Interaction Effect

Interaction effects, the phenomenon where the combined impact of multiple factors surpasses the sum of their individual effects, are a central focus in diverse scientific fields. Current research emphasizes developing methods to accurately detect, quantify, and interpret these interactions, employing techniques like ANOVA decomposition, Shapley values, and Bayesian tensor regression within various model architectures including neural networks. This focus stems from the need for improved model interpretability and accurate predictions across domains ranging from biological systems and agricultural modeling to machine learning applications and online experimentation, ultimately leading to more reliable insights and informed decision-making.