Results 21 to 30 of about 1,964,226 (308)
Quantitative Causality, Causality-Aided Discovery, and Causal Machine Learning
Ocean-Land-Atmosphere Research, 2023 It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence algorithms, however, is challenged with its vagueness, nonquantitativeness, computational inefficiency, etc.
Xin‐Zhong Liang +2 more
openaire +2 more sources
Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach [PDF]
, 2020 Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions.
Carrara, A. +7 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 +6 more
semanticscholar +1 more source
Nonlinear causal discovery with confounders. [PDF]
J Am Stat Assoc, 2023 This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a
Li C, Shen X, Pan W.
europepmc +4 more sources
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 +4 more
semanticscholar +1 more source
Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis [PDF]
arXiv.org, 2023 ChatGPT has demonstrated exceptional proficiency in natural language conversation, e.g., it can answer a wide range of questions while no previous large language models can.
Ruibo Tu, Chao Ma, Cheng Zhang
semanticscholar +1 more source
Causal KL: Evaluating Causal Discovery
, 2021 26 ...
O'Donnell, Rodney T. +2 more
openaire +2 more sources
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 +4 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 +4 more
doaj +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 +6 more
semanticscholar +1 more source