Personalized Experimentation

Personalized experimentation aims to optimize treatment assignment for individuals based on their predicted responses, moving beyond traditional A/B testing's one-size-fits-all approach. Current research emphasizes developing and deploying scalable, interpretable models—often leveraging machine learning techniques—to predict heterogeneous treatment effects and generate personalized policies that maximize multiple outcomes simultaneously. This approach promises significant improvements in efficiency and effectiveness across various fields, from online advertising to healthcare, by tailoring interventions to individual needs and characteristics.