  Estimating the total treatment effect (TTE) of a new feature in social
platforms is crucial for understanding its impact on user behavior. However,
the presence of network interference, which arises from user interactions,
often complicates this estimation process. Experimenters typically face
challenges in fully capturing the intricate structure of this interference,
leading to less reliable estimates. To address this issue, we propose a novel
approach that leverages surrogate networks and the pseudo inverse estimator.
Our contributions can be summarized as follows: (1) We introduce the surrogate
network framework, which simulates the practical situation where experimenters
build an approximation of the true interference network using observable data.
(2) We investigate the performance of the pseudo inverse estimator within this
framework, revealing a bias-variance trade-off introduced by the surrogate
network. We demonstrate a tighter asymptotic variance bound compared to
previous studies and propose an enhanced variance estimator outperforming the
original estimator. (3) We apply the pseudo inverse estimator to a real
experiment involving over 50 million users, demonstrating its effectiveness in
detecting network interference when combined with the difference-in-means
estimator. Our research aims to bridge the gap between theoretical literature
and practical implementation, providing a solution for estimating TTE in the
presence of network interference and unknown interference structures.
