论文标题
单位选择：从有限人口数据中学习益处功能
Unit Selection: Learning Benefit Function from Finite Population Data
论文作者
论文摘要
单位选择问题是确定一群最有可能表现出所需的行为方式的个体，例如，选择如果激励人数，将以一种方式做出反应的人，如果没有激励，则会以不同的方式做出反应。单位选择问题包括评估和搜索子问题。 Li和Pearl定义了“福利函数”，以评估选择具有给定特征的某人的平均收益。然后，搜索子问题将设计一种算法，以确定最大化上述益处函数的特征。搜索子问题的硬度是由于每个人可用的大量特征以及每个特征中可用的数据的稀疏性。在本文中，我们提出了一个机器学习框架，该框架使用福利函数的边界，可以从有限的总体数据中估算出来，以了解每个特征单元格的福利函数的界限。因此，我们可以轻松地获得最大化福利函数的特征。
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.
