  Causal inference in networks should account for interference, which occurs
when a unit's outcome is influenced by treatments or outcomes of peers.
Heterogeneous peer influence (HPI) occurs when a unit's outcome is influenced
differently by different peers based on their attributes and relationships, or
when each unit has a different susceptibility to peer influence. Existing
solutions to estimating direct causal effects 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 direct causal effects in the presence of
HPI where the mechanism of influence is not known a priori. We propose a
structural causal model for networks that can capture different possible
assumptions about network structure, interference conditions, and causal
dependence and enables reasoning about identifiability in the presence of HPI.
We find potential heterogeneous contexts using the causal model and propose a
novel graph neural network-based estimator to estimate individual direct causal
effects. We show that state-of-the-art methods for individual direct effect
estimation produce biased results in the presence of HPI, and that our proposed
estimator is robust.
