  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", which is the payoff/cost
associated with selecting an individual with given characteristics. This paper
extends the benefit function to the general form such that the treatment and
effect are not restricted to binary. We propose an algorithm to test the
identifiability of the nonbinary benefit function and an algorithm to compute
the bounds of the nonbinary benefit function using experimental and
observational data.
