Adaptive Experiment

Adaptive experimentation optimizes the design and execution of scientific studies or A/B tests by dynamically adjusting treatment allocation based on accumulating data, aiming to improve statistical power and efficiency. Current research emphasizes addressing practical challenges like delayed feedback, non-stationarity, and multiple objectives, often employing mathematical programming frameworks, Bayesian methods such as Thompson Sampling, and contextual bandit algorithms to achieve this. This field is significant because it enhances the efficiency and robustness of experimentation across diverse domains, from clinical trials and digital marketing to educational interventions and online services, leading to more informed decision-making and resource optimization.