Random Effect

Random effects modeling addresses the challenge of analyzing data with inherent correlations or heterogeneity among observations, improving the accuracy and reliability of statistical inferences. Current research focuses on integrating random effects within machine learning frameworks, such as adapting double machine learning for panel data and developing novel algorithms like reBandit for online reinforcement learning, particularly in applications like personalized medicine. These advancements aim to overcome limitations of traditional methods by handling complex data structures and improving predictive performance, impacting fields ranging from causal inference to personalized interventions. The incorporation of random effects into deep neural networks and Bayesian methods further enhances model flexibility and uncertainty quantification.