Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
结合治疗信息的双曲表示学习:社交网络中的因果效应估计
Estimating the individual treatment effect (ITE) from observational data is a crucial research topic that holds significant value across multiple domains. How to identify hidden confounders poses a key challenge in ITE estimation. Recent studies have incorporated the structural information of social networks to tackle this challenge, achieving notable advancements. However, these methods utilize graph neural networks to learn the representation of hidden confounders in…