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.

翻译：网络中的因果推断需考虑干扰效应，即个体的结果会受到同伴的处理或结果的影响。当个体的结果因不同同伴的属性与关系而受到差异化影响，或每个体对同伴影响具有不同敏感性时，即产生异构同伴影响（HPI）。现有针对干扰下的直接因果效应估计方法，要么假设同伴影响具有同质性，要么仅针对特定异构影响机制（例如基于局部邻域结构）。本文提出一种在HPI场景下估计个体直接因果效应的方法，且无需预先知晓影响机制。我们构建了一个适用于网络的结构因果模型，它能够捕捉关于网络结构、干扰条件及因果依赖性的不同可能假设，并支持在HPI情境下推导可识别性。利用该因果模型，我们识别出潜在的异构情境，并提出一种基于图神经网络的新型估计器，用于估计个体直接因果效应。研究表明，现有最先进的个体直接效应估计方法在HPI存在时会产生有偏结果，而我们所提出的估计器具有稳健性。