Causal Inference in Plant Disease Contexts.

Area of Science:

• Plant Pathology
• Agricultural Science
• Causal Inference

Background:

• Many plant pathology questions are causal in nature.
• Formal causal inference frameworks are underutilized in plant pathology.
• Agricultural researchers need accessible introductions to causal concepts.

Purpose of the Study:

• To provide an accessible, higher-level introduction to causal inference for agricultural researchers.
• To highlight the relevance and application of causal inference methods in plant pathology.
• To encourage the adoption of causal inference in the discipline.

Main Methods:

• Introduction to the potential outcomes framework for defining counterfactuals.
• Explanation of directed acyclic graphs for visualizing causal assumptions.
• Discussion of randomized controlled trials and observational data analysis for causal effect estimation.

Main Results:

• The review defines key causal inference concepts.
• It discusses the application of these methods in agriculturally relevant contexts, including interplot interference and heterogeneous treatment effects.
• Pioneering studies applying causal inference in plant pathology are highlighted.

Conclusions:

• Causal inference offers a powerful framework for addressing complex questions in plant pathology.
• The adoption of these methods can enhance the rigor and understanding of plant disease research.
• Further application of causal inference, especially with observational data, is encouraged.