Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data
仅用聚合数据识别个体随机化实验的全局平均处理效应
Individualized randomized experiments are central to online platforms for optimizing personalized decisions in complex environments. In two-sided markets, however, standard treatment effect estimation is often invalid due to strong temporal and cross-unit interference, a challenge compounded when only aggregated data are available because of privacy or system constraints. To address these issues, we identify the Global Average Treatment Effect (GATE) using only group-level…