There have been several recent advancements in the development of causal learning methods that combine structure learning and causal inference. Inspired by the frequentist IDA method, the Bayesian IDA (BIDA) method is a scalable Bayesian approach for estimating causal effects from observational data under an unknown causal Bayesian network. Here, we extend BIDA to the setting where the causal structure is partly known.