Causal Interpretability

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6 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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Latest papers 69

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CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners
Jun 12, 2026Zikun GuoEnd-to-End Autonomous Driving Autonomous Driving Planning
School of Intelligent Driving, Xinwei Institute of Artificial Intelligence · Geely Central Research Institute
When Does Routing Become Interpretable? Causal Probes on Block Attention Residuals
Jun 11, 2026Aydin JavadovTransformer Interpretability Attention Residual Connections
ETH Zurich
Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models
Jun 10, 2026Darpan Aswal, Thomas Palmeira Ferraz, Yongxin Zhou +1Continuous Latent Reasoning Causal Interpretability
Université Grenoble Alpes, CNRS, Grenoble INP, LIG · Université Paris-Saclay · NAVER LABS Europe
From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models
Jun 9, 2026Leonard Engmann, Christian Medeiros Adriano, Holger GieseMixture-of-Experts Pruning LLM Auditing
Hasso Plattner Institute, University of Potsdam, Germany.
Many Circuits, One Mechanism: Input Variation and Evaluation Granularity in Circuit Discovery
Jun 4, 2026Alireza Bayat Makou, Jingcheng Niu, Subhabrata Dutta +1Circuit Discovery Mechanistic Interpretability
UKP Lab, Technical University of Darmstadt · National Research Center for Applied Cybersecurity ATHENE
LLM Explainability with Counterfactual Chains and Causal Graphs
Jun 4, 2026Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor +1LLM Reasoning with Graphs LLM Interpretability
Faculty of Data and Decision Sciences, Technion · IBM Research
EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
Jun 4, 2026Sota AsanumaStructural Causal Models Causal Structure Learning
SoftBank Corp.
Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety
Jun 3, 2026Abhinaw Priyadershi, Mandar Pitale, Jelena Frtunikj +1AI Assurance Autonomous Driving Safety Evaluation
NVIDIA Corporation, Santa Clara, CA, USA · NVIDIA GmbH, Munich, Germany
When and How Long? The Readout-Mediator Angle in Temporal Reasoning
May 27, 2026Shreyas Fadnavis, Praitayini Kanakaraj, Felix WyssTemporal Reasoning in Language Models Temporal Reasoning
Bioscope AI
MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability
May 25, 2026Barsat KhadkaTransformer Interpretability Circuit Discovery
School of Computing Sciences and Engineering The University of Southern Mississippi
Causal Tongue-Tie: LLMs Can Encode Causal Direction, But Their Yes/No Outputs Fail to Express
May 25, 2026Ziyi Ding, Xiao-Ping ZhangLLM Evaluation Causal Reasoning in Language Models
Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen, China
Represented Is Not Computed: A Causal Test of Candidate Algorithmic Intermediates in a Transformer
May 21, 2026Ishita Darade, Sushrut ThoratNumerical Reasoning in Language Models Transformer Interpretability
MKSSS’s Cummins College of Engineering for Women, Pune 411052, India · Institute of Cognitive Science, Osnabrück University, Osnabrück 49069, Germany
From Correlation to Cause: A Five-Stage Methodology for Feature Analysis in Transformer Language Models
May 21, 2026Caleb MunigetyTransformer Interpretability Causal Reasoning in Language Models
Independent Researcher
Geometry-Adaptive Explainer for Faithful Dictionary-Based Interpretability under Distribution Shift
May 21, 2026Sungjun Lim, Heedong Kim, Andrew Lee +1Distribution Shift Robustness OOD Generalization
aYonsei University · bHarvard University
From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach
May 20, 2026Nura Aljaafari, Danilo S. Carvalho, Andre FreitasCircuit Discovery Mechanistic Interpretability
Department of Computer Science, University of Manchester, United Kingdom · CRUK National Biomarker Centre, University of Manchester, United Kingdom · Idiap Research Institute, Switzerland
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
May 19, 2026Guangzhi Xiong, Qiao Jin, Sanchit Sinha +2Medical VQA VLM Interpretability
University of Virginia · National Institutes of Health
Learning Quantifiable Visual Explanations Without Ground-Truth
May 18, 2026Amritpal Singh, Andrey Barsky, Mohamed Ali Souibgui +2Perturbation-Based Feature Attribution Causal Interpretability
Computer Vision Center, Barcelona, Spain · Autonomous University of Barcelona, Spain
OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
May 18, 2026Chiara Maria Russo, Simone Carnemolla, Simone Palazzo +3Explainable Artificial Intelligence Concept-Based Explanations
University of Catania Catania, Italy
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
Causal Dimensionality of Transformer Representations: Measurement, Scaling, and Layer Structure
May 9, 2026Nilesh Sarkar, Dawar Jyoti DekaTransformer Interpretability Sparse Autoencoders
Erdős AI Lab
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
May 8, 2026Houssam Zenati, Arthur GrettonSemiparametric Inference Causal Effect Estimation
1Gatsby Computational Neuroscience Unit, University College London
Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims
May 8, 2026Zezheng Lin, Fengming LiuMechanistic Interpretability Causal Identifiability
Preprint.
Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation
May 8, 2026Amy Rafferty, Rishi Ramaesh, Ajitha RajanChest X-Ray Classification Concept Bottleneck Models
University of Edinburgh, UK · NHS Lothian, UK
PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
May 7, 2026Jonathn Chang, Arya Datla, Ziv GoldfeldMechanistic Interpretability Causal Abstraction
Cornell University
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
May 7, 2026Ronaldo Canizales, Divya Gopinath, Corina Păsăreanu +1Abductive Reasoning Concept-Based Explanations
Colorado State University, Fort Collins CO, USA · KBR Inc., NASA Ames, Moffett Field CA, USA · Carnegie Mellon University, Pittsburgh PA, USA
Where Reliability Lives in Vision-Language Models: A Mechanistic Study of Attention, Hidden States, and Causal Circuits
May 5, 2026Logan Mann, Ajit Saravanan, Ishan Dave +4VLM Evaluation VLM Interpretability
UC Santa Barbara · UC Berkeley · NVIDIA +2
Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction
May 4, 2026Li Puyin, Jiyuan Tan, Ahmad Jabbar +2Mechanistic Interpretability Causal Abstraction
♢Stanford University · ♠Goodfire
ISAAC: Auditing Causal Reasoning in Deep Models for Drug-Target Interaction
May 3, 2026Barbara Tarantino, Sun Kim, Yijingxiu Lu +1Reasoning Evaluation Causal Interpretability
Department of Economics and Management, University of Pavia, Pavia, Italy · Department of Computer Science and Engineering, Seoul National University, Gwanak-gu 08826, Korea.
Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models
May 1, 2026Hanxin Zhang, Mingshuo Xu, Abdulqader Dhafer +3VLM Robustness VLM Interpretability
1DANiLab, University of Leicester, UK · School of Computing and Mathematical Sciences, University of Leicester, UK · School of Metallurgy and Materials, University of Birmingham, UK
Causal Learning with Neural Assemblies
Apr 29, 2026Evangelia Kopadi, Dimitris KallesNeural Processes Causal Discovery
Hellenic Open University Greece

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CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners

Zikun Guo

School of Intelligent Driving, Xinwei Institute of Artificial Intelligence · Geely Central Research Institute

End-to-End Autonomous Driving Autonomous Driving Planning Causal Interpretability Shortcut Learning

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End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with expert actions (a roadside object, a building facade) with driving decisions, rather than the variables that causally determine them. Such causal confusion silently compromises reliability in long-tail scenarios, and it is difficult to detect, because prevailing open-loop metrics (L2 displacement and collision rate) are dominated by ego status and do not indicate whether a planner depends on spurious cues. Existing remedies based on causal-intervention training require retraining large models and cannot audit a planner that is already deployed. We present CADET, a training-free framework that audits, benchmarks, and repairs spurious reliance in pretrained E2E planners without any parameter update.

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