arXiv AI

Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs

arXiv:2608. 03772v1 Announce Type: new Abstract: Explaining the predictions of neural networks is a central challenge in trustworthy AI.

arXiv Machine Learning

Jun 25

CauScale: Neural Causal Discovery at Scale

arXiv:2602. 08629v2 Announce Type: replace Abstract: Causal discovery is essential for advancing data-driven fields such as scientific AI and data analysis, yet existing approaches face significant time- and space-efficiency bottlenecks when scaling to large graphs.

By Bo Peng, Sirui Chen, Jiaguo Tian, Yu Qiao, Chaochao Lu

rag

arXiv AI

Jun 4

Binary Spiking Neural Networks as Causal Models

arXiv:2604. 27007v2 Announce Type: replace Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior.

By Aditya Kar (CNRS, IRIT), Emiliano Lorini (CNRS, IRIT), Timoth\'ee Masquelier (CNRS, CERCO UMR5549)

arXiv AI

Jul 3

Causal Explanations for Image Classifiers

arXiv:2411. 08875v4 Announce Type: replace Abstract: Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them.

By Hana Chockler, David A. Kelly, Daniel Kroening, Youcheng Sun

benchmarks

arXiv AI

Aug 6

Can Post-Training Transform LLMs into Causal Reasoners?

arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.

By Junqi Chen, Sirui Chen, Chaochao Lu

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 11

TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation

arXiv:2510. 27544v3 Announce Type: replace Abstract: Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoning, but underperform at counterfactual causal understanding and reasoning.

By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

llms agents benchmarks

arXiv Machine Learning

Jul 10

Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence

arXiv:2607. 08641v1 Announce Type: new Abstract: Over the last few years, there has been an increased interest in making machine learning models more interpretable.

By Yann Claes, Pierre Geurts, V\^an Anh Huynh-Thu

arXiv Machine Learning

Jul 31

EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.

By Lei Zan, Keli Zhang, Shifeng Xie, Jiale Zheng, Zehao Xiao, Zhiwei Dong, Ke Zhang, Ruichu Cai, Malik Tiomoko, Lujia Pan

llms benchmarks safety

arXiv AI

Jul 17

CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment

arXiv:2607. 14416v1 Announce Type: new Abstract: The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults.

By Rabimba Karanjai, Hemanth Madhavarao, Lei Xu, Weidong Shi

safety

arXiv AI

Jun 9

TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

arXiv:2510. 27544v2 Announce Type: replace Abstract: Temporal reasoning involves understanding how systems evolve over time through input-driven state transitions.

By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

llms fine-tuning benchmarks

arXiv AI

Jun 3

Causal Neural Probabilistic Circuits

arXiv:2603. 01372v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting the class label from the concept predictions.

By Weixin Chen, Han Zhao

benchmarks safety

arXiv Machine Learning

Jun 5

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.

By Hazhir Aliahmadi, Irina Babayan, Greg van Anders

arXiv AI

Aug 5

GENESIS: Towards Explainable Causal Discovery

arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.

By Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat, Harsh Vardhan Singh Chauhan, Niranjan Pedanekar

llms benchmarks

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