Unit Selection with Nonbinary Treatment and Effect
This work addresses a methodological gap in causal inference for researchers, but it is incremental as it builds directly on prior binary-focused results.
The paper tackles the unit selection problem by extending the benefit function to nonbinary treatments and effects, proposing algorithms to test identifiability and compute bounds using experimental and observational data.
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.