arXiv 31 Jul 2023 · Econometrics
arXiv:2308.00202 · PDF · DOI · OpenAlex · Extracted main text
We develop randomization-based tests for heterogeneous treatment effects in the presence of network interference. Leveraging the exposure mapping framework, we study a broad class of null hypotheses that represent various forms of constant treatment effects in networked populations. These null hypotheses, unlike the classical Fisher sharp null, are not sharp due to unknown parameters and multiple potential outcomes. Existing conditional randomization procedures either fail to control size or suffer from low statistical power in this setting. We propose a testing procedure that constructs a data-dependent focal assignment set and permits variation in focal units across focal assignments. These features complicate both estimation and inference, necessitating new technical developments. We establish the asymptotic validity of the proposed procedure under general conditions on the test statistic and characterize the asymptotic size distortion in terms of observable quantities. The procedure is applied to experimental network data and evaluated via Monte Carlo simulations.
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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 | Leung, M. P (2020) Treatment and spillover effects under network interference | 0.928 | 4 | 3 | 100% |
| 2 | Ding, P., A. Feller, and L. Miratrix (2016) Randomization inference for treatment effect variation | 0.888 | 10 | 4 | 70% |
| 3 | Cai, J., A. De Janvry, and E. Sadoulet (2015) Social networks and the decision to insure | 0.874 | 5 | 2 | 100% |
| 4 | Zhang, Y. and Q. Zhao (2023) What is a randomization test? | 0.874 | 5 | 2 | 100% |
| 5 | Aronow, P. M., C. Samii, et al (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.855 | 8 | 5 | 62% |
| 6 | Hoshino, T. and T. Yanagi (2023) Randomization test for the specification of interference structure | 0.843 | 3 | 3 | 100% |
| 7 | Athey, S., D. Eckles, and G. W. Imbens (2018) Exact p-values for network interference | 0.822 | 9 | 5 | 56% |
| 8 | Morgan, W (1939) A test for the significance of the difference between the two variances in a sample from a normal bivariate population | 0.737 | 3 | 3 | 67% |
| 9 | Berger, R. L. and D. D. Boos (1994) P values maximized over a confidence set for the nuisance parameter | 0.644 | 2 | 2 | 100% |
| 10 | Cox, D. R (1958) Planning of experiments | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Unconditional Randomization Tests for Interference | 0.737 | 3 | 3 |
| 2 | A Nonparametric Test of Heterogeneous Treatment Effects under Interference | 0.405 | 1 | 1 |