In the past two decades, there has been a fast-growing literature on fiducial inference since it was first proposed by R. A. Fisher in the 1930s. However, most of the fiducial inference based methods and related approaches have been developed for point-identified models, i.e., statistical models where the parameters of interest are uniquely determined by the observed data and the model's assumptions. In this paper, we propose a novel fiducial approach for partially identified statistical models. As a leading example, we consider the instrumental variable model with a variety of causal assumptions and estimands. The proposed methods are illustrated through extensive simulations and a data analysis evaluating the effect of consuming Vitamin A supplementation on reducing mortality rates.