Causal inference in networks should account for interference, which occurs
when a unit's outcome is influenced by treatments or outcomes of peers. There
can be heterogeneous peer influence between units when a unit's outcome is
subjected to variable influence from different peers based on their attributes
and relationships, or when each unit has a different susceptibility to peer
influence. Existing solutions to causal inference under interference consider
either homogeneous influence from peers or specific heterogeneous influence
mechanisms (e.g., based on local neighborhood structure). This paper presents a
methodology for estimating individual causal effects in the presence of
heterogeneous peer influence due to arbitrary mechanisms. We propose a
structural causal model for networks that can capture arbitrary assumptions
about network structure, interference conditions, and causal dependence. We
identify potential heterogeneous contexts using the causal model and propose a
novel graph neural network-based estimator to estimate individual causal
effects. We show that existing state-of-the-art methods for individual causal
effect estimation produce biased results in the presence of heterogeneous peer
influence, and that our proposed estimator is robust.

提出一种通过结构因果模型来解决网络中存在异质性干预的方法，使用图神经网络估算个体因果效应来较好地解决同质性影响的偏见问题。