Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference

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dc.contributor.authorAli, Sahara
dc.contributor.authorFaruque, Omar
dc.contributor.authorHuang, Yiyi
dc.contributor.authorGani, Md. Osman
dc.contributor.authorSubramanian, Aneesh
dc.contributor.authorShchlegel, Nicole-Jienne
dc.contributor.authorWang, Jianwu
dc.date.accessioned2023-04-06T18:01:11Z
dc.date.available2023-04-06T18:01:11Z
dc.date.issued2023-03-14
dc.description22nd IEEE International Conference on Machine Learning and Applications (ICMLA) 2023
dc.description.abstractThe warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers, however, the details of its underlying thermodynamic causes are still unknown. Inferring the causal effects of atmospheric processes on sea ice melt using fixed treatment effect strategies leads to unrealistic counterfactual estimations. Such models are also prone to bias due to timevarying confoundedness. In order to tackle these challenges, we propose TCINet - time-series causal inference model to infer causation under continuous treatment using recurrent neural networks. Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt.en_US
dc.description.sponsorshipThis work is supported by NSF grant: HDR Institute: HARP - Harnessing Data and Model Revolution in the Polar Regions (OAC-2118285). We thank Dr. Yiyi Huang (NASA Langley Research Lab) for her assistance in introducing the dataset.en_US
dc.description.urihttps://arxiv.org/abs/2303.07122en_US
dc.format.extent10 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m20hdn-svpi
dc.identifier.urihttps://doi.org/10.48550/arXiv.2303.07122
dc.identifier.urihttp://hdl.handle.net/11603/27429
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleQuantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inferenceen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0001-9962-358X
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170

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