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. Further, the complex non-linearity in Earth science data makes it infeasible to perform causal inference using existing marginal structural techniques. In order to tackle these challenges, we propose TCINet - time-series causal inference model to infer causation under continuous treatment using recurrent neural networks and a novel probabilistic balancing technique. 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, further paving paths for causal inference in observational Earth science.

翻译：北极变暖（亦称北极放大效应）由多种大气与海洋驱动因素共同导致，但其潜在热力学成因的细节尚不明确。采用固定处理效应策略推断大气过程对海冰融化的因果效应会产生不切实际的反事实估计，此类模型还易因时变混杂因素而产生偏差。此外，地球科学数据中存在的复杂非线性特征，使得运用现有边缘结构技术进行因果推断不可行。为应对这些挑战，我们提出TCINet——一种基于循环神经网络与新型概率平衡技术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表明，本研究可显著提升量化北极海冰融化主导成因的能力，为地球科学观测数据的因果推断开辟新路径。