Randomized Experiment

Randomized experiments are the gold standard for establishing causal relationships, aiming to rigorously assess treatment effects by randomly assigning subjects to different conditions. Current research emphasizes improving the efficiency and robustness of these experiments, focusing on areas like optimizing sample size for heterogeneous treatment effects, mitigating bias in observational data through techniques such as instrumental variables, and developing methods for early stopping in the presence of harmful effects. These advancements are crucial for enhancing the reliability and interpretability of experimental results across diverse fields, from personalized medicine and A/B testing to policy evaluation and AI fairness.