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...
| Main Authors: | , , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
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Maryland Shared Open Access Repository
2023
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.13016/m20hdn-svpi https://mdsoar.org/handle/11603/27429 |
| Summary: | 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. ... |
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