Tadao Hoshino, Takahide Yanagi
arXiv 17 Aug 2021 · Statistics — Methodology · publishedJournal of the American Statistical Association (2023) · 5 citations (OpenAlex)
arXiv:2108.07455 · PDF · DOI · OpenAlex · Extracted main text
We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of network interference is unknown to researchers. To estimate meaningful causal parameters in this situation, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate identification conditions for the intention-to-treat effects and the average treatment effects for compliers, while explicitly considering the possibility of misspecification of exposure mapping. Based on our identification results, we develop nonparametric estimation procedures via inverse probability weighting. Their asymptotic properties, including consistency and asymptotic normality, are investigated using an approximate neighborhood interference framework. For an empirical illustration, we apply our method to experimental data on the anti-conflict intervention school program. The proposed methods are readily available with the companion R package latenetwork.
appendix boundary found by appendix_command · 41% 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 | Imai, K., Z. Jiang, and A. Malani (2021) Causal inference with interference and noncompliance in two-stage randomized experiments | 0.965 | 10 | 3 | 90% |
| 2 | Hu, Y., S. Li, and S. Wager (2022) Average direct and indirect causal effects under interference | 0.928 | 5 | 3 | 80% |
| 3 | Paluck, E. L., H. Shepherd, and P. M. Aronow (2016) Changing climates of conflict: A social network experiment in 56 schools | 0.928 | 4 | 3 | 100% |
| 4 | Leung, M. P (2022) Causal inference under approximate neighborhood interference | 0.855 | 16 | 6 | 62% |
| 5 | Li, S. and S. Wager (2022) Random graph asymptotics for treatment effect estimation under network interference | 0.843 | 3 | 3 | 100% |
| 6 | 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% |
| 7 | Kojevnikov, D., V. Marmer, and K. Song (2021) Limit theorems for network dependent random variables | 0.709 | 14 | 4 | 36% |
| 8 | Vazquez-Bare, G (2023) Causal spillover effects using instrumental variables | 0.659 | 7 | 3 | 29% |
| 9 | Dupas, P (2014) Short-run subsidies and long-run adoption of new health products: Evidence from a field experiment | 0.644 | 2 | 2 | 100% |
| 10 | Forastiere, L., E. M. Airoldi, and F. Mealli (2021) Identification and estimation of treatment and interference effects in observational studies on networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 30 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.