Results 301 to 310 of about 1,819,138 (329)

Incremental Causal Discovery and Visualization

Proceedings of the Workshop on Interactive Data Mining, 2019
Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available ...
Anders Holst, Sepideh Pashami, Juhee Bae
openaire   +1 more source

Causal Discovery with Fewer Conditional Independence Tests

International Conference on Machine Learning
Many 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, Jiaqi Zhang, Caroline Uhler   +2 more
semanticscholar   +1 more source

Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

arXiv.org
Causal analysis has become an essential component in understanding the underlying causes of phenomena across various fields. Despite its significance, existing literature on causal discovery algorithms is fragmented, with inconsistent methodologies, i.e.,
Wenjing Niu, Zijun Gao, Liyang Song, Lingbo Li   +3 more
semanticscholar   +1 more source

A new causal discovery heuristic

Annals of Mathematics and Artificial Intelligence, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Steven D. Prestwich, S. Armagan Tarim, Ibrahim Özkan   +2 more
openaire   +1 more source

Ensembling MML Causal Discovery

2004
This 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 0001, Gang Li 0009, Zhi-Hua Zhou   +2 more
openaire   +1 more source

Causal Discovery in Semi-Stationary Time Series

Neural Information Processing Systems
Discovering 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, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu   +4 more
semanticscholar   +1 more source

Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior

Knowledge Discovery and Data Mining
Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn ...
Pingchuan Ma, Rui Ding, Qiang Fu, Jiaru Zhang, Shuai Wang, Shi Han, Dongmei Zhang   +6 more
semanticscholar   +1 more source

A Survey on Causal Discovery

2022
Wenxiu Zhou, Qingcai Chen
openaire   +1 more source

Information-Theoretic Causal Discovery

2021
It 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   +3 more sources

D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery

ACM Computing Surveys, 2023
Matthew J Vowels, Necati Cihan CAMGÖZ, Richard Bowden   +2 more
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