  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.
