Results 251 to 260 of about 1,964,226 (308)

Choosing Optimal Causal Backgrounds for Causal Discovery

Quarterly Journal of Experimental Psychology, 2010
In two experiments, we studied the strategies that people use to discover causal relationships. According to inferential approaches to causal discovery, if people attempt to discover the power of a cause, then they should naturally select the most informative and unambiguous context.
Barberia, I., Baetu, I., Sansa, J., Baker, A.   +3 more
openaire   +3 more sources

LLM-Driven Causal Discovery via Harmonized Prior

IEEE Transactions on Knowledge and Data Engineering
Traditional domain-specific causal discovery relies on expert knowledge to guide the data-based structure learning process, thereby improving the reliability of recovered causality. Recent studies have shown promise in using the Large Language Model (LLM)
Taiyu Ban, Lyuzhou Chen, Derui Lyu, Xiangyu Wang, Qinrui Zhu, Huanhuan Chen   +5 more
semanticscholar   +1 more source

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

Symmetry-Aware Transformers for Asymmetric Causal Discovery in Financial Time Series

Symmetry
Financial markets exhibit fundamental asymmetries in temporal causality, where policy interventions create asymmetric transmission patterns that traditional symmetric modeling approaches fail to capture. This work introduces a mathematical framework that
Wenxia Zheng, Wenhe Liu
semanticscholar   +1 more source

Online causal discovery

9th IEEE International Conference on Cognitive Informatics (ICCI'10), 2010
The standard causal discovery assumes that all variables are available from the beginning. In this paper, we consider an untouched scenario in which not all variables are available in advance. We call this scenario online causal discovery which assumes that the target of interest is given in advance while the other variables are unknown.
Kui Yu, Xindong Wu, Hao Wang
openaire   +2 more sources

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

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

Assumption violations in causal discovery and the robustness of score matching

Neural Information Processing Systems, 2023
When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recover the causal structure, exploiting the statistical properties of their ...
Francesco Montagna, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti, L. Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello   +7 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 ...
Cheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wenjie Peng   +4 more
semanticscholar   +1 more source

Causal Discovery from Temporal Data

Knowledge Discovery and Data Mining, 2023
Temporal data representing chronological observations of complex systems can be ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many tasks have been studied for mining temporal data and offered significant value ...
Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li, Jingping Bi, Lun Du, Jin Wang   +6 more
semanticscholar   +1 more source