The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.
A fast and high quality multilevel scheme for partitioning irregular graphs.SIAM Journal on Scientific Computing, 20(1):359–392
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Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.