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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.06685 |
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| _version_ | 1866917070403272704 |
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| author | Jia, Su Frazier, Peter Kallus, Nathan Yu, Christina Lee |
| author_facet | Jia, Su Frazier, Peter Kallus, Nathan Yu, Christina Lee |
| contents | We study the estimation of the ATE in randomized controlled trials under a dynamically evolving interference structure. This setting arises in applications such as ride-sharing, where drivers move over time, and social networks, where connections continuously form and dissolve. In particular, we focus on scenarios where outcomes exhibit spatio-temporal interference driven by a sequence of random interference graphs that evolve independently of the treatment assignment. Loosely, our main result states that a truncated Horvitz-Thompson estimator achieves an MSE that vanishes linearly in the number of spatial and time blocks, times a factor that measures the average complexity of the interference graphs. As a key technical contribution that contrasts the static setting we present a fine-grained covariance bound for each pair of space-time points that decays exponentially with the time elapsed since their last ``interaction''. Our results can be applied to many concrete settings and lead to simplified bounds, including where the interference graphs (i) are induced by moving points in a metric space, or (ii) follow a dynamic Erdos-Renyi model, where each edge is created or removed independently in each time period. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimentation Under Non-stationary Interference Jia, Su Frazier, Peter Kallus, Nathan Yu, Christina Lee Statistics Theory We study the estimation of the ATE in randomized controlled trials under a dynamically evolving interference structure. This setting arises in applications such as ride-sharing, where drivers move over time, and social networks, where connections continuously form and dissolve. In particular, we focus on scenarios where outcomes exhibit spatio-temporal interference driven by a sequence of random interference graphs that evolve independently of the treatment assignment. Loosely, our main result states that a truncated Horvitz-Thompson estimator achieves an MSE that vanishes linearly in the number of spatial and time blocks, times a factor that measures the average complexity of the interference graphs. As a key technical contribution that contrasts the static setting we present a fine-grained covariance bound for each pair of space-time points that decays exponentially with the time elapsed since their last ``interaction''. Our results can be applied to many concrete settings and lead to simplified bounds, including where the interference graphs (i) are induced by moving points in a metric space, or (ii) follow a dynamic Erdos-Renyi model, where each edge is created or removed independently in each time period. |
| title | Experimentation Under Non-stationary Interference |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2511.06685 |