Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender systems) is one of the most crucial causal inference problems for practitioners. Traditionally, randomized controlled trials (e.g., A/B tests) are performed to evaluate treatment assignment functions. However, they can be time-consuming, expensive, and even unethical in some cases. Therefore, counterfactual evaluation of treatment assignment functions becomes a pressing issue because a massive amount of observational data becomes available in the big data era. Counterfactual evaluation requires controlling the influence of hidden confounders – the unmeasured features that causally influence both treatment assignments and outcomes. However, most of the existing methods rely on the assumption of no hidden confounders. This assumption can be untenable in the context of massive observational data. When such data comes with network information, the later can be potentially useful to correct hidden confounding bias. As such, we first formulate a novel problem, counterfactual evaluation of treatment assignment functions with networked observational data. Then, we investigate the following research questions: How can we utilize network information in counterfactual evaluation? Can network information improve the estimates in counterfactual evaluation? Toward answering these questions, first, we propose a novel framework, Counterfactual Network Evaluator (CONE), which (1) learns partial representations of latent confounders under the supervision of observed treatments and outcomes; and (2) combines them for counterfactual evaluation. Then through extensive experiments, we corroborate the effectiveness of CONE. The results imply that incorporating network information mitigates hidden confounding bias in counterfactual evaluation.