Disentangled Instrumental Variables for Causal Inference with Networked Observational Data

TL;DR

This paper introduces DisIV, a novel framework for causal inference in networked observational data that disentangles individual-specific instrumental variables to address confounding, outperforming existing methods.

Contribution

DisIV employs network homogeneity and structural disentanglement to extract valid individual-specific IVs, overcoming limitations of previous approaches relying on shared environment-induced correlations.

Findings

DisIV outperforms state-of-the-art baselines in causal effect estimation.

DisIV effectively disentangles individual-specific IVs in networked data.

The method is validated on real-world datasets with semi-synthetic experiments.

Abstract

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.