Artem Kuriksha, Kenneth Hung
arXiv 5 Oct 2026 · Econometrics
arXiv:2610.07363 · PDF · Extracted main text
We present a methodology for analyzing node-level outcomes while experimenting with edge-level treatments in a population connected by an undirected graph. Under our design, nodes are randomly assigned to test or control, and each edge inherits the treatment of its endpoints, with conflicts resolved by randomization. We use each node's assigned status as an instrument for its treatment exposure. We show that the Wald estimator is consistent for the global average treatment effect (GATE), even when the edge weights used to construct the exposure are misspecified. We formalize the assumptions needed both in terms of the linearity of potential outcomes and the sparsity of the graph, and prove the asymptotic normality of the Wald estimator. The estimator is straightforward to implement, requiring no assignment simulations over the graph. Our Monte Carlo study demonstrates the strong performance of our approach in the context of a social platform.
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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 | Christopher Harshaw and Fredrik Sävje and David Eisenstat and Vahab… (2023) Design and analysis of bipartite experiments under a linear exposure-response model | 1.000 | 6 | 3 | 100% |
| 2 | Angrist, Joshua D and Graddy, Kathryn and Imbens, Guido W (2000) The interpretation of instrumental variables estimators in simultaneous equations models with an application to the demand for f… | 0.737 | 3 | 2 | 100% |
| 3 | Angrist, Joshua D and Imbens, Guido W (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity | 0.644 | 2 | 2 | 100% |
| 4 | Wooldridge, Jeffrey M (1997) On two stage least squares estimation of the average treatment effect in a random coefficient model | 0.644 | 2 | 2 | 100% |
| 5 | Wooldridge, Jeffrey M (2003) Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model | 0.644 | 2 | 2 | 100% |
| 6 | Abadie, Alberto (2003) Semiparametric instrumental variable estimation of treatment response models | 0.405 | 1 | 1 | 100% |
| 7 | Angrist, Joshua D and Imbens, Guido W and Rubin, Donald B (1996) Identification of causal effects using instrumental variables | 0.405 | 1 | 1 | 100% |
| 8 | Aronow, Peter M and Samii, Cyrus (2017) Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment | 0.405 | 1 | 1 | 100% |
| 9 | Bajari, Patrick and Burdick, Brian and Imbens, Guido W and Masoero,… (2021) Multiple randomization designs | 0.405 | 1 | 1 | 100% |
| 10 | Borusyak, Kirill and Hull, Peter (2023) Non-random exposure to exogenous shocks | 0.405 | 1 | 1 | 100% |
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