Tadao Hoshino, Takahide Yanagi
arXiv 13 Jan 2023 · Statistics — Methodology
arXiv:2301.05580 · PDF · Extracted main text
This study considers testing the specification of spillover effects in causal inference. We focus on experimental settings in which the treatment assignment mechanism is known to researchers. We develop a new randomization test utilizing a hierarchical relationship between different exposures. Compared with existing approaches, our approach is essentially applicable to any null exposure specifications and produces powerful test statistics without a priori knowledge of the true interference structure. As empirical illustrations, we revisit two existing social network experiments: one on farmers' insurance adoption and the other on anti-conflict education programs.
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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 | Cai, J., De Janvry, A., and Sadoulet, E (2015) Social networks and the decision to insure | 1.000 | 5 | 4 | 100% |
| 2 | Paluck, E.L., Shepherd, H., and Aronow, P.M (2016) Changing climates of conflict: A social network experiment in 56 schools | 1.000 | 5 | 3 | 100% |
| 3 | Puelz, D., Basse, G., Feller, A., and Toulis, P (2022) A graph-theoretic approach to randomization tests of causal effects under general interference | 0.928 | 4 | 3 | 100% |
| 4 | Athey, S., Eckles, D., and Imbens, G.W (2018) Exact p-values for network interference | 0.811 | 4 | 2 | 100% |
| 5 | Basse, G.W., Feller, A., and Toulis, P (2019) Randomization tests of causal effects under interference | 0.644 | 2 | 2 | 100% |
| 6 | Hoshino, T. and Yanagi, T (2023) Causal inference with noncompliance and unknown interference self | 0.644 | 2 | 2 | 100% |
| 7 | Imbens, G.W. and Rubin, D.B (2015) Causal Inference in Statistics, Social, and Biomedical Sciences | 0.644 | 2 | 2 | 100% |
| 8 | Aronow, P.M (2012) A general method for detecting interference between units in randomized experiments | 0.405 | 1 | 1 | 100% |
| 9 | Aronow, P.M. and Samii, C (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.405 | 1 | 1 | 100% |
| 10 | Aronow, P.M., Eckles, D., Samii, C., and Zonszein, S (2021) Spillover effects in experimental data | 0.405 | 1 | 1 | 100% |
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