Robust Causality and False Attribution in Data-Driven Earth Science Discoveries

TL;DR

This paper addresses the challenges of causal inference in earth sciences by developing a robust, ensemble-based transfer entropy method to improve the reliability of causality detection in climate and ecohydrology data.

Contribution

It introduces a subsample-based ensemble approach to enhance the robustness of transfer entropy causal graphs in earth science data analysis.

Findings

The proposed method improves causality detection robustness.

It is effective in climate and ecohydrology data.

The approach reduces spurious causal inferences.

Abstract

Causal and attribution studies are essential for earth scientific discoveries and critical for informing climate, ecology, and water policies. However, the current generation of methods needs to keep pace with the complexity of scientific and stakeholder challenges and data availability combined with the adequacy of data-driven methods. Unless carefully informed by physics, they run the risk of conflating correlation with causation or getting overwhelmed by estimation inaccuracies. Given that natural experiments, controlled trials, interventions, and counterfactual examinations are often impractical, information-theoretic methods have been developed and are being continually refined in the earth sciences. Here we show that transfer entropy-based causal graphs, which have recently become popular in the earth sciences with high-profile discoveries, can be spurious even when augmented with statistical significance. We develop a subsample-based ensemble approach for robust causality analysis. Simulated data, and observations in climate and ecohydrology, suggest the robustness and consistency of this approach.