Instrumental variables (IVs) are crucial for addressing unobservable confounders, yet their stringent exogeneity assumptions pose significant challenges in networked data. Existing methods typically rely on modelling neighbour information when recovering IVs, thereby inevitably mixing shared environment-induced endogenous correlations and individual-specific exogenous variation, leading the resulting IVs to inherit dependence on unobserved confounders and to violate exogeneity. To overcome this challenge, we propose $\underline{Dis}$entangled $\underline{I}$nstrumental $\underline{V}$ariables (DisIV) framework, a novel method for causal inference based on networked observational data with latent confounders. DisIV exploits network homogeneity as an inductive bias and employs a structural disentanglement mechanism to extract individual-specific components that serve as latent IVs. The causal validity of the extracted IVs is constrained through explicit orthogonality and exclusion conditions. Extensive semi-synthetic experiments on real-world datasets demonstrate that DisIV consistently outperforms state-of-the-art baselines in causal effect estimation under network-induced confounding.

翻译：工具变量（IVs）对于处理不可观测混杂因素至关重要，但其严格的外生性假设在网络化数据中构成显著挑战。现有方法在恢复工具变量时通常依赖于对邻居信息建模，从而不可避免地混合了共享环境诱导的内生相关性及个体特异性外生变异，导致所得工具变量继承了对未观测混杂因素的依赖性并违反外生性。为克服这一挑战，我们提出解耦工具变量（DisIV）框架——一种基于含潜在混杂因素网络化观测数据的新型因果推断方法。DisIV利用网络同质性作为归纳偏置，并采用结构化解耦机制来提取可作为潜在工具变量的个体特异性成分。所提取工具变量的因果有效性通过显式正交性与排他性条件进行约束。在真实世界数据集上的大量半合成实验表明，DisIV在网络诱导混杂下的因果效应估计中持续优于最先进的基线方法。