  Randomized experiments have become a standard tool in economics. In analyzing
randomized experiments, the traditional approach has been based on the Stable
Unit Treatment Value (SUTVA: \cite{rubin}) assumption which dictates that there
is no interference between individuals. However, the SUTVA assumption fails to
hold in many applications due to social interaction, general equilibrium,
and/or externality effects. While much progress has been made in relaxing the
SUTVA assumption, most of this literature has only considered a setting with
perfect compliance to treatment assignment. In practice, however, noncompliance
occurs frequently where the actual treatment receipt is different from the
assignment to the treatment. In this paper, we study causal effects in
randomized experiments with network interference and noncompliance. Spillovers
are allowed to occur at both treatment choice stage and outcome realization
stage. In particular, we explicitly model treatment choices of agents as a
binary game of incomplete information where resulting equilibrium treatment
choice probabilities affect outcomes of interest. Outcomes are further
characterized by a random coefficient model to allow for general unobserved
heterogeneity in the causal effects. After defining our causal parameters of
interest, we propose a simple control function estimator and derive its
asymptotic properties under large-network asymptotics. We apply our methods to
the randomized subsidy program of \cite{dupas} where we find evidence of
spillover effects on both short-run and long-run adoption of
insecticide-treated bed nets. Finally, we illustrate the usefulness of our
methods by analyzing the impact of counterfactual subsidy policies.
