arXiv 27 Nov 2025 · Econometrics
arXiv:2511.22643 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a general framework for identifying causal effects in settings with spillovers, where both outcomes and endogenous treatment decisions are influenced by peers within a known group. It introduces the generalized local average controlled spillover and direct effects (LACSEs and LACDEs), which extend the local average treatment effect framework to settings with spillovers and establish sufficient conditions for their point identification without restricting the cardinality of the support of instrumental variables. These conditions clarify the necessity of commonly imposed restrictions to achieve point identification with binary instruments in related studies. The paper then defines the marginal controlled spillover and direct effects (MCSEs and MCDEs), which naturally extend the marginal treatment effect framework to settings with spillovers and are nonparametrically point identified from continuous variation in instruments. These marginal effects serve as building blocks for a broad class of policy-relevant treatment effects, including some causal spillover parameters in the related literature. Semiparametric and parametric estimators are developed, and an application using Add Health data reveals heterogeneity in education spillovers within best-friend networks.
appendix boundary found by appendix_command · 56% of the source is main text. Read the extracted text to check this.
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, Gonzalo (2022) Causal spillover effects using instrumental variables | 0.961 | 9 | 3 | 89% |
| 2 | DiTraglia, Francis J and Garc\'ia-Jimeno, Camilo and O’Keeffe-O’Dono… (2023) Identifying causal effects in experiments with spillovers and non-compliance | 0.928 | 4 | 3 | 100% |
| 3 | Hoshino, Tadao and Yanagi, Takahide (2023) Treatment effect models with strategic interaction in treatment decisions | 0.843 | 4 | 4 | 75% |
| 4 | Carneiro, Pedro and Lee, Sokbae (2009) Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enro… | 0.843 | 5 | 4 | 60% |
| 5 | Aradillas-Lopez, Andres (2010) Semiparametric estimation of a simultaneous game with incomplete information | 0.830 | 7 | 3 | 57% |
| 6 | Balat, Jorge F and Han, Sukjin (2023) Multiple treatments with strategic substitutes | 0.737 | 3 | 3 | 67% |
| 7 | Manski, Charles F (2013) Identification of treatment response with social interactions | 0.737 | 3 | 3 | 67% |
| 8 | Aronow, Peter M and Samii, Cyrus (2017) ESTIMATING AVERAGE CAUSAL EFFECTS UNDER GENERAL INTERFERENCE, WITH APPLICATION TO A SOCIAL NETWORK EXPERIMENT | 0.644 | 2 | 2 | 100% |
| 9 | Cools, Angela and Fernández, Raquel and Patacchini, Eleonora (2019) Girls, boys, and high achievers | 0.644 | 2 | 2 | 100% |
| 10 | Heckman, James J and Vytlacil, Edward (2005) Structural equations, treatment effects, and econometric policy evaluation 1 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.