Abstract While much of the literature on cross-section dependence has focused on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spillovers and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful inferences and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Small sample performance is examined through a Monte Carlo study. Our methods are applied to a study of committee decision making within the Bank of England's Monetary Policy Committee.