arXiv 12 Mar 2020 · Econometrics · publishedJournal of the American Statistical Association (2021) · 9 citations (OpenAlex)
arXiv:2003.06023 · PDF · DOI · OpenAlex · Extracted main text
I set up a potential outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and show that causal effects can be estimated by 2SLS in this case. I illustrate the proposed methods using data from an experiment on social interactions and voting behavior. I also introduce an alternative assumption, independence of peers' types, that identifies parameters of interest under two-sided noncompliance by restricting the amount of heterogeneity in average potential outcomes.
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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 | Foos, F., and de Rooij, E. A (2017) All in the Family: Partisan Disagreement and Electoral Mobilization in Intimate Networks—A Spillover Experiment | 0.843 | 5 | 5 | 60% |
| 2 | Abadie, A., and Cattaneo, M. D (2018) Econometric Methods for Program Evaluation | 0.843 | 3 | 3 | 100% |
| 3 | Kang, H., and Imbens, G (2016) Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects | 0.843 | 3 | 3 | 100% |
| 4 | Imbens, G. W., and Rubin, D. B (1997) Estimating Outcome Distributions for Compliers in Instrumental Variables Models | 0.737 | 3 | 3 | 67% |
| 5 | Vazquez-Bare, G. (forthcoming), Identification and Estimation of Spi… self | 0.737 | 3 | 2 | 100% |
| 6 | Angrist, J. D., Imbens, G. W., and Rubin, D. B (1996) Identification of Causal Effects Using Instrumental Variables | 0.644 | 2 | 2 | 100% |
| 7 | Angrist, J. D., and Krueger, A. B (2001) Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments | 0.644 | 2 | 2 | 100% |
| 8 | DiTraglia, F. J., García-Jimeno, C., O'Keeffe-O'Donovan, R., and Sán… (2021) Identifying Causal Effects in Experiments with Spillovers and Non-Compliance | 0.644 | 2 | 2 | 100% |
| 9 | Duflo, E., and Saez, E (2003) The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment | 0.644 | 2 | 2 | 100% |
| 10 | Imai, K., Jiang, Z., and Malani, A (2021) Causal Inference with Interference and Noncompliance in Two-Stage Randomized Experiments | 0.644 | 2 | 2 | 100% |
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