Results 271 to 280 of about 1,964,226 (308)
Some of the next articles are maybe not open access.
Causal Discovery with Fewer Conditional Independence Tests
International Conference on Machine LearningMany questions in science center around the fundamental problem of understanding causal relationships. However, most constraint-based causal discovery algorithms, including the well-celebrated PC algorithm, often incur an exponential number of ...
Kirankumar Shiragur +2 more
semanticscholar +1 more source
A Meta-Learning Approach to Bayesian Causal Discovery
International Conference on Learning RepresentationsDiscovering 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 +3 more
semanticscholar +1 more source
Causal Discovery in Semi-Stationary Time Series
Neural Information Processing SystemsDiscovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas, such as retail sales, transportation systems, and medical science. Here,
Shanyun Gao +4 more
semanticscholar +1 more source
Local Causal Discovery Without Causal Sufficiency
Proceedings of the AAAI Conference on Artificial IntelligenceLocal causal discovery is crucial for revealing the causal relationships between specific variables from data. Existing local causal discovery algorithms are designed under the assumption of causal sufficiency, which states that there are no latent common causes for two or more of the observed variables in data.
Zhaolong Ling +7 more
openaire +1 more source
Information-Theoretic Causal Discovery
2021It is well-known that correlation does not equal causation, but how can we infer causal relations from data? Causal discovery tries to answer precisely this question by rigorously analyzing under which assumptions it is feasible to infer causal networks from passively collected, so-called observational data. Particularly, causal discovery aims to infer
openaire +2 more sources
Ensembling MML Causal Discovery
2004This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with ...
Honghua Dai, Gang Li, Zhi-Hua Zhou
openaire +1 more source
DISCOVERY OF CAUSALITY POSSIBILITIES
International Journal of Pattern Recognition and Artificial Intelligence, 2004Determining 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
Causal Discovery and Causal Effect Identification
Understanding how changes in an environment affect outcomes is at the core of causal inference. Unlike standard prediction tasks that capture associations between variables, causal inference aims to reveal what happens when we intervene in a system. In this field, there is a common pipeline consisting of two main problems: causal discovery and causal ...openaire +1 more source
Topological materials discovery from crystal symmetry
Nature Reviews Materials, 2021Benjamin J Wieder +2 more
exaly
A guide to comprehensive phosphor discovery for solid-state lighting
Nature Reviews Materials, 2023Shruti Hariyani +2 more
exaly