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

• URL: http://arxiv.org/abs/2209.12580v1
• Date: Mon, 26 Sep 2022 10:45:48 GMT
• Title: Robust Causality and False Attribution in Data-Driven Earth Science Discoveries
• Authors: Elizabeth Eldhose (1), Tejasvi Chauhan (1), Vikram Chandel (1), Subimal Ghosh (1 and 2), and Auroop R. Ganguly (3 and 4) ((1) Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India, (2) Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India, (3) Sustainability and Data Sciences Laboratory, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA, (4) Pacific Northwest National Laboratory, Richland, WA, USA)
• Abstract summary: Causal and attribution studies are essential for earth scientific discoveries and informing climate, ecology, and water policies. Here we show that transfer entropy-based causal graphs can be spurious even when augmented with statistical significance. We develop a subsample-based ensemble approach for robust causality analysis.
• Score: 0.3503794925747607
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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