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Causal Inference in Geosciences with Kernel Sensitivity Maps by Pérez-Suay, Adrián; Camps-Valls, Gustau is a scholarly article available to read on EtoBox.

What is Causal Inference in Geosciences with Kernel Sensitivity Maps about?

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.

Author

Pérez-Suay, Adrián; Camps-Valls, Gustau

Published

2020

Language

EN