Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ...
The 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 strategi...
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Maryland Shared Open Access Repository
2023
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| Online Access: | https://dx.doi.org/10.13016/m20hdn-svpi https://mdsoar.org/handle/11603/27429 |
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ftdatacite:10.13016/m20hdn-svpi 2023-08-27T04:07:02+02:00 Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ... Ali, Sahara Faruque, Omar Wang, Jianwu 2023 https://dx.doi.org/10.13016/m20hdn-svpi https://mdsoar.org/handle/11603/27429 unknown Maryland Shared Open Access Repository Creative Commons Attribution 4.0 International Attribution 4.0 International (CC BY 4.0) This 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. https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CreativeWork article 2023 ftdatacite https://doi.org/10.13016/m20hdn-svpi 2023-08-07T14:24:23Z The 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. ... Article in Journal/Newspaper Arctic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
| language |
unknown |
| description |
The 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. ... |
| format |
Article in Journal/Newspaper |
| author |
Ali, Sahara Faruque, Omar Wang, Jianwu |
| spellingShingle |
Ali, Sahara Faruque, Omar Wang, Jianwu Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ... |
| author_facet |
Ali, Sahara Faruque, Omar Wang, Jianwu |
| author_sort |
Ali, Sahara |
| title |
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ... |
| title_short |
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ... |
| title_full |
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ... |
| title_fullStr |
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ... |
| title_full_unstemmed |
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference ... |
| title_sort |
quantifying causes of arctic amplification via deep learning based time-series causal inference ... |
| publisher |
Maryland Shared Open Access Repository |
| publishDate |
2023 |
| url |
https://dx.doi.org/10.13016/m20hdn-svpi https://mdsoar.org/handle/11603/27429 |
| geographic |
Arctic |
| geographic_facet |
Arctic |
| genre |
Arctic Sea ice |
| genre_facet |
Arctic Sea ice |
| op_rights |
Creative Commons Attribution 4.0 International Attribution 4.0 International (CC BY 4.0) This 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. https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
| op_doi |
https://doi.org/10.13016/m20hdn-svpi |
| _version_ |
1775347753203793920 |