Causal models, value of intervention, and search for opportunities

While algorithms for influence diagrams allow for computing the optimal setting for decision variables, they offer no guidance in generation of decision variables, arguably a critical stage of decision making. A decision maker confronted with a complex system may not know which variables to best manipulate to achieve a desired objective. We introduce the concept of search for opportunities which amounts to identifying the set of decision variables and computing their optimal settings, given an objective expressed by a utility function. Search for opportunities is built on value of intervention in causal models.