Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916046936473600 |
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| author | Yu, Shuguang Li, Ting Lu, Yuchen Shi, Chengchun Zhou, Fan Zou, Zhichao Zhen, Peng Zhu, Hongtu |
| author_facet | Yu, Shuguang Li, Ting Lu, Yuchen Shi, Chengchun Zhou, Fan Zou, Zhichao Zhen, Peng Zhu, Hongtu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26532 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data Yu, Shuguang Li, Ting Lu, Yuchen Shi, Chengchun Zhou, Fan Zou, Zhichao Zhen, Peng Zhu, Hongtu Methodology 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. |
| title | Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2605.26532 |