  Consider a policymaker who wants to decide which intervention to perform in
order to change a currently undesirable situation. The policymaker has at her
disposal a team of experts, each with their own understanding of the causal
dependencies between different factors contributing to the outcome. The
policymaker has varying degrees of confidence in the experts' opinions. She
wants to combine their opinions in order to decide on the most effective
intervention. We formally define the notion of an effective intervention, and
then consider how experts' causal judgments can be combined in order to
determine the most effective intervention. We define a notion of two causal
models being \emph{compatible}, and show how compatible causal models can be
merged. We then use it as the basis for combining experts' causal judgments. We
also provide a definition of decomposition for causal models to cater for cases
when models are incompatible. We illustrate our approach on a number of
real-life examples.
