Steven Wilkins Reeves, Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick
arXiv 17 Jun 2024 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2406.11940 · PDF · DOI · OpenAlex · Extracted main text
The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, treatments can have an effect on many others beyond the immediately treated. Interference can generically be thought of as mediated through some network structure. In many empirically relevant situations however, complete network data (required to adjust for these spillover effects) are too costly or logistically infeasible to collect. Partially or indirectly observed network data (e.g., subsamples, aggregated relational data (ARD), egocentric sampling, or respondent-driven sampling) reduce the logistical and financial burden of collecting network data, but the statistical properties of treatment effect adjustments from these design strategies are only beginning to be explored. In this paper, we present a framework for the estimation and inference of treatment effect adjustments using partial network data through the lens of structural causal models. We also illustrate procedures to assign treatments using only partial network data, with the goal of either minimizing estimator variance or optimally seeding. We derive single network asymptotic results applicable to a variety of choices for an underlying graph model. We validate our approach using simulated experiments on observed graphs with applications to information diffusion in India and Malawi.
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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 | Beaman, L., A. BenYishay, J. Magruder, and A. M. Mobarak (2021) Can network theory-based targeting increase technology adoption? | 1.000 | 12 | 5 | 100% |
| 2 | Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2019) Using gossips to spread information: Theory and evidence from two randomized controlled trials self | 1.000 | 12 | 4 | 100% |
| 3 | Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2013) The diffusion of microfinance self | 1.000 | 9 | 4 | 100% |
| 4 | Breza, E., A. G. Chandrasekhar, S. Lubold, T. H. McCormick, and M. Pan (2023) Consistently estimating network statistics using aggregated relational data self | 1.000 | 8 | 4 | 100% |
| 5 | Chandrasekhar, A. G., M. O. Jackson, T. H. McCormick, and V. Thiyage… (2023) General covariance-based conditions for central limit theorems with dependent triangular arrays self | 1.000 | 7 | 3 | 100% |
| 6 | Gao, C., Y. Lu, and H. H. Zhou (2015) Rate-optimal graphon estimation | 0.843 | 3 | 3 | 100% |
| 7 | Ogburn, E. L., O. Sofrygin, I. Diaz, and M. J. Van der Laan (2022) Causal inference for social network data | 0.843 | 3 | 3 | 100% |
| 8 | Aronow, P. M. and C. Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.811 | 4 | 2 | 100% |
| 9 | Breza, E., A. G. Chandrasekhar, T. H. McCormick, and M. Pan (2020) Using aggregated relational data to feasibly identify network structure without network data self | 0.811 | 4 | 2 | 100% |
| 10 | Heckathorn, D. D (1997) Respondent-driven sampling: a new approach to the study of hidden populations | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 106 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence | 0.405 | 1 | 1 |
| 2 | Network-Adjusted GMM Estimation under Network Uncertainty | 0.405 | 1 | 1 |