Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
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Date
2023-03-14
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Attribution 4.0 International (CC BY 4.0)
Attribution 4.0 International (CC BY 4.0)
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Abstract
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.