Causal Inference in Geosciences with Kernel Sensitivity Maps
This work addresses the challenge of establishing causal relations from observational data in geosciences, which is crucial for understanding Earth's system interactions.
This paper explores a framework for deriving cause-effect relations from pairs of variables in geosciences using regression and dependence estimation. The method focuses on the sensitivity (curvature) of the dependence estimator to account for the asymmetry of forward and inverse densities of approximation residuals, demonstrating good capabilities across 28 geoscience causal inference problems.
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.