  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.
