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 time-varying 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 the leading causes of Arctic sea ice melt.

翻译：北极变暖现象（即北极放大效应）受到多个大气和海洋驱动因素的影响，但其潜在热力学成因的细节仍不明确。采用固定处理效应策略推断大气过程对海冰消融的因果作用，会导致反事实估计脱离实际。此类模型还因时变混杂因素而存在偏差。为应对这些挑战，我们提出TCINet——一种基于循环神经网络的连续处理时间序列因果推断模型。通过合成数据与观测数据的实验证明，本研究能显著提升对北极海冰消融主要诱因的量化能力。