Unit Selection: Learning Benefit Function from Finite Population Data
This addresses the search subproblem in unit selection for selecting individuals with desired behaviors, but it appears incremental as it builds on existing definitions and focuses on estimable bounds.
The paper tackles the unit selection problem by developing a machine learning framework that learns bounds for the benefit function from finite population data, enabling easy identification of characteristics that maximize the benefit function.
The unit selection problem is to identify a group of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if incentivized and a different way if not. The unit selection problem consists of evaluation and search subproblems. Li and Pearl defined the "benefit function" to evaluate the average payoff of selecting a certain individual with given characteristics. The search subproblem is then to design an algorithm to identify the characteristics that maximize the above benefit function. The hardness of the search subproblem arises due to the large number of characteristics available for each individual and the sparsity of the data available in each cell of characteristics. In this paper, we present a machine learning framework that uses the bounds of the benefit function that are estimable from the finite population data to learn the bounds of the benefit function for each cell of characteristics. Therefore, we could easily obtain the characteristics that maximize the benefit function.