Treatment-Aware Hyperbolic Representation Learning for Causal Effect
  Estimation with Social Networks
Ziqiang Cui Xing Tang Yang Qiao Bowei He Liang Chen Xiuqiang He Chen Ma
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 Euclidean space, disregarding two critical issues: (1) the
social networks often exhibit a scalefree structure, while Euclidean embeddings
suffer from high distortion when used to embed such graphs, and (2) each
ego-centric network within a social network manifests a treatment-related
characteristic, implying significant patterns of hidden confounders. To address
these issues, we propose a novel method called Treatment-Aware Hyperbolic
Representation Learning (TAHyper). Firstly, TAHyper employs the hyperbolic
space to encode the social networks, thereby effectively reducing the
distortion of confounder representation caused by Euclidean embeddings.
Secondly, we design a treatment-aware relationship identification module that
enhances the representation of hidden confounders by identifying whether an
individual and her neighbors receive the same treatment. Extensive experiments
on two benchmark datasets are conducted to demonstrate the superiority of our
method.