Mohsen Bayati, Yuwei Luo, William Overman, Sadegh Shirani, Ruoxuan Xiong
arXiv 1 Nov 2024 · Machine Learning · 1 citations (OpenAlex)
arXiv:2411.00945 · PDF · DOI · OpenAlex · Extracted main text
Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences and online marketplaces, where treating one experimental unit can influence outcomes for others through direct or indirect interactions. Such interference can lead to biased treatment effect estimates, particularly when the structure of these interactions is unknown. We address this challenge by introducing a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference. Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data to estimate the evolution of the system state. Concretely, we construct non-linear features from the moments of unit outcomes and treatments and then learn a function that maps these features to future mean and variance of unit outcomes. This allows for the estimation of the treatment effect over time. Extensive simulations across multiple domains, using synthetic and real network data, demonstrate the efficacy of our approach in estimating total treatment effect dynamics, even in cases where interference exhibits non-monotonic behavior in the probability of treatment.
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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 | Sadegh Shirani and Mohsen Bayati (2024) Causal message-passing for experiments with unknown and general network interference self | 1.000 | 11 | 4 | 100% |
| 2 | Mayleen Cortez, Matthew Eichhorn, and Christina Yu (2022) Staggered rollout designs enable causal inference under interference without network knowledge | 1.000 | 6 | 3 | 100% |
| 3 | Michael P Leung (2022) Causal inference under approximate neighborhood interference | 0.811 | 4 | 2 | 100% |
| 4 | Su Jia, Nathan Kallus, and Christina Lee Yu (2024) Clustered switchback experiments: Near-optimal rates under spatiotemporal interference, 2024 | 0.737 | 3 | 2 | 100% |
| 5 | Daniel L Sussman and Edoardo M Airoldi (2017) Elements of estimation theory for causal effects in the presence of network interference | 0.737 | 3 | 2 | 100% |
| 6 | Mohsen Bayati and Andrea Montanari (2011) The dynamics of message passing on dense graphs, with applications to compressed sensing self | 0.644 | 2 | 2 | 100% |
| 7 | Qianyi Chen, Bo Li, Lu Deng, and Yong Wang (2024) Optimized covariance design for ab test on social network under interference | 0.644 | 2 | 2 | 100% |
| 8 | Dean Eckles, Brian Karrer, and Johan Ugander (2016) Design and analysis of experiments in networks: Reducing bias from interference | 0.644 | 2 | 2 | 100% |
| 9 | Guido W Imbens and Donald B Rubin (2015) Causal inference in statistics, social, and biomedical sciences | 0.644 | 2 | 2 | 100% |
| 10 | Anish Agarwal, Sarah Cen, Devavrat Shah, and Christina Lee Yu (2022) Network synthetic interventions: A framework for panel data with network interference | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 66 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Can We Validate Counterfactual Estimations in the Presence of General Network Interference? | 0.644 | 2 | 2 |
| 2 | On Evolution-Based Models for Experimentation Under Interference | 0.405 | 1 | 1 |
| 3 | Validating Causal Message Passing Against Network-Aware Methods on Real Experiments | 0.405 | 1 | 1 |