This article presents a two-dimensional structural model of learning under ambiguity in the context of clinical trials. Clinical trials offer an ideal environment to study learning under ambiguity. The randomization process found in these studies leaves patients uncertain to their actual group assignment. Therefore, patients cannot immediately attribute changes in health to the experimental drug. The article proposes the use of “learning instrumental variables” to simultaneously update patients’ beliefs of the treatment effect and group assignment. Patient learning is found to be faster when observable side effects are incorporated to account for the uncertainty in group assignment.