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.

本论文提出了一种机器学习框架，利用可估算的有限人口数据上的福利函数范围来学习特征单元格的福利函数范围，以便轻松获得最大化福利函数的特征。