approximated via Monte Carlo. By specifying a distribution over "candidate explanations," a distribution over counterfactual values, and a scoring function, PCI provides tractable, causally grounded, graded explanations, generalizing AC and Pearl's probability of causation as degenerate cases. We evaluate PCI in synthetic and real-world examples, spanning consistency checks with AC, scaling experiments, complex continuous-valued dynamical systems, and a real-world deployed causal machine learning model trained on millions of datapoints.

Momentum

Related topics

Latest papers 10