Interpreting Black‑Box Climate Models for Policymakers with SHAP and Counterfactuals

No, but you do need climate data literacy. You should understand what your variables mean, why time-aware splits matter, and which features reflect hazard versus vulnerability or adaptive capacity. Without that, even a technically correct explanation can become a misleading policy story.

Yes, but you should be conservative. Small datasets make complex models easier to overfit, and explanations can become unstable. In that setting, simpler baselines, careful resampling, and explicit uncertainty checks matter even more.

Not directly. Counterfactuals are best treated as structured “what-if” scenarios generated by the model. They are useful for prioritization and discussion, but they still need causal scrutiny, feasibility checks, and human review before becoming policy proposals.

A ranking metric such as ROC-AUC is useful, but it is rarely enough. PR-AUC, threshold-specific precision and recall, Brier score, and calibration curves are usually more informative when model scores influence triage, targeting, or budget allocation.

As soon as model outputs start influencing meaningful decisions. At that point, explanation generation, model versioning, review workflows, logging, and human oversight are not optional extras. They are part of the system design.