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Causal inference in networks should account for interference, which occurswhen a unit's outcome is influenced by treatments or outcomes of peers.Heterogeneous peer influence (HPI) occurs when a unit's outcome is influenceddifferently by different peers based on their attributes and relationships, orwhen each unit has a different susceptibility to peer influence. Existingsolutions to estimating direct causal effects under interference considereither homogeneous influence from peers or specific heterogeneous influencemechanisms (e.g., based on local neighborhood structure). This paper presents amethodology for estimating individual direct causal effects in the presence ofHPI where the mechanism of influence is not known a priori. We propose astructural causal model for networks that can capture different possibleassumptions about network structure, interference conditions, and causaldependence and enables reasoning about identifiability in the presence of HPI.We find potential heterogeneous contexts using the causal model and propose anovel graph neural network-based estimator to estimate individual direct causaleffects. We show that state-of-the-art methods for individual direct effectestimation produce biased results in the presence of HPI, and that our proposedestimator is robust.

Inferring Causal Effects Under Heterogeneous Peer Influence