Difference-in-Differences using Double Negative Controls and Graph Neural Networks for Unmeasured Network Confounding
结合双重负向控制与图神经网络的双重差分:处理未观测网络混杂
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.…