解耦工具变量用于具有潜在混杂因素的网络观测数据因果推断
Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
AI总结：
DisIV通过解耦机制提取个体特定组件作为潜在工具变量，有效解决网络观测数据中潜在混杂因素的因果推断问题。
摘要：
工具变量（IVs）对于解决不可观测的混杂因素至关重要，但其严格的外生性假设在联网数据中带来了重大挑战。现有方法通常依赖于建模邻居信息来恢复IVs，从而不可避免地混合了共享环境引起的内生相关性和个体特定的外生变异，导致所得到的IVs继承对未观测混杂因素的依赖并违反外生性。为克服这一挑战，我们提出了Disentangled Instrumental Variables（DisIV）框架，一种基于具有潜在混杂因素的网络观测数据的因果推断新方法。DisIV利用网络同质性作为归纳偏差，并采用结构解耦机制来提取个体特定的组件，这些组件用作潜在IVs。提取的IVs的因果有效性通过显式的正交性和排除条件来约束。在现实世界数据集上的广泛半合成实验表明，DisIV在受网络引起的混杂影响下的因果效应估计中始终优于最先进的基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
