Unit Selection: Learning Benefit Function from Finite Population Data

TL;DR

This paper introduces a machine learning framework that estimates the bounds of a benefit function from finite population data to improve unit selection strategies for identifying individuals with desired behaviors.

Contribution

It proposes a novel approach to learn benefit function bounds from limited data, enhancing the efficiency of selecting optimal individuals.

Findings

Framework effectively estimates benefit function bounds from finite data

Enables identification of characteristics that maximize benefit

Improves unit selection accuracy in sparse data scenarios

Abstract

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.