Local Causal Structures

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Adversarial Causal Intervention Falsification
Aug 5, 2026Mojtaba EslamiLocal Causal Structures Causal
University of Calgary
Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs
Aug 4, 2026Jannick Strobel, Muqsit Azeem, Stefan LeueLocal Causal Structures Explainable AI Methods
University of Konstanz
Causal Discovery with Inverted Self-attention for Multivariate Time Series
Jul 30, 2026Yusen Liu, Yong Wang, Yifan Yin +3Causal Discovery Methods Multivariate Time-Series
School of Computer Science, University of Technology Sydney, Sydney, Australia · TianFu YongXing Laboratory, Chengdu, China · Faculty of Data Science, City University of Macau, Macau, China +1
Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations
Jul 24, 2026Sergei V. KalininLocal Causal Structures Directed Acyclic Graph
Department of Materials Science and Engineering, University of Tennessee, Knoxville
Indexing: the Beginning and the End
Jul 24, 2026Alexander Kozachinskiy, Vicente Opazo, Felipe UrrutiaDeep Learning Architectures Information Bottleneck
CENIA · Pontifical Catholic University of Chile
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Jul 23, 2026Kilian Rueckschloss, Felix WeitkaemperCausal Modeling Local Causal Structures
Eberhard Karls Universitaet Tuebingen · University of Tübingen Tübingen, Germany · German University of Digital Science +1
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Jul 22, 2026Zheng Li, Hao Zhang, Ruxin Wang +3Causal Discovery Methods Local Causal Structures
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. · School of Computer Science, Guangdong University of Technology, Guangzhou, China. · Peng Cheng Laboratory, Shenzhen, China. +3
StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure
Jul 13, 2026Wenyi Wu, Sibo Zhu, Kun Zhou +3Long-Horizon Agents Agent Loop
University of California, San Diego · 2Aether AI Lab · §Work done during an internship at Aether AI.
Words as Difference Makers: How Large Language Models Determine Causal Structure in Text
Jun 21, 2026Wolfgang PietschLocal Causal Structures Causal
tum
FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift
Jun 20, 2026Kaijie Chen, Alex Johnson, Maria Garcia +2Federated Learning Regularization
Mindlab
Pattern Selectivity is Not Task-Causal Structure: A Cross-Architecture Mechanistic Study of Composed-Task Circuits in 1B-Class Language Models
Jun 3, 2026Yongzhong XuAblation Language Modeling
The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs
May 30, 2026Zihan Chen, Yiming Zhang, Wenxiang Geng +2LLM Reasoning Strategies Process Reward Model
1HFIPS, Chinese Academy of Sciences · University of Science and Technology of China
Causal Past Logic for Runtime Verification of Distributed LLM Agent Workflows
May 20, 2026Benedikt BolligRuntime Enforcement Local Causal Structures
Universit´e Paris-Saclay, CNRS, ENS Paris-Saclay, LMF, Gif-sur-Yvette, France
CasualSynth: Generating Structurally Sound Synthetic Data
May 17, 2026Zehua Cheng, Wei Dai, Jiahao Sun +1Synthetic Data Local Causal Structures
Department of Computer Science, University of Oxford · FLock.io United Kingdom · Institute of Logic and Computation, TU Wien
Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios
May 7, 2026Peizheng Yan, Yu Zhao, Liang Xie +3Video Understanding Fine-Grained Video Understanding
Tianjin Key Lab of Intelligent Unmanned Swarm Tech & System Tianjin University Tianjin, China · Institute of Computing and Intelligence Harbin Institute of Technology (Shenzhen) Shenzhen, China · Tianjin Artificial Intelligence Innovation Center Tianjin, China +2
M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
May 1, 2026J. Jake Nichol, Michael Weylandt, G. Matthew Fricke +2Causal Discovery Methods Local Causal Structures
Scientific Machine Learning, Center for Computing Research Sandia National Laboratories · Zicklin School of Business Baruch College, CUNY · Department of Computer Science University of New Mexico +2
A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population data
Apr 18, 2026Adela Bâra, Simona-Vasilica OpreaKnowledge Graphs Local Causal Structures
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania

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Adversarial Causal Intervention Falsification

Mojtaba Eslami

University of Calgary

Local Causal Structures Causal Adversarial Robustness

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Generative models can reproduce an observational distribution while encoding an incorrect causal structure. We study a sequential game in which a structural causal generator proposes observational and interventional distributions, while an adversarial experimentalist selects interventions intended to maximally falsify the generator. The discriminator is therefore not merely a real-versus-synthetic classifier: it is indexed by an intervention and tests whether the generator reproduces the corresponding post-intervention law. We introduce Adversarial Causal Intervention Falsification (ACIF), formulate oracle and implementable versions of the game, and distinguish three objects that are often conflated: observational fit, interventional equivalence over an admissible query class, and point identification of a structural causal model. For finite model and intervention classes, we prove: (i) an exact reduction of the adversarial objective to a worst-intervention integral probability metric; (ii) identification up to interventional equivalence, with point identification under a separating intervention family; (iii) existence of mixed-strategy equilibria; (iv) finite-sample uniform convergence and margin-based model-selection guarantees; and (v) a logarithmic elimination guarantee for a disagreement-driven sequential design under a balanced-separation condition. We also give a complete linear-Gaussian example in which two observationally indistinguishable causal directions are separated by a single well-chosen intervention. The framework clarifies what an adversarial causal discriminator can and cannot certify, and provides a principled bridge between causal generative modeling, active causal discovery, and experimental design.

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