Results 31 to 40 of about 1,819,138 (329)

Causal Discovery from Temporal Data: An Overview and New Perspectives [PDF]

ACM Computing Surveys, 2023
Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, finance, healthcare, and climatology. Analyzing the underlying structures,
Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li, Jingping Bi   +4 more
semanticscholar   +1 more source

The KDD 2021 Workshop on Causal Discovery (CD2021)

, 2021
As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled ...
Cooper, Gregory, Bareinboim, Elias, Li, Jiuyong, Liu, Huan, Thuc Duy Le, Triantafyllou, Sofia, Kiyavash, Negar   +6 more
core   +2 more sources

CUTS+: High-dimensional Causal Discovery from Irregular Time-series [PDF]

AAAI Conference on Artificial Intelligence, 2023
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios.
Yuxiao Cheng, Lianglong Li, Tingxiong Xiao, Zongren Li, J. Suo, K. He, Qionghai Dai   +6 more
semanticscholar   +1 more source

Markov Boundary Discovery with Ridge Regularized Linear Models

Journal of Causal Inference, 2016
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association ...
Strobl Eric V., Visweswaran Shyam
doaj   +1 more source

TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery [PDF]

Knowledge Discovery and Data Mining
Robust causal discovery in time series datasets depends on reliable benchmark datasets with known ground-truth causal relationships. However, such datasets remain scarce, and existing synthetic alternatives often overlook critical temporal properties ...
Muhammad Hasan Ferdous, Emam Hossain, Md. Osman Gani   +2 more
semanticscholar   +1 more source

CUTS: Neural Causal Discovery from Irregular Time-Series Data [PDF]

International Conference on Learning Representations, 2023
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing
Yuxiao Cheng, Runzhao Yang, Tingxiong Xiao, Zongren Li, J. Suo, K. He, Qionghai Dai   +6 more
semanticscholar   +1 more source

Causal Discovery of Flight Service Process Based on Event Sequence

Journal of Advanced Transportation, 2021
The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains ...
Qian Luo, Lin Zhang, Zhiwei Xing, Huan Xia, Zhao-Xin Chen   +4 more
doaj   +1 more source

Discovering Ecological Relationships in Flowing Freshwater Ecosystems

Frontiers in Ecology and Evolution, 2022
Knowledge of ecological responses to changes in the environment is vital to design appropriate measures for conserving biodiversity. Experimental studies are the standard to identify ecological cause-effect relationships, but their results do not ...
Konrad P. Mielke, Konrad P. Mielke, Aafke M. Schipper, Aafke M. Schipper, Tom Heskes, Michiel C. Zijp, Leo Posthuma, Leo Posthuma, Mark A. J. Huijbregts, Tom Claassen   +9 more
doaj   +1 more source

Learning latent functions for causal discovery

Machine Learning: Science and Technology, 2023
Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions.
Emiliano Díaz, Gherardo Varando, J Emmanuel Johnson, Gustau Camps-Valls   +3 more
doaj   +1 more source

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise [PDF]

CLEaR, 2023
Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian ...
Francesco Montagna, Nicoletta Noceti, L. Rosasco, Kun Zhang, Francesco Locatello   +4 more
semanticscholar   +1 more source