Causal Inference in Geosciences with Kernel Sensitivity Maps

TL;DR

This paper introduces a novel framework using kernel sensitivity maps to infer causal relationships in geosciences, leveraging dependence estimation and residual analysis to better understand Earth's complex processes.

Contribution

The paper proposes a new method based on dependence sensitivity to identify causal directions in geoscientific data, addressing the asymmetry in residual densities.

Findings

Effective in 28 geoscience causal inference problems

Demonstrates robustness of the kernel sensitivity approach

Improves understanding of cause-effect relations in Earth's systems

Abstract

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.