The estimation the connectivity structure of neuronal networks is hindered by one's inability to simultaneously and individually measure the activity of all neurons. Many unmeasured neurons could be interacting with the small set of measured neurons and corrupting estimates of connectivity in unknown ways. For example, a common connection from an unmeasured neuron could introduce correlations among two measured neurons, which might lead one to erroneously infer a connection between the measured neurons. We present a model-based approach to control for such effects of unmeasured neurons. We demonstrate the promise of this approach via simulations of small networks of neurons driven by a visual stimulus.