Albert Tan, Sadegh Shirani, James Nordlund, Mohsen Bayati
arXiv 4 Feb 2026 · Statistics — Methodology
arXiv:2602.04230 · PDF · DOI · OpenAlex · Extracted main text
Estimating total treatment effects in the presence of network interference typically requires knowledge of the underlying interaction structure. However, in many practical settings, network data is either unavailable, incomplete, or measured with substantial error. We demonstrate that causal message passing, a methodology that leverages temporal structure in outcome data rather than network topology, can recover total treatment effects comparable to network-aware approaches. We apply causal message passing to two large-scale field experiments where a recently developed bipartite graph methodology, which requires network knowledge, serves as a benchmark. Despite having no access to the interaction network, causal message passing produces effect estimates that match the network-aware approach in direction across all metrics and in statistical significance for the primary decision metric. Our findings validate the premise of causal message passing: that temporal variation in outcomes can serve as an effective substitute for network observation when estimating spillover effects. This has important practical implications: practitioners facing settings where network data is costly to collect, proprietary, or unreliable can instead exploit the temporal dynamics of their experimental data.
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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 | Tan, Albert and Bayati, Mohsen and Nordlund, James and Istomin, Roman (2025) Estimating Total Effects in Bipartite Experiments with Spillovers and Partial Eligibility self | 1.000 | 10 | 3 | 100% |
| 2 | Shirani, Sadegh and Luo, Yuwei and Overman, William and Xiong, Ruoxu… Can We Validate Counterfactual Estimations in the Presence of General Network Interference? | 1.000 | 5 | 3 | 100% |
| 3 | Sadegh Shirani and Mohsen Bayati (2024) Causal message-passing for experiments with unknown and general network interference self | 0.811 | 4 | 2 | 100% |
| 4 | Shirani, Sadegh and Bayati, Mohsen (2025) On Evolution-Based Models for Experimentation Under Interference self | 0.737 | 3 | 2 | 100% |
| 5 | Harshaw, Christopher and Sävje, Fredrik and Eisenstat, David and Mir… (2023) Design and analysis of bipartite experiments under a linear exposure-response model | 0.585 | 3 | 1 | 100% |
| 6 | Johari, Ramesh and Li, Hannah and Liskovich, Inessa and Weintraub, G… (2022) Experimental design in two-sided platforms: An analysis of bias | 0.585 | 3 | 1 | 100% |
| 7 | Munro, Evan and Wager, Stefan and Xu, Kuang (2021) Treatment Effects in Market Equilibrium | 0.585 | 3 | 1 | 100% |
| 8 | Aronow, Peter M. and Samii, Cyrus (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.511 | 2 | 1 | 100% |
| 9 | Cortez, Mayleen and Eichhorn, Matthew and Yu, Christina (2022) Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge | 0.511 | 2 | 1 | 100% |
| 10 | Doudchenko, Nick and Zhang, Minzhengxiong and Drynkin, Evgeni and Ai… (2020) Causal inference with bipartite designs | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 58 scored citations.