Causal Inference in Geoscience and Remote Sensing from Observational Data

TL;DR

This paper introduces a robust method for causal inference from observational data in geoscience and remote sensing, using additive noise models and a novel sensitivity criterion to improve causal direction detection.

Contribution

It proposes a new sensitivity-based criterion for causal inference that enhances robustness and accuracy over existing methods in complex geoscience scenarios.

Findings

Achieves state-of-the-art detection rates across 28 geoscience problems.

Robust to noise sources and data distortions.

Effective in diverse applications like vegetation modeling and carbon cycle analysis.

Abstract

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's \blue{science}. In remote sensing and geosciences this is of special relevance to better understand the Earth's system and the complex interactions between the governing processes. In this paper, we focus on observational causal inference, thus we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (non-deterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires 1) two regression models in the forward and backward directions, and 2) the estimation of {\em statistical independence} between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the {\em sensitivity} (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence the criterion is robust to spurious detections. We illustrate performance in a collection of 28 geoscience causal inference problems, in a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and in assessing the impact of different regression models in a carbon cycle problem. The criterion achieves state-of-the-art detection rates in all cases, it is generally robust to noise sources and distortions.