Inferring causation from time series in Earth system sciences
Inferring causation from time series in Earth system sciences
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DOI：
10.1038/s41467-019-10105-3
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发表时间：
2019-06-14
影响因子：
16.6
通讯作者：
Zscheischler, Jakob
中科院分区：
文献类型：
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作者：
Runge, Jakob;Bathiany, Sebastian;Zscheischler, Jakob
科学事业的核心是理性地努力理解我们观察到的现象背后的原因。在像地球系统这样的大规模复杂动力系统中，真实的实验很少是可行的。然而，大量的观测和模拟数据的快速增长，开辟了新的数据驱动的因果关系的方法，超越了常用的相关技术的使用。在这里，我们给出了因果推理框架的概述，并确定了在地球系统科学及其他领域中常见的有前途的通用应用案例。我们讨论了挑战，并启动了基准平台causeme。net来缩小方法用户和开发人员之间的差距。
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme. net to close the gap between method users and developers.