  The unit selection problem aims to identify a set of individuals who are most
likely to exhibit a desired mode of behavior, for example, selecting
individuals who would respond one way if encouraged and a different way if not
encouraged. Using a combination of experimental and observational data, Li and
Pearl derived tight bounds on the "benefit function" - the payoff/cost
associated with selecting an individual with given characteristics. This paper
shows that these bounds can be narrowed significantly (enough to change
decisions) when structural information is available in the form of a causal
model. We address the problem of estimating the benefit function using
observational and experimental data when specific graphical criteria are
assumed to hold.
