Ali, SaharaFaruque, OmarHuang, YiyiGani, Md. OsmanSubramanian, AneeshShchlegel, Nicole-JienneWang, Jianwu2023-04-062023-04-062023-03-14https://doi.org/10.48550/arXiv.2303.07122http://hdl.handle.net/11603/2742922nd IEEE International Conference on Machine Learning and Applications (ICMLA) 2023The 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.10 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Attribution 4.0 International (CC BY 4.0)Text