arXiv subjects

Lianyan Fu

Publications and source records attributed to Lianyan Fu.

2 recordsLinked to original sources

arXiv2026

Graph Neural Networks for Generalized Mundlak Estimator under Network Confounding

This paper proposes a generalized Mundlak estimator based on graph neural networks (GME-GNN). The estimator is designed to mitigate bias arising from group-level heterogeneity and to accommodate within-group dependence among individuals. Traditional fixed-effects models handle group heterogeneity via group-specific intercepts, but require overly strict linear additivity and intra-group independence assumptions, and are confined to within-group comparisons. Rather than relying on intercepts, GME-GNN uses aggregated group-level balancing statistics to fully control between-group confounding, enabling valid cross-group comparisons and relaxing linearity constraints. It further employs graph neural network message-passing to adaptively learn nonlinear representations and capture intra-group interaction effects. Theoretical analysis shows that the estimator satisfies double robustness and is asymptotically normal. Simulation and empirical studies confirm its performance.

econ.EM↗

arXiv2026

Difference-in-Differences using Double Negative Controls and Graph Neural Networks for Unmeasured Network Confounding

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 $\psi$-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.

econ.EM↗