Network Interference

Network interference, the phenomenon where the outcome of one unit is influenced by others, poses a significant challenge across diverse fields, from e-commerce A/B testing to wireless communication and causal inference. Current research focuses on developing robust methods for estimating causal effects and optimizing resource allocation in the presence of interference, employing techniques like graph neural networks, targeted learning, and multi-armed bandits with sparse interference models. These advancements are crucial for improving the accuracy of analyses in various applications, leading to more effective decision-making in areas such as personalized medicine, marketing, and network optimization.