Sadegh Shirani, Yuwei Luo, William Overman, Ruoxuan Xiong, Mohsen Bayati
arXiv 3 Feb 2025 · Machine Learning
arXiv:2502.01106 · PDF · DOI · OpenAlex · Extracted main text
In experimental settings with network interference, a unit's treatment can influence outcomes of other units, challenging both causal effect estimation and its validation. Classic validation approaches fail as outcomes are only observable under one treatment scenario and exhibit complex correlation patterns due to interference. To address these challenges, we introduce a new framework enabling cross-validation for counterfactual estimation. At its core is our distribution-preserving network bootstrap method -- a theoretically-grounded approach inspired by approximate message passing. This method creates multiple subpopulations while preserving the underlying distribution of network effects. We extend recent causal message-passing developments by incorporating heterogeneous unit-level characteristics and varying local interactions, ensuring reliable finite-sample performance through non-asymptotic analysis. We also develop and publicly release a comprehensive benchmark toolbox with diverse experimental environments, from networks of interacting AI agents to opinion formation in real-world communities and ride-sharing applications. These environments provide known ground truth values while maintaining realistic complexities, enabling systematic examination of causal inference methods. Extensive evaluation across these environments demonstrates our method's robustness to diverse forms of network interference. Our work provides researchers with both a practical estimation framework and a standardized platform for testing future methodological developments.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Shirani, S. and Bayati, M (2024) Causal message-passing for experiments with unknown and general network interference self | 1.000 | 17 | 8 | 100% |
| 2 | Li, G. and Wei, Y (2022) A non-asymptotic framework for approximate message passing in spiked models | 1.000 | 6 | 5 | 100% |
| 3 | Bayati, M. and Montanari, A (2011) The dynamics of message passing on dense graphs, with applications to compressed sensing self | 1.000 | 6 | 3 | 100% |
| 4 | Bayati, M., Luo, Y., Overman, W., Shirani, S., and Xiong, R (2024) Higher-order causal message passing for experimentation with complex interference self | 0.644 | 2 | 2 | 100% |
| 5 | Bolthausen, E (2014) An iterative construction of solutions of the tap equations for the sherrington–kirkpatrick model | 0.644 | 2 | 2 | 100% |
| 6 | Donoho, D. L., Maleki, A., and Montanari, A (2009) Message-passing algorithms for compressed sensing | 0.644 | 2 | 2 | 100% |
| 7 | Eckles, D., Karrer, B., and Ugander, J (2016) Design and analysis of experiments in networks: Reducing bias from interference | 0.644 | 2 | 2 | 100% |
| 8 | Holtz, D., Lobel, R., Liskovich, I., and Aral, S (2020) Reducing interference bias in online marketplace pricing experiments | 0.644 | 2 | 2 | 100% |
| 9 | Hu, Y. and Wager, S (2022) Switchback experiments under geometric mixing | 0.644 | 2 | 2 | 100% |
| 10 | Hudgens, M. G. and Halloran, M. E (2008) Toward causal inference with interference | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 112 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | On Evolution-Based Models for Experimentation Under Interference | 1.000 | 6 | 3 |