The objective of this paper is to introduce the concept of Bayesian causal mapping which is build from causal maps (CMs). CMs provide a rich representation of ideas, through the modeling of complex structures --representing the chain of arguments-- as networks. However, CMs is not easy to define and the magnitude of the effect is difficult to express in numbers. Hence, Bayesian causal maps can be used to make inferences in CMs
