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pro vyhledávání: '"Moosavi, Niloofar"'
Various methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data generating processes when high-dimensional nuisance models are estimated by post-model-selection or machine l
Externí odkaz:
http://arxiv.org/abs/2401.06564
Publikováno v:
Journal of Computational and Graphical Statistics, 2023
Convolutional neural networks (CNN) have been successful in machine learning applications. Their success relies on their ability to consider space invariant local features. We consider the use of CNN to fit nuisance models in semiparametric estimatio
Externí odkaz:
http://arxiv.org/abs/2301.11732
The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters
Publikováno v:
Statistical Science 38(1): 1-12, 2023
Important advances have recently been achieved in developing procedures yielding uniformly valid inference for a low dimensional causal parameter when high-dimensional nuisance models must be estimated. In this paper, we review the literature on unif
Externí odkaz:
http://arxiv.org/abs/2105.02071
Autor:
Ghasempour, Mohammad1 (AUTHOR) mohammad.ghasempour@umu.se, Moosavi, Niloofar1 (AUTHOR), de Luna, Xavier1 (AUTHOR)
Publikováno v:
Journal of Computational & Graphical Statistics. Apr-Jun2024, Vol. 33 Issue 2, p714-723. 10p.
Akademický článek
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Autor:
Moosavi, Niloofar
The objective of this thesis is to consider some challenges that arise when conducting causal inference based on observational data. High dimensionality can occur when it is necessary to adjust for many covariates, and flexible models must be used to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______264::50c97fac3f70d8fb43673d33bb1950d7
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-199258
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-199258
Akademický článek
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Autor:
Rahmati, Mehdi, Weihermüller, Lutz, Vanderborght, Jan, Pachepsky, Yakov A., Mao, Lili, Sadeghi, Seyed Hamidreza, Moosavi, Niloofar, Kheirfam, Hossein, Montzka, Carsten, Van Looy, Kris, Toth, Brigitta, Hazbavi, Zeinab, Yamani, Wafa Al, Albalasmeh, Ammar A., Alghzawi, Ma'in Z., Angulo-Jaramillo, Rafael, Dantas Antonino, Antônio Celso, Arampatzis, George, Armindo, Robson André, Asadi, Hossein
Publikováno v:
Earth System Science Data Discussions; 2018, p1-42, 42p