Spillover Effect

Spillover effects, the influence of a treatment or event on units beyond those directly targeted, are a central focus in diverse fields, aiming to understand and model these indirect impacts for improved decision-making. Current research emphasizes developing methods to accurately estimate spillover effects in various contexts, employing techniques like empirical Bayes designs, graph neural networks, and dual weighting regression to account for network interference and heterogeneous impacts. These advancements are crucial for refining A/B testing, policy evaluation, and understanding complex systems like financial markets and social networks, ultimately leading to more effective interventions and predictions.