Anish Agarwal, Sarah H. Cen, Devavrat Shah, Christina Lee Yu
arXiv 20 Oct 2022 · Econometrics
arXiv:2210.11355 · PDF · DOI · OpenAlex · Extracted main text
We propose a generalization of the synthetic controls and synthetic interventions methodology to incorporate network interference. We consider the estimation of unit-specific potential outcomes from panel data in the presence of spillover across units and unobserved confounding. Key to our approach is a novel latent factor model that takes into account network interference and generalizes the factor models typically used in panel data settings. We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network. We further establish that the estimator is asymptotically normal. We furnish two validity tests for whether the NSI estimator reliably generalizes to produce accurate counterfactual estimates. We provide a novel graph-based experiment design that guarantees the NSI estimator produces accurate counterfactual estimates, and also analyze the sample complexity of the proposed design. We conclude with simulations that corroborate our theoretical findings.
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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 | Abadie, A (2021) Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects | 1.000 | 5 | 3 | 100% |
| 2 | Manski, C. F (2013) Identification of Treatment Response with Social Interactions | 0.737 | 3 | 2 | 100% |
| 3 | Yu, C. L., Airoldi, E. M., Borgs, C., and Chayes, J. T (2022) Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure self | 0.737 | 3 | 2 | 100% |
| 4 | Agarwal, A., Shah, D., and Shen, D (2020) Synthetic Interventions self | 0.731 | 23 | 7 | 39% |
| 5 | Sussman, D. L. and Airoldi, E. M (2017) Elements of Estimation Theory for Causal Effects in the Presence of Network Interference | 0.585 | 3 | 1 | 100% |
| 6 | Chatterjee, S (2015) Matrix Estimation by Universal Singular Value Thresholding | 0.511 | 2 | 2 | 50% |
| 7 | Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B (2011) kneedle | 0.511 | 2 | 2 | 50% |
| 8 | Zack, G. W., Rogers, W. E., and Latt, S. A (1977) Automatic Measurement of Sister Chromatid Exchange Frequency | 0.511 | 2 | 2 | 50% |
| 9 | Aronow, P. M., Samii, C., et al (2017) Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment | 0.511 | 2 | 1 | 100% |
| 10 | Basse, G. W. and Airoldi, E. M (2018) Model-assisted Design of Experiments in the Presence of Network-correlated Outcomes | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 60 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Validating Causal Message Passing Against Network-Aware Methods on Real Experiments | 0.405 | 1 | 1 |