Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

Estimating causal effects under networked interference is a crucial yet challenging problem. Existing methods based on observational data mainly rely on the networked unconfoundedness assumption, which guarantees the identification of networked effects. However, the networked unconfoundedness assumption is usually violated due to the latent confounders in observational data, hindering the identification of networked effects. Interestingly, in such networked settings, interactions between units provide valuable information for recovering latent confounders. In this paper, we identify three types of latent confounders in networked inference that hinder identification: those affecting only the individual, those affecting only neighbors, and those influencing both. Specifically, we devise a networked effect estimator based on identifiable representation learning techniques. Theoretically, we establish the identifiability of all latent confounders, and leveraging the identified latent confounders, we provide the networked effect identification result. Extensive experiments validate our theoretical results and demonstrate the effectiveness of the proposed method.