Michael P. Leung, Pantelis Loupos
arXiv 15 Nov 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2211.07823 · PDF · DOI · OpenAlex · Extracted main text
This paper studies causal inference with observational data from a single large network. We consider a nonparametric model with interference in potential outcomes and selection into treatment. Both stages may be the outcomes of simultaneous equation models, which allow for endogenous peer effects. This results in high-dimensional network confounding where the network and covariates of all units constitute sources of selection bias. In contrast, the existing literature assumes that confounding can be summarized by a known, low-dimensional function of these objects. We propose to use graph neural networks (GNNs) to adjust for network confounding. When interference decays with network distance, we argue that the model has low-dimensional structure that makes estimation feasible and justifies the use of shallow GNN architectures.
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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 (2022) Causal Inference Under Approximate Neighborhood Interference self | 1.000 | 16 | 5 | 100% |
| 2 | Kojevnikov, Marmer and Song (2021) Limit Theorems for Network Dependent Random Variables | 1.000 | 15 | 7 | 100% |
| 3 | He and Song (2024) Measuring Diffusion over a Large Network | 1.000 | 10 | 3 | 100% |
| 4 | Farrell, Liang and Misra (2021) Deep Neural Networks for Estimation and Inference | 1.000 | 5 | 5 | 100% |
| 5 | Kojevnikov (2021) The Bootstrap for Network Dependent Processes | 1.000 | 5 | 3 | 100% |
| 6 | Emmenegger, Spohn and Bühlmann (2025) Treatment Effect Estimation from Observational Network Data Using Augmented Inverse Probability Weighting and Machine Learning | 0.928 | 4 | 4 | 100% |
| 7 | Farrell (2018) Robust Inference on Average Treatment Effects with Possibly more Covariates than Observations | 0.928 | 4 | 3 | 100% |
| 8 | Morris, Lipman, Maron, Rieck, Kriege, Grohe, Fey and Borgwardt (2021) Weisfeiler and Leman Go Machine Learning: The Story So Far | 0.874 | 5 | 2 | 100% |
| 9 | Xu, Hu, Leskovec and Jegelka (2018) How Powerful are Graph Neural Networks? | 0.737 | 3 | 2 | 100% |
| 10 | Wang, Gu and Otsu (2024) Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity | 0.693 | 7 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.