  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.
