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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

Causality and Explainability for Trustworthy Integrated Pest Management

Ilias Tsoumas · Vasileios Sitokonstantinou · GEORGIOS GIANNARAKIS · Evagelia Lampiri · Christos Athanassiou · Gustau Camps-Valls · Charalampos Kontoes · Ioannis Athanasiadis


Pesticides, widely used in agriculture for pest control, contribute to the climate crisis. Integrated pest management (IPM) is preferred as a climate-smart alternative. However, low adoption rates of IPM are observed due to farmers' skepticism about its effectiveness, so we introduce an enhancing data analysis framework for IPM to combat that. Our framework provides i) robust pest population predictions across diverse environments with invariant and causal learning, ii) interpretable pest presence predictions using transparent models, iii) actionable advice through counterfactual explanations for in-season IPM interventions, iv) field-specific treatment effect estimations, and v) causal inference to assess advice effectiveness.

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