IDEAS home Printed from https://ideas.repec.org/r/arx/papers/1911.07085.html
   My bibliography  Save this item

Causal Inference Under Approximate Neighborhood Interference

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ruonan Xu, 2023. "Difference-in-Differences with Interference," Papers 2306.12003, arXiv.org, revised Jan 2025.
  2. Tadao Hoshino & Takahide Yanagi, 2023. "Randomization Test for the Specification of Interference Structure," Papers 2301.05580, arXiv.org, revised Dec 2023.
  3. Gonzalo Vazquez-Bare, 2020. "Causal Spillover Effects Using Instrumental Variables," Papers 2003.06023, arXiv.org, revised Dec 2021.
  4. Stefan Faridani & Paul Niehaus, 2022. "Linear estimation of global average treatment effects," Papers 2209.14181, arXiv.org, revised Dec 2024.
  5. Yuchen Hu & Stefan Wager, 2022. "Switchback Experiments under Geometric Mixing," Papers 2209.00197, arXiv.org, revised Apr 2024.
  6. Mohsen Bayati & Yuwei Luo & William Overman & Sadegh Shirani & Ruoxuan Xiong, 2024. "Higher-Order Causal Message Passing for Experimentation with Complex Interference," Papers 2411.00945, arXiv.org, revised Feb 2025.
  7. Christopher Harshaw & Fredrik Savje & Yitan Wang, 2022. "A General Design-Based Framework and Estimator for Randomized Experiments," Papers 2210.08698, arXiv.org, revised Aug 2025.
  8. Michael P. Leung, 2021. "Rate-Optimal Cluster-Randomized Designs for Spatial Interference," Papers 2111.04219, arXiv.org, revised Sep 2022.
  9. Eric Auerbach & Hongchang Guo & Max Tabord-Meehan, 2021. "The Local Approach to Causal Inference under Network Interference," Papers 2105.03810, arXiv.org, revised Mar 2025.
  10. Tadao Hoshino & Takahide Yanagi, 2024. "Causal Inference with Noncompliance and Unknown Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(548), pages 2869-2880, October.
  11. Yike Wang & Chris Gu & Taisuke Otsu, 2024. "Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity," Papers 2401.16275, arXiv.org.
  12. Alejandro Sanchez-Becerra, 2022. "The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment," Papers 2209.14391, arXiv.org.
  13. Ruonan Xu & Jeffrey M. Wooldridge, 2022. "A Design-Based Approach to Spatial Correlation," Papers 2211.14354, arXiv.org.
  14. Michael P. Leung, 2023. "Network Cluster‐Robust Inference," Econometrica, Econometric Society, vol. 91(2), pages 641-667, March.
  15. Bora Kim, 2020. "Analysis of Randomized Experiments with Network Interference and Noncompliance," Papers 2012.13710, arXiv.org.
  16. Han, Kevin & Basse, Guillaume & Bojinov, Iavor, 2024. "Population interference in panel experiments," Journal of Econometrics, Elsevier, vol. 238(1).
  17. Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2024.
  18. Tadao Hoshino, 2021. "Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach," Papers 2112.15114, arXiv.org, revised Jan 2023.
  19. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org, revised Apr 2025.
  20. Nicolas Debarsy & Julie Le Gallo, 2025. "Identification of Spatial Spillovers: Do's and Don'ts," Post-Print hal-05107904, HAL.
  21. Fredrik Savje, 2021. "Causal inference with misspecified exposure mappings: separating definitions and assumptions," Papers 2103.06471, arXiv.org, revised Mar 2023.
  22. Haoge Chang, 2023. "Design-based Estimation Theory for Complex Experiments," Papers 2311.06891, arXiv.org, revised May 2025.
  23. Davide Viviano & Lihua Lei & Guido Imbens & Brian Karrer & Okke Schrijvers & Liang Shi, 2023. "Causal clustering: design of cluster experiments under network interference," Papers 2310.14983, arXiv.org, revised Jan 2025.
  24. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.
  25. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised May 2025.
  26. Nicolas Debarsy & Julie Le Gallo, 2025. "Identification of spatial spillovers: Do’s and don'ts," Post-Print hal-04549691, HAL.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.