Results 291 to 300 of about 1,819,138 (329)

Multi-Agent Causal Discovery Using Large Language Models

arXiv.org
Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical causal discovery (SCD) methods rely solely on observational data and ignore the contextual information ...
Hao Duong Le, X. Xia, Zhan Chen
semanticscholar   +1 more source

Sample, estimate, aggregate: A recipe for causal discovery foundation models

Trans. Mach. Learn. Res.
Causal discovery, the task of inferring causal structure from data, has the potential to uncover mechanistic insights from biological experiments, especially those involving perturbations.
Menghua Wu, Yujia Bao, R. Barzilay, T. Jaakkola   +3 more
semanticscholar   +1 more source

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. For generative causes this would be a context with a low base rate of
Barberia, I., Baetu, I., Sansa, J., Baker, A.   +3 more
openaire   +3 more sources

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes

International Conference on Learning Representations
Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance. Such processes can often be accurately modeled via stochastic differential equations
Georg Manten, Cecilia Casolo, E. Ferrucci, S. W. Mogensen, C. Salvi, Niki Kilbertus   +5 more
semanticscholar   +1 more source

Federated Causal Discovery from Heterogeneous Data

International Conference on Learning Representations
Conventional causal discovery methods rely on centralized data, which is inconsistent with the decentralized nature of data in many real-world situations.
Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guan-Hong Chen, Tongliang Liu, Bin Gu, Kun Zhang   +7 more
semanticscholar   +1 more source

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

CLEaR
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited.
Philippe Brouillard, C. Squires, Jonas Wahl, K. Kording, K. Sachs, Alexandre Drouin, Dhanya Sridhar   +6 more
semanticscholar   +1 more source

Large Language Models for Constrained-Based Causal Discovery

arXiv.org
Causality is essential for understanding complex systems, such as the economy, the brain, and the climate. Constructing causal graphs often relies on either data-driven or expert-driven approaches, both fraught with challenges.
Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Díaz, Vasileios Sitokonstantinou, G. Camps-Valls   +4 more
semanticscholar   +1 more source

A Meta-Learning Approach to Bayesian Causal Discovery

International Conference on Learning Representations
Discovering a unique causal structure is difficult due to both inherent identifiability issues, and the consequences of finite data. As such, uncertainty over causal structures, such as those obtained from a Bayesian posterior, are often necessary for ...
Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk   +3 more
semanticscholar   +1 more source

Relational Blocking for Causal Discovery

Proceedings of the AAAI Conference on Artificial Intelligence, 2011
Blocking is a technique commonly used in manual statistical analysis to account for confounding variables. However, blocking is not currently used in automated learning algorithms. These algorithms rely solely on statistical conditioning as an operator to identify conditional independence. In this work, we present relational blocking
Matthew J. Rattigan, Marc E. Maier, David D. Jensen   +2 more
openaire   +1 more source

On the Reliability of Large Language Models for Causal Discovery

Annual Meeting of the Association for Computational Linguistics
This study investigates the efficacy of Large Language Models (LLMs) in causal discovery. Using newly available open-source LLMs, OLMo and BLOOM, which provide access to their pre-training corpora, we investigate how LLMs address causal discovery through
Tao Feng, Lizhen Qu, Niket Tandon, Zhuang Li, Xiaoxi Kang, Gholamreza Haffari   +5 more
semanticscholar   +1 more source