Bayesian networks (BNs) are graphical models that can combine knowledge with data to represent the causal probabilistic relationships between a set of variables and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. This paper describes a BN causal model for the early diagnosis and prediction of endometriosis. The causal structure of the BN model is developed using an idioms-based approach and the model parameters are derived from the data reported in multiple medical observational studies and trials. The BN incorporates the impact of errors and omissions in reporting endometriosis, and the distinction between assumed and actual cases. Hence, it is also able to explain both flawed and counterintuitive results of observational studies.