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We consider a causal inference model in which individuals interact in asocial network and they may not comply with the assigned treatments. Inparticular, we suppose that the form of network interference is unknown toresearchers. To estimate meaningful causal parameters in this situation, weintroduce a new concept of exposure mapping, which summarizes potentiallycomplicated spillover effects into a fixed dimensional statistic ofinstrumental variables. We investigate identification conditions for theintention-to-treat effects and the average treatment effects for compliers,while explicitly considering the possibility of misspecification of exposuremapping. Based on our identification results, we develop nonparametricestimation procedures via inverse probability weighting. Their asymptoticproperties, including consistency and asymptotic normality, are investigatedusing an approximate neighborhood interference framework. For an empiricalillustration, we apply our method to experimental data on the anti-conflictintervention school program. The proposed methods are readily available withthe companion R package latenetwork.

Causal Inference with Noncompliance and Unknown Interference