Cluster Randomization

Cluster randomization is a statistical technique used to design experiments where the outcome of one unit can be influenced by others, a phenomenon known as interference. Current research focuses on improving the accuracy of causal effect estimation in the presence of interference, particularly when this influence spreads in cascading patterns or across complex networks. This involves developing advanced randomization strategies, such as those informed by network structure or incorporating machine learning to identify and model interference patterns. These advancements are crucial for ensuring the validity of experiments in various fields, including social sciences, marketing, and online platforms, where interference is prevalent.