Results 281 to 290 of about 1,819,138 (329)

Large-Scale Hierarchical Causal Discovery via Weak Prior Knowledge

IEEE Transactions on Knowledge and Data Engineering
Causal discovery faces significant challenges as the number of hypotheses grows exponentially with the number of variables. This complexity becomes particularly daunting when dealing with large sets of variables.
Xiangyu Wang, Taiyu Ban, Lyuzhou Chen, Derui Lyu, Qinrui Zhu, Huanhuan Chen   +5 more
semanticscholar   +1 more source

Integrating Large Language Model for Improved Causal Discovery

IEEE Transactions on Artificial Intelligence, 2023
Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios.
Taiyu Ban, Lyuzhou Chen, Derui Lyu, Xiangyu Wang, Qinrui Zhu, Qiang Tu, Huanhuan Chen   +6 more
semanticscholar   +1 more source

Can Large Language Models Help Experimental Design for Causal Discovery?

arXiv.org
Designing proper experiments and selecting optimal intervention targets is a longstanding problem in scientific or causal discovery. Identifying the underlying causal structure from observational data alone is inherently difficult.
J. Li, Yongqiang Chen, Chenxi Liu, Qianyi Cai, Tongliang Liu, Bo Han, Kun Zhang, H. Xiong   +7 more
semanticscholar   +1 more source

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

Trans. Mach. Learn. Res.
In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is important for reasonable causal models reflecting the broad knowledge of domain experts, despite the challenges in the systematic ...
Masayuki Takayama, Tadahisa Okuda, Thong Pham, T. Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai   +6 more
semanticscholar   +1 more source

MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning

Neural Information Processing Systems
Video causal reasoning aims to achieve a high-level understanding of video content from a causal perspective. However, current video reasoning tasks are limited in scope, primarily executed in a question-answering paradigm and focusing on short videos ...
Tieyuan Chen, Huabin Liu, Tianyao He, Yihang Chen, Chaofan Gan, Xiao Ma, Cheng Zhong, Yang Zhang, Yingxue Wang, Hui Lin, Weiyao Lin   +10 more
semanticscholar   +1 more source

DISCOVERY OF CAUSALITY POSSIBILITIES

International Journal of Pattern Recognition and Artificial Intelligence, 2004
Determining causality has been a tantalizing goal throughout human history. Proper sacrifices to the gods were thought to bring rewards; failure to make suitable observations were thought to lead to disaster. Today, data mining holds the promise of extracting unsuspected information from very large databases.
openaire   +1 more source

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery

Neural Information Processing Systems, 2023
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However, computational challenges arise due to joint inference over combinatorial
Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong   +5 more
semanticscholar   +1 more source

Root Cause Analysis In Microservice Using Neural Granger Causal Discovery

AAAI Conference on Artificial Intelligence
In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site reliability engineers (SREs) to pinpoint the root cause due to the complex ...
Chen Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wenjie Peng   +4 more
semanticscholar   +1 more source

ALCM: Autonomous LLM-Augmented Causal Discovery Framework

arXiv.org
To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observational data. However, obtaining a complete and accurate causal graph poses
Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani   +4 more
semanticscholar   +1 more source

Large Language Models for Causal Discovery: Current Landscape and Future Directions

International Joint Conference on Artificial Intelligence
Causal discovery (CD) and Large Language Models (LLMs) have emerged as transformative fields in artificial intelligence that have evolved largely independently.
Guangya Wan, Yu-An Lu, Yuqi Wu, Mengxuan Hu, Sheng Li   +4 more
semanticscholar   +1 more source