A large body of research in network and social sciences studies the effects of interventions in network systems. Nearly all of this work assumes that network participants will respond to interventions in similar ways. However, in real-world systems, a subset of participants may respond in ways purposefully different than their true outcome. We characterize the influence of non-cooperative nodes and the bias these nodes introduce in estimates of average treatment effect (ATE). In addition to theoretical bounds, we empirically demonstrate estimation bias through experiments on synthetically generated graphs and a real-world network. We demonstrate that causal estimates in networks can be sensitive to the actions of non-cooperative members, and we identify network structures that are particularly vulnerable to non-cooperative responses.