论文标题
具有内核灵敏度图的地球科学的因果推断
Causal Inference in Geosciences with Kernel Sensitivity Maps
论文作者
论文摘要
从观察数据中建立随机变量之间的因果关系可能是当今科学中最重要的挑战。在遥感和地球科学中，这与更好地了解地球系统以及过程之间的复杂且难以捉摸的相互作用具有特殊的相关性。在本文中，我们探讨了一个框架，通过回归和依赖性估计来得出从变量对的原因效应关系。我们建议关注依赖估计量的灵敏度（曲率），以解释近似残差的正向和反向密度的不对称性。结果大量收集了28个地球科学因果推断问题，证明了该方法的良好能力。
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science. In remote sensing and geosciences this is of special relevance to better understand the Earth's system and the complex and elusive interactions between processes. In this paper we explore a framework to derive cause-effect relations from pairs of variables via regression and dependence estimation. We propose to focus on the sensitivity (curvature) of the dependence estimator to account for the asymmetry of the forward and inverse densities of approximation residuals. Results in a large collection of 28 geoscience causal inference problems demonstrate the good capabilities of the method.
