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Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity

Yike Wang, Chris Gu, Taisuke Otsu

arXiv 29 Jan 2024 · Econometrics

arXiv:2401.16275 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper presents a novel application of graph neural networks for modeling and estimating network heterogeneity. Network heterogeneity is characterized by variations in unit's decisions or outcomes that depend not only on its own attributes but also on the conditions of its surrounding neighborhood. We delineate the convergence rate of the graph neural networks estimator, as well as its applicability in semiparametric causal inference with heterogeneous treatment effects. The finite-sample performance of our estimator is evaluated through Monte Carlo simulations. In an empirical setting related to microfinance program participation, we apply the new estimator to examine the average treatment effects and outcomes of counterfactual policies, and to propose an enhanced strategy for selecting the initial recipients of program information in social networks.

Citation extraction

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2013) The diffusion of microfinance1.00093100%
2Farrell, M. H., T. Liang, and S. Misra (2021) Deep neural networks for estimation and inference0.92314779%
3Bartlett, P. L., O. Bousquet, and S. Mendelson (2005) Local rademacher complexities0.7218438%
4Janson, S (1988) Normal convergence by higher semiinvariants with applications to sums of dependent random variables and random graphs0.5112250%
5Rogers, E. M (2003) Diffusion of Innovations0.40511100%
6Van der Vaart, A. W (1998) Asymptotic Statistics0.40511100%
7Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2019) Using gossips to spread information: Theory and evidence from two randomized controlled trials0.40511100%
8Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls0.40511100%
9Berry, S., J. Levinsohn, and A. Pakes (1995) Automobile prices in market equilibrium0.40511100%
10Bonhomme, S. and E. Manresa (2015) Grouped patterns of heterogeneity in panel data0.40511100%

Showing the top 10 of 27 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Graph Neural Networks for Causal Inference Under Network Confounding0.69371
2Neighborhood Stability in Double/Debiased Machine Learning with Dependent Data0.51121
3Robust Network Targeting with Multiple Nash Equilibria0.40511
4Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder0.40511