Estimating Peer Direct and Indirect Effects in Observational Network Data
This work addresses the problem of improving causal inference for decision-makers in networked systems like social networks and epidemiology, though it is incremental as it builds on existing methods by adding specificity to peer effect types.
The paper tackles the challenge of estimating causal peer effects in observational network data by proposing a method that distinguishes direct and indirect peer effects and the individual's own treatment, using attention mechanisms and GNNs with HSIC regularization, achieving effectiveness confirmed through experiments on semi-synthetic datasets.
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions. Many algorithms have been proposed to estimate causal effects involving network data, particularly peer effects, but they often overlook the variety of peer effects. To address this issue, we propose a general setting which considers both peer direct effects and peer indirect effects, and the effect of an individual's own treatment, and provide identification conditions of these causal effects and proofs. To estimate these causal effects, we utilize attention mechanisms to distinguish the influences of different neighbors and explore high-order neighbor effects through multi-layer graph neural networks (GNNs). Additionally, to control the dependency between node features and representations, we incorporate the Hilbert-Schmidt Independence Criterion (HSIC) into the GNN, fully utilizing the structural information of the graph, to enhance the robustness and accuracy of the model. Extensive experiments on two semi-synthetic datasets confirm the effectiveness of our approach. Our theoretical findings have the potential to improve intervention strategies in networked systems, with applications in areas such as social networks and epidemiology.