Tadao Hoshino
arXiv 16 Oct 2025 · Statistics — Methodology
arXiv:2510.14415 · PDF · Extracted main text
This paper develops a sensitivity analysis framework that transfers the average total treatment effect (ATTE) from source data with a fully observed network to target data whose network is completely unknown. The ATTE represents the average social impact of a policy that assigns the treatment to every individual in the dataset. We postulate a covariate-shift type assumption that both source and target datasets share the same conditional mean outcome. However, because the target network is unobserved, this assumption alone is not sufficient to pin down the ATTE for the target data. To address this issue, we consider a sensitivity analysis based on the uncertainty of the target network's degree distribution, where the extent of uncertainty is measured by the Wasserstein distance from a given reference degree distribution. We then construct bounds on the target ATTE using a linear programming-based estimator. The limiting distribution of the bound estimator is derived via the functional delta method, and we develop a wild bootstrap approach to approximate the distribution. As an empirical illustration, we revisit the social network experiment on farmers' weather insurance adoption in China by Cai et al. (2015).
appendix boundary found by appendix_command · 64% 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 | Cai, J., Janvry, A.D., and Sadoulet, E (2015) Social networks and the decision to insure | 1.000 | 10 | 5 | 100% |
| 2 | Conley, T.G., Goncalves, S., Kim, M.S., and Perron, B (2023) Bootstrap inference under cross-sectional dependence | 0.769 | 11 | 4 | 45% |
| 3 | Fang, Z. and Santos, A (2019) Inference on directionally differentiable functions | 0.737 | 3 | 3 | 67% |
| 4 | Faridani, S. and Niehaus, P (2024) Linear estimation of global average treatment effects | 0.737 | 3 | 2 | 100% |
| 5 | Gao, R. and Kleywegt, A (2023) Distributionally robust stochastic optimization with Wasserstein distance | 0.737 | 3 | 2 | 100% |
| 6 | Paluck, E.L., Shepherd, H., and Aronow, P.M (2016) Changing climates of conflict: A social network experiment in 56 schools | 0.737 | 3 | 2 | 100% |
| 7 | Kelejian, H.H. and Prucha, I.R (2007) HAC estimation in a spatial framework | 0.644 | 3 | 2 | 67% |
| 8 | Blanchet, J. and Murthy, K (2019) Quantifying distributional model risk via optimal transport | 0.644 | 2 | 2 | 100% |
| 9 | Chin, A (2019) Regression adjustments for estimating the global treatment effect in experiments with interference | 0.644 | 2 | 2 | 100% |
| 10 | Christensen, T. and Connault, B (2023) Counterfactual sensitivity and robustness | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 42 scored citations.