Online Experiment

Online experimentation, encompassing A/B testing and multi-armed bandit algorithms, aims to efficiently evaluate the causal effects of interventions, such as new features or marketing campaigns, in digital environments. Current research emphasizes improving the accuracy and efficiency of these experiments, focusing on robust statistical estimators for heavy-tailed data, incorporating network interference effects, and leveraging large language models for user behavior simulation and content optimization. These advancements enhance the reliability and speed of online experimentation, leading to more data-driven decision-making across various fields, from e-commerce and advertising to healthcare and software development.