Causality and Explainability for Trustworthy Integrated Pest Management (Proposals Track)

Ilias Tsoumas (National Observatory of Athens); Vasileios Sitokonstantinou (University of Valencia); Georgios Giannarakis (National Observatory of Athens); Evagelia Lampiri (University of Thessaly); Christos Athanassiou (University of Thessaly); Gustau Camps-Valls (Universitat de València); Charalampos Kontoes (National Observatory of Athens); Ioannis N Athanasiadis (Wageningen University and Research)

Paper PDF Poster File NeurIPS 2023 Poster Cite
Causal & Bayesian Methods Agriculture & Food


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.