arXiv 14 Sep 2023 · Econometrics · publishedJournal of Econometrics (2025) · 2 citations (OpenAlex)
arXiv:2309.07476 · PDF · DOI · OpenAlex · Extracted main text
Network experiments are powerful tools for studying spillover effects, which avoid endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. We show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the interference patterns under network experiments. We first recall a well-known result that the H\'ajek estimator is numerically identical to the coefficient from the weighted-least-squares fit based on the inverse probability of the exposure mapping. Moreover, we demonstrate that the regression-based approach offers three notable advantages: its ease of implementation, the ability to derive standard errors through the same regression fit, and the potential to integrate covariates into the analysis to improve efficiency. Recognizing that the regression-based network-robust covariance estimator can be anti-conservative under nonconstant effects, we propose an adjusted covariance estimator to improve the empirical coverage rates.
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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 | Aronow, P. M. and C. Samii (2017) Estimating Average Causal Effects under General Interference, with Application to a Social Network Experiment | 1.000 | 14 | 4 | 100% |
| 2 | Paluck, E. L., H. Shepherd, and P. M. Aronow (2016) Changing Climates of Conflict: A Social Network Experiment in 56 Schools | 1.000 | 8 | 3 | 100% |
| 3 | Lin, W (2013) Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman's Critique | 0.894 | 7 | 3 | 71% |
| 4 | Kojevnikov, D (2021) The Bootstrap for Network Dependent Processes, ArXiv preprint arXiv:http://arxiv.org/abs/2101.123122101.12312 | 0.883 | 16 | 4 | 69% |
| 5 | Fisher, R. A (1935) The Design of Experiments | 0.874 | 5 | 2 | 100% |
| 6 | Leung, M. P (2022) a): Causal Inference Under Approximate Neighborhood Interference | 0.816 | 68 | 7 | 54% |
| 7 | Leung, M. P (2019) Causal Inference Under Approximate Neighborhood Interference, ArXiv preprint arXiv:1911.07106v1 | 0.769 | 11 | 3 | 45% |
| 8 | Su, F. and P. Ding (2021) Model-Assisted Analyses of Cluster-Randomized Experiments | 0.737 | 3 | 3 | 67% |
| 9 | Zhao, A. and P. Ding (2022) Reconciling Design-Based and Model-Based Causal Inferences for Split-Plot Experiments | 0.737 | 3 | 3 | 67% |
| 10 | Zhao, A., P. Ding, and F. Li (2024) Covariate Adjustment in Randomized Experiments with Missing Outcomes and Covariates | 0.737 | 3 | 3 | 67% |
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