arXiv 31 Jan 2025 · Econometrics
arXiv:2501.19394 · PDF · DOI · OpenAlex · Extracted main text
In contrast to problems of interference in (exogenous) treatments, models of interference in unit-specific (endogenous) outcomes do not usually produce a reduced-form representation where outcomes depend on other units' treatment status only at a short network distance, or only through a known exposure mapping. This remains true if the structural mechanism depends on outcomes of peers only at a short network distance, or through a known exposure mapping. In this paper, we first define causal estimands that are identified and estimable from a single experiment on the network under minimal assumptions on the structure of interference, and which represent average partial causal responses which generally vary with other global features of the realized assignment. Under a fixed-population, design-based approach, we show unbiasedness and consistency for inverse-probability weighting (IPW) estimators for those causal parameters from a randomized experiment on a single network. We also analyze more closely the case of marginal interventions in a model of equilibrium with smooth response functions where we can recover LATE-type weighted averages of derivatives of those response functions. Under additional structural assumptions, these “agnostic" causal estimands can be combined to recover model parameters, but also retain their less restrictive causal interpretation.
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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 | Aronow and Samii (2017) Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment | 1.000 | 8 | 4 | 100% |
| 2 | Li and Wager (2022) Random Graph Asymptotics for Treatment Effiect Estimation under Network Interference | 0.928 | 5 | 3 | 80% |
| 3 | Manski (2011) Identification of Treatment Response with Social Interactions | 0.874 | 5 | 2 | 100% |
| 4 | Sävje (2024) Causal Inference with Misspecified Exposure Mappings: Separating Definitions and Assumptions | 0.874 | 5 | 2 | 100% |
| 5 | Harshaw, Sävje, and Wang (2022) A Design-Based Riesz Representation Framework for Randomized Experiments | 0.843 | 3 | 3 | 100% |
| 6 | Bramoullé, Djebbari, and Fortin (2009) Identification of Peer Effects through Social Networks | 0.811 | 4 | 2 | 100% |
| 7 | Hudgens and Halloran (2008) Toward Causal Inference with Interference | 0.737 | 4 | 2 | 75% |
| 8 | Leung (2022) Causal Inference under Approximate Neighborhood Interference | 0.737 | 3 | 2 | 100% |
| 9 | Tchetgen-Tchetgen and VanderWeele (2010) On Causal Inference in the Presence of Interference | 0.737 | 3 | 2 | 100% |
| 10 | Abadie, Athey, Imbens, and Wooldridge (2014) Finite Population Causal Standard Errors | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Decomposition of Spillover Effects Under Misspecification: Pseudo-True Estimands and a Local-Global Extension | 0.644 | 2 | 2 |
| 2 | Testing Exclusion and Shape Restrictions in Potential Outcomes Models | 0.405 | 1 | 1 |