
A novel GNN-driven instrumental variable approach, CgNN, that leverages network structure to mitigate hidden confounder bias and accurately estimate main, peer, and total causal effects in network data.


coremsg

causal-inference-in-networks-a-gnn-driven-instrumental-variable-approach-for-estimating-treatment-effects-in-the-presence-of-hidden-confounders


Causal Inference in Networks: A GNN-Driven Instrumental Variable Approach for Estimating Treatment Effects in the Presence of Hidden Confounders
