arXiv 8 Aug 2023 · Econometrics
arXiv:2308.04276 · PDF · DOI · OpenAlex · Extracted main text
This study investigates the causal interpretation of linear social interaction models in the presence of endogeneity in network formation under a heterogeneous treatment effects framework. We consider an experimental setting in which individuals are randomly assigned to treatments while no interventions are made for the network structure. We show that running a linear regression ignoring network endogeneity is not problematic for estimating the average direct treatment effect. However, it leads to sample selection bias and negative-weights problem for the estimation of the average spillover effect. To overcome these problems, we propose using potential peer treatment as an instrumental variable (IV), which is automatically a valid IV for actual spillover exposure. Using this IV, we examine two IV-based estimands and demonstrate that they have a local average treatment-effect-type causal interpretation for the spillover effect.
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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 | Vazquez-Bare, G (2022) Identification and estimation of spillover effects in randomized experiments | 0.928 | 4 | 4 | 100% |
| 2 | Paluck, E.L., Shepherd, H., and Aronow, P.M (2016) Changing climates of conflict: A social network experiment in 56 schools | 0.811 | 4 | 2 | 100% |
| 3 | Mogstad, M., Torgovitsky, A., and Walters, C.R (2021) The causal interpretation of two-stage least squares with multiple instrumental variables | 0.585 | 3 | 1 | 100% |
| 4 | DiTraglia, F.J., Garcá-Jimeno, C., O'Keeffe-O'Donovan, R., and Sánch… (2023) Identifying causal effects in experiments with spillovers and non-compliance | 0.405 | 1 | 1 | 100% |
| 5 | Angelucci, M., Prina, S., Royer, H., and Samek, A (2019) Incentives and unintended consequences: Spillover effects in food choice | 0.405 | 1 | 1 | 100% |
| 6 | Baird, S., Bohren, J.A., McIntosh, C., and Özler, B (2018) Optimal design of experiments in the presence of interference | 0.405 | 1 | 1 | 100% |
| 7 | Blandhol, C., Bonney, J., Mogstad, M., and Torgovitsky, A (2022) When is tsls actually late? | 0.405 | 1 | 1 | 100% |
| 8 | Booij, A.S., Leuven, E., and Oosterbeek, H (2017) Ability peer effects in university: Evidence from a randomized experiment | 0.405 | 1 | 1 | 100% |
| 9 | Borusyak, K. and Hull, P (2020) Non-random exposure to exogenous shocks: Theory and applications | 0.405 | 1 | 1 | 100% |
| 10 | Cai, J., De Janvry, A., and Sadoulet, E (2015) Social networks and the decision to insure | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.