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Nonlinear Causal Discovery with Confounders

arXiv (Cornell University)(2023)10.48550/arxiv.2302.03178Source: DataRank Database

Nonlinear Causal Discovery with Confounders is a research paper published in arXiv (Cornell University) (2023). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (7 grants)OA: greenTopics (27)IDs (PubMed, OpenAlex)SDGs (1)

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Authors (3)

Xiaotong Shen ORCID,Wei Pan ORCID,Chunlin Li ORCID

Live enrichment

PubMed:30945415 OpenAlex:https://openalex.org/W4319653460

National Institutes of Health

Grant: 1R01AG069895-01

Deep Learning with Neuroimaging Genetic Data for Alzheimer's Disease

National Institutes of Health

Grant: 5R01GM126002-04

Estimation and Inference of Gene Regulatory Networks

National Institutes of Health

Grant: 1R01AG074858-01

Estimation and inference in directed acyclic graphical models for biological networks

National Institutes of Health

Grant: 5U01AG073079-03

Causal and integrative deep learning for Alzheimer's disease genetics

National Institutes of Health

Grant: 5R01AG065636-02

Discovering causal genes, brain regions and other risk factors for Alzheimer'a disease

National Science Foundation

Grant: 1952539

FRG: Collaborative Research: Generative Learning on Unstructured Data with Applications to Natural Language Processing and Hyperlink Prediction

National Institutes of Health

Grant: 9R01GM113250-11A1

Statistical Methods for Genomic Data

greenLicense: cc-by-nc-nd

repositoryhttps://arxiv.org/pdf/2302.03178

repositoryhttp://arxiv.org/abs/2302.03178

repositoryhttps://doi.org/10.48550/arxiv.2302.03178

https://doi.org/10.1080/01621459.2023.2179490

https://dx.doi.org/10.6084/m9.figshare.22094649.v1

Fields of Study

Bayesian Modeling and Causal InferenceComputational Drug Discovery MethodsMethodology (stat.ME)FOS: Computer and information sciences0202 electrical engineering, electronic engineering, information engineering02 engineering and technology0101 mathematics01 natural sciences

Keywords

IdentifiabilityConfoundingComputer scienceDirected acyclic graphCausal inferenceNonlinear systemCausal structureCausal modelMachine learningEconometricsArtificial intelligenceAlgorithmTheoretical computer scienceData miningMathematicsStatisticsMethodology (stat.ME)FOS: Computer and information sciencesStatistics - Methodology

Sustainable Development Goals

SDG 0: Partnerships for the goals