Zihan Zhang, Lianyan Fu, Dehui Wang
arXiv 2 Jan 2026 · Econometrics
arXiv:2601.00603 · PDF · DOI · OpenAlex · Extracted main text
Estimating causal effects from observational network data faces dual challenges of network interference and unmeasured confounding. To address this, we propose a general Difference-in-Differences framework that integrates double negative controls (DNC) and graph neural networks (GNNs). Based on the modified parallel trends assumption and DNC, semiparametric identification of direct and indirect causal effects is established. We then propose doubly robust estimators. Specifically, an approach combining GNNs with the generalized method of moments is developed to estimate the functions of high-dimensional covariates and network structure. Furthermore, we derive the estimator's asymptotic normality under the $ψ$-network dependence and approximate neighborhood interference. Simulations show the finite-sample performance of our estimators. Finally, we apply our method to analyze the impact of China's green credit policy on corporate green innovation.
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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 \ Loupos (2022) `Graph neural networks for causal inference under network confounding', arXiv preprint arXiv:2211.07823 | 1.000 | 13 | 4 | 100% |
| 2 | Kojevnikov, Marmer \ Song (2021) `Limit theorems for network dependent random variables', Journal of Econometrics 222(2), 882–908 | 1.000 | 7 | 4 | 100% |
| 3 | Leung (2022) `Causal inference under approximate neighborhood interference', Econometrica 90(1), 267–293 | 0.928 | 4 | 3 | 100% |
| 4 | Miao, Geng \ Tchetgen Tchetgen (2018) `Identifying causal effects with proxy variables of an unmeasured confounder', Biometrika 105(4), 987–993 | 0.928 | 4 | 3 | 100% |
| 5 | Egami \ Tchetgen Tchetgen (2024) `Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding', Journa… | 0.874 | 6 | 2 | 100% |
| 6 | Cui, Pu, Shi, Miao \ Tchetgen Tchetgen (2024) `Semiparametric proximal causal inference', Journal of the American Statistical Association 119(546), 1348–1359 | 0.874 | 5 | 2 | 100% |
| 7 | Hoshino \ Yanagi (2024) `Causal inference with noncompliance and unknown interference', Journal of the American Statistical Association 119(548), 2869–2… | 0.843 | 3 | 3 | 100% |
| 8 | Hansen (1982) `Large sample properties of generalized method of moments estimators', Econometrica: Journal of the econometric society pp. 1029… | 0.644 | 2 | 2 | 100% |
| 9 | Sävje (2024) `Causal inference with misspecified exposure mappings: separating definitions and assumptions', Biometrika 111(1), 1–15 | 0.644 | 2 | 2 | 100% |
| 10 | Xu (2023) `Difference-in-differences with interference', arXiv preprint arXiv:2306.12003 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 45 scored citations.