  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.
