Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data
摘要
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 data from treatment and control groups. We first establish identification conditions based on aggregated observations, and then propose the Individualized Randomized Experiment Varying Coefficient Decision Process (IRE-VCDP) model, which accounts for interference through supply-demand dynamics. Building on this framework, we develop a complete procedure for estimation and statistical inference of the GATE, along with theoretical guarantees for the proposed test. Extensive simulations and real-world experiments using data from a leading ridesharing platform demonstrate the effectiveness of our approach.
引用
@article{arxiv.2605.26532,
title = {Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data},
author = {Shuguang Yu and Ting Li and Yuchen Lu and Chengchun Shi and Fan Zhou and Zhichao Zou and Peng Zhen and Hongtu Zhu},
journal= {arXiv preprint arXiv:2605.26532},
year = {2026}
}