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• W3217137954 abstract "Thanks to technological advances leading to near-continuous time observations, emerging multivariate point process data offer new opportunities for causal discovery. However, a key obstacle in achieving this goal is that many relevant processes may not be observed in practice. Naïve estimation approaches that ignore these hidden variables can generate misleading results because of the unadjusted confounding. To plug this gap, we propose a deconfounding procedure to estimate high-dimensional point process networks with only a subset of the nodes being observed. Our method allows flexible connections between the observed and unobserved processes. It also allows the number of unobserved processes to be unknown and potentially larger than the number of observed nodes. Theoretical analyses and numerical studies highlight the advantages of the proposed method in identifying causal interactions among the observed processes." @default.
• W3217137954 created "2021-12-06" @default.
• W3217137954 creator A5056101043 @default.
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• W3217137954 date "2021-12-01" @default.
• W3217137954 modified "2023-10-16" @default.
• W3217137954 title "Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes" @default.
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• W3217137954 doi "https://doi.org/10.3390/e23121622" @default.
• W3217137954 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34945928" @default.
• W3217137954 hasPublicationYear "2021" @default.
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