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.

Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semiparametric methods, e.g., leveraging neural networks to fit only one single nuisance function, may still encounter misspecification problems under networked interference without appropriate assumptions on the data generation process. To mitigate bias stemming from misspecification, we propose a novel doubly robust causal effect estimator under networked interference, by adapting the targeted learning technique to the training of neural networks. Specifically, we generalize the targeted learning technique into the networked interference setting and establish the condition under which an estimator achieves double robustness. Based on the condition, we devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss. Moreover, we provide a theoretical analysis of our designed estimator, revealing a faster convergence rate compared to a single nuisance model. Extensive experimental results on two real-world networks with semisynthetic data demonstrate the effectiveness of our proposed estimators.